[{"content":" Latest Publications and News # Here you will find the latest peer-reviewed articles, news, and publications covering SENS motion® technology and its applications in healthcare and research.\nLatest Publications Validation of an Accelerometer System for Measuring Physical Activity and Sedentary Behaviour in Healthy Children and Adolescents \u0026#8599; \u0026#8598; 15 August 2023 This study validates the use of SENS motion® accelerometers in assessing physical activity and sedentary behaviour among healthy children and adolescents. Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis \u0026#8599; \u0026#8598; 20 July 2022 This research assesses the accuracy of SENS motion® accelerometers in measuring linear accelerations during walking in elderly patients with knee osteoarthritis. Validation of Two Activity Monitors in Slow and Fast Walking Hospitalized Patients \u0026#8599; \u0026#8598; 10 May 2022 This study compares the performance of SENS motion® activity monitors in detecting walking speeds among hospitalized patients. ","date":"15 August 2023","externalUrl":null,"permalink":"/en/news/","section":"News and Publications","summary":"","title":"News and Publications","type":"news"},{"content":" Discrete Wearable Activity Sensor for Healthcare and Research Better Treatment. Better Research. What is SENS motion® SENS motion® is an integrated system for collecting physical activity data from groups of people. It consists of a wireless activity sensor that automatically transfers data to a secure cloud. It is especially well suited for use in the healthcare sector and for large research projects. System Measures: \u0026#x2714;\u0026#xFE0F;Rest time\u0026#x2714;\u0026#xFE0F;Standing time\u0026#x2714;\u0026#xFE0F;Walking time\u0026#x2714;\u0026#xFE0F;Running \u0026amp; High Intensity Movement time\u0026#x2714;\u0026#xFE0F;Cycling time\u0026#x2714;\u0026#xFE0F;Steps\u0026#x2714;\u0026#xFE0F;Intensity\u0026#x2714;\u0026#xFE0F;Sleep time and quality SENS motion® – For Healthcare SENS motion® – For Healthcare enables the use of SENS motion® activity sensor in healthcare environments, such as hospitals or care centres. The collected activity data can be presented to the patient itself for motivational purpose, assist healthcare professionals in treatment and help prioritize their valuable time efficiently.\nRead More →\nSENS motion® – For Healthcare consists of: \u0026#x2714;\u0026#xFE0F;The SENS motion activity sensor for use on patients\u0026#x2714;\u0026#xFE0F;A patient app, with gamification feedback for motivating the patient to become more active\u0026#x2714;\u0026#xFE0F;(Optional) Monitor display providing overview of patients\u0026#x2714;\u0026#xFE0F;Access to reports of individual patients activity SENS motion® – For Research SENS motion® – For Research enables the use of SENS motion activity sensor for collecting activity data or raw accelerometer data in research projects.\nRead More →\nSENS motion® – For Research consists of: \u0026#x2714;\u0026#xFE0F;The SENS motion® activity sensor\u0026#x2714;\u0026#xFE0F;Smartphone app for automatically transferring data to the secure cloud\u0026#x2714;\u0026#xFE0F;Web-application for managing sensors, viewing or exporting data SENS motion® – Connect OEM Platform for medical sensors\nDo you want to build a wireless, scalable, battery-powered medical sensor or have a business opportunity where measurement of physical data is needed?\nContact us for options on OEM Cooperation ","date":"15 August 2023","externalUrl":null,"permalink":"/en/","section":"SENS Innovation ApS – Discrete Wearable Activity Sensor","summary":"","title":"SENS Innovation ApS – Discrete Wearable Activity Sensor","type":"page"},{"content":" About the Study # This peer-reviewed article validates the use of SENS motion® accelerometers for measuring physical activity and sedentary behaviour in healthy children and adolescents. The study demonstrates the system\u0026rsquo;s accuracy and reliability in research settings.\nAuthors # Milther, C.; Winther, L.; Stahlhut, M.; Curtis, D. J.; Aadahl, M.; Kristensen, M. T.; Sørensen, J. L.; Dall, C. H.\nPublication # European Journal of Pediatrics (2023) 182:3639–3647\nKey Findings # Validation of SENS motion® accelerometers in child and adolescent research Accurate measurement of physical activity and sedentary behaviour Applicable in large-scale research projects with children and adolescents Read the full article →\n","date":"15 August 2023","externalUrl":"https://doi.org/10.1007/s00431-023-05014-z","permalink":"/en/news/2023-validation-accelerometer-children/","section":"News and Publications","summary":"This study validates the use of SENS motion® accelerometers in assessing physical activity and sedentary behaviour among healthy children and adolescents.","title":"Validation of an Accelerometer System for Measuring Physical Activity and Sedentary Behaviour in Healthy Children and Adolescents","type":"news"},{"content":" About the Study # This peer-reviewed article investigates the criterion validity of linear accelerations measured with low-sampling-frequency accelerometers (SENS motion®) during overground walking in elderly patients with knee osteoarthritis. The study demonstrates the system\u0026rsquo;s accuracy in clinical settings.\nAuthors # Ghaffari, A.; Rahbek, O.; Lauritsen, R.E.K.; Kappel, A.; Kold, S.; Rasmussen, J.\nPublication # Sensors 2022, 22, 5289\nKey Findings # Validation of SENS motion® in measuring gait patterns in elderly patients Accurate measurement of linear accelerations during overground walking Applicable in clinical practice for assessing mobility and gait function Read the full article →\n","date":"20 July 2022","externalUrl":"https://doi.org/10.3390/s22145289","permalink":"/en/news/2022-criterion-validity-elderly/","section":"News and Publications","summary":"This research assesses the accuracy of SENS motion® accelerometers in measuring linear accelerations during walking in elderly patients with knee osteoarthritis.","title":"Criterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis","type":"news"},{"content":" About the Study # This peer-reviewed article compares the performance of two activity monitors, including SENS motion®, in detecting walking speeds among hospitalized patients. The study evaluates the system\u0026rsquo;s accuracy in both slow and fast walking conditions.\nAuthors # Britt Stævnsbo Pedersen, Morten Tange Kristensen, Christian Ohrhammer Josefsen, Kasper Lundberg Lykkegaard, Line Rokkedal Jønsson, Mette Merete Pedersen\nPublication # Rehabilitation Research and Practice, vol. 2022, Article ID 9230081\nKey Findings # Comparison of activity monitors in hospital settings Accurate detection of walking speeds in hospitalized patients Applicable for monitoring patient activity levels during hospitalization Read the full article →\n","date":"10 May 2022","externalUrl":"https://doi.org/10.1155/2022/9230081","permalink":"/en/news/2022-validation-activity-monitors/","section":"News and Publications","summary":"This study compares the performance of SENS motion® activity monitors in detecting walking speeds among hospitalized patients.","title":"Validation of Two Activity Monitors in Slow and Fast Walking Hospitalized Patients","type":"news"},{"content":" Company Profile SENS Innovation ApS Background SENS Innovation ApS was founded in 2014 by Kasper L. Lykkegaard and Morten Kjærgaard. The company develops medical sensor products, based on advanced sensor technology, for monitoring of physical activity with patients.\nThe SENS products and platforms are easy to use, flexible, scalable and present accurate monitoring of the body\u0026rsquo;s position and the nature of the activity, for both the individual or even larger groups. Therefore, SENS\u0026rsquo;s products and technology are a valuable tool in research trials and studies in correlation to physical activity and diseases.\nOur vision is to revolutionize the way physical activity can be used as an integrated and measurable part of the treatment and prevention of numerous diseases – on an equal footing with other medicine. Kasper L. Lykkegaard Co-founder and CEO Follow us on LinkedIn Meet Our Team Kasper L.\nLykkegaard CEO \u0026amp; Founder kasper@sens.dk Morten\nKjærgaard CTO \u0026amp; Founder morten@sens.dk Ida Marie\nPierrel-Boas Senior Project Manager ida@sens.dk Christian\nLindharth Senior Account Executive christian@sens.dk Miriam\nCabrita Partnerships Manager miriam@sens.dk Karen\nShakespeare UK \u0026 Ireland Account Executive karen@sens.dk Amin\nRezaei Software Developer August Blicher\nFriis Software Developer Renata\nOliveira UX Specialist Huong\nNguyen Frontend Developer Mathias Elmegaard\nNielsen Student Assistant Mats\nEllingsen Student Assistant Lea Møller\nStorm Student Assistant ","externalUrl":null,"permalink":"/en/about/","section":"About","summary":"","title":"About","type":"about"},{"content":" We are currently updating our support articles and some info may be outdated. Read release notes here. Background # This guide contains a guide on how to add patients in the SENS motion web app.\nThe term \u0026ldquo;Participant\u0026rdquo; refers to a study participant who wears the sensor. By creating participants, data can be associated with an individual participant, allowing the same sensor to be used for multiple participants.\nBefore creating a new participant, make sure to turn on the sensor. See here for instructions on how to turn on a sensor.\nHow to add a new participant # Log on to the web app.\nEnter the \u0026ldquo;Workspace\u0026rdquo; tab via the top menu:\nClick \u0026ldquo;Add participant\u0026rdquo; to add a new participant: Enter the participant information: Please enter a participant ID or first name. The participant ID does not have to contain personally identifiable information. Entering the last name and phone number is optional.\nUnder \u0026lsquo;Measurement method\u0026rsquo; a default algorithm is pre-selected (choose activity 5.4 unless otherwise agreed).\nUnder \u0026ldquo;Sensor on Thigh\u0026rdquo;, select sensor. The sensor ID is on the sensor label. Select only sensors that are ready to use and are not already associated with another participant. Sensors \u0026ldquo;ready for use\u0026rdquo; are marked with a green dot in front of the sensor ID followed by a grey person:\nOther colours indicate:\nBlack dot, grey person: The sensor is turned off. See how to turn on the sensor here.\nGreen dot, orange person: The sensor is already in use. Select another sensor or end the sensor\u0026rsquo;s current monitor period (under edit participant).\nSelect the start and end time of the period to be measured:\nFor start/end time, there are three options; \u0026ldquo;Now\u0026rdquo;, \u0026ldquo;Specific time\u0026rdquo; and \u0026ldquo;Not defined yet\u0026rdquo;. For the end time you can select \u0026ldquo;Expected period\u0026rdquo; in addition to the former three. Note, end time cannot be added if \u0026ldquo;Not defined yet\u0026rdquo; is selected under start time.\nYou can always subsequently adjust the start and end time of the monitor period. Data will be stored on the sensor as long as it is running and adjusting the monitor period will not impact the stored data.\nClick \u0026ldquo;Add participant\u0026rdquo;. You have now added a participant. Follow this guide on how to assemble and position the patch on the participant. ","externalUrl":null,"permalink":"/en/support/web-application/add-a-participant/","section":"Support","summary":"","title":"Add a participant","type":"support"},{"content":"","externalUrl":null,"permalink":"/en/categories/app-smartphone/","section":"Categories","summary":"","title":"App-Smartphone","type":"categories"},{"content":"","externalUrl":null,"permalink":"/en/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":" We are currently updating our support articles and some info may be outdated. Read release notes here. Background # The sensor\u0026rsquo;s battery level is updated when the sensor has made contact with the SENS motion sync app. For more information on data transfer see the article Transferring data from sensor.\nThe battery in the sensor is not rechargeable and therefore the sensor must be returned to SENS Innovation when the sensor battery has run out of power. Within the period of use, the battery can last for 120-130 days when activated and 2 years in off mode.\nThe sensor overview displays comprehensive battery information to help you monitor sensor health.\nEach sensor shows:\nBattery voltage – The current voltage reading from the sensor (when hovering over the icon) Estimated days left – A calculation of how many days of battery life remain based on the sensor\u0026rsquo;s runtime and usage patterns What you need to know: The estimated days left is calculated based on how long the sensor has been active and its current battery voltage. This helps you plan ahead for battery replacements and ensures you don\u0026rsquo;t lose data due to dead batteries.\nHow to use it: Look for the battery icon and information under each sensor in the Sensors section. The estimated days left will help you identify sensors that need attention soon. To see battery voltage, hover the mouse cursor over the battery icon.\nBattery lifetime estimation # The system calculates and displays estimated battery lifetime for each sensor.\nWhat you need to know: The estimated battery lifetime shows how many days are left before the sensor\u0026rsquo;s battery is expected to run out. This calculation is based on:\nHow long the sensor has been active The current battery voltage Expected battery consumption patterns Sensor batteries are typically supported for 120 days. After this period, sensors may stop working at any moment.\nHow to use it: Monitor the \u0026ldquo;Est. X days left\u0026rdquo; indicator in the sensor overview. When this number gets low (especially below 10 days), do not use the sensor for any measurements meant to run for longer.\nNOTE: It is always recommended that the sensor\u0026rsquo;s battery level is updated before the sensor is attached to a participant. This ensures that there is enough battery for the sensor to measure the desired period. If the sensor has not been near the SENS motion app in the last 10 days, the battery status will change to a grey question mark. As the sensor will always use some power, it is therefore not possible to say exactly how much battery is left on the sensor. It is therefore recommended that the SENS motion app is opened near the sensor to measure the battery level and see if the sensor is still functional.\n","externalUrl":null,"permalink":"/en/support/web-application/battery-life/","section":"Support","summary":"","title":"Battery life","type":"support"},{"content":"","externalUrl":null,"permalink":"/en/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":" Contact Us Feel free to contact SENS by filling out the form below. Send us a message # Name Work e-mail Telephone (optional) Organisation name Don\u0026#39;t fill this out if you\u0026#39;re human: Message Send message ","externalUrl":null,"permalink":"/en/contact/","section":"Contact Us","summary":"","title":"Contact Us","type":"contact"},{"content":" We are currently updating our support articles and some info may be outdated. Read release notes here. General # Exporting data is done via our web application: https://app.sens.dk/\nWe recommend using an internet browser on a computer. This article explains how to export one or more files:\nExport summarised data, PDF files and raw data by participant profiles Download data to your device Furthermore the article contains information about the difference of the available file formats:\nCSV format - summarised data PDF reports - visual reports Raw accelerometer data in BIN/HEX or CSV format Export summarised data, PDF files and raw data by participant profiles # It is possible to export data for several participants by only a few clicks. This is possible for summarized data and the visual PDF reports, as well as for raw accelerometer data.\nExport your files by entering the \u0026ldquo;Workspace\u0026rdquo; menu from the top menu bar. Click the tab \u0026ldquo;Completed\u0026rdquo;. Click \u0026ldquo;Bulk export\u0026rdquo; and select the participants you wish to export data for. Select all participants by selecting the top check box, or click the participants you want to export. Click the green \u0026ldquo;Export selected\u0026rdquo; button.\nSelect the preferred file format (Activity data - CSV, PDF, or raw accelerometer data - Binary). Read more about the available file formats below.\nYou can now download your files from the \u0026ldquo;Files\u0026rdquo; tab.\nActivity data - summarised CSV format # You have the following options for exporting CSV files:\n\u0026ldquo;1 hour\u0026rdquo;, \u0026ldquo;15 min\u0026rdquo; and \u0026ldquo;Detailed\u0026rdquo; contain data for all activitiy categories. Select the degree of detail that best suits your data processing needs.\nNote: Beware that data can only be exported if you have entered a specific start- and end date for the participant. See how to define the export period here. Activity data - visual PDF reports # PDF reports give a quick and easy overview of the monitoring period. Note that a maximum of 14 days can be included in the report.\nYou have the following options for PDF reports:\n\u0026ldquo;Week overview\u0026rdquo; includes a detailed view of each day in the monitor period like what you see in the web app.\nThe \u0026ldquo;Average\u0026rdquo; includes a summary of the monitor period with average activity per day, a pie chart and a bar chart.\n\u0026ldquo;Combined\u0026rdquo; is a combination of the \u0026ldquo;Week overview\u0026rdquo; and \u0026ldquo;Average\u0026rdquo;.\nNote: It is possible to create a PDF report for a maximum of 14 days due to the large amount of data. Raw accelerometer data in BIN/HEX or CSV format # \u0026ldquo;Accelerometer Data\u0026rdquo; contains a BIN-file with raw accelerometer data. When you export from the Workspace tab, you will export the data by participant profile.\nBinary format is compact and can be parsed in Python, R or similar.\nThe format is:\n6 bytes = Unix timestamp in ms 2 bytes = X, signed 16 bit integer x 0.0078125 G 2 bytes = Y, signed 16 bit integer x 0.0078125 G 2 bytes = Z, signed 16 bit integer x 0.0078125 G Range: -4G to +4G, 10 bit resolution\nExample:\n016C87D85F170022FFD9FFAC 016C87D85F6E0022FFD9FFAC 016C87D85FC50022FFD9FFAC Read more about processing of raw data here.\nYou can now download your files from the \u0026ldquo;Files\u0026rdquo; tab: Downloading data # After initiating file generation as per the guides in this article, the files will be available in the \u0026ldquo;Files\u0026rdquo; tab which can be accessed in the top menu bar. You can follow the progress of the file generation in the status bar. This allows you to generate several files simultaneously in order to quickly download the files you need.\nClick \u0026ldquo;Download file\u0026rdquo; to download the file to your device. If you want to download several files, click \u0026ldquo;Bulk actions\u0026rdquo;, select the files you want to download and click \u0026ldquo;Download in a single file\u0026rdquo;. ","externalUrl":null,"permalink":"/en/support/web-application/data-export/","section":"Support","summary":"","title":"Data export","type":"support"},{"content":" The SENS web app provides a detailed analysis of physical activity by categorizing data from the sensor into nine distinct activity types. This articles offers a comprehensive guide for users seeking a deeper understanding of the data presented on the platform and the categories in the exported CSV-files. This article consists of the following sections:\nBackground Description of Activity Categories from Web App Description of Categories (columns) in Exported CSV Files Including an explanation of units For a description of sleep parameters, see this article: Description of sleep parameters\nBackground # Data are analysed in 5 second intervals and each interval is estimated to belong to a specific activity category. Different types of physical activity are displayed in unique colours. A measurement may appear on the web app like this:\nThe data is analysed according to the intensity and frequency of the activity. Each bar in the graph represents 15 minutes. The activity categories are represented in the graph vertically with colours depending on the type of activity.\nDescription of Activity Categories from Web App # Measured activity is divided into 9 categories for data visualization on the web-app. Below you will find a brief explanation of these categories. For details, refer to the next section.\nCategory Description No data No data collected. Lying/Sitting Rest The participant sits or lies down with little or no movement. Lying/Sitting Movement Movements of the participant in a lying or sitting state. Standing The participant stands upright. Sporadic Walking The participant stands upright, but with a few movements. The activity relates to \u0026lsquo;Standing\u0026rsquo; and \u0026lsquo;Walking\u0026rsquo;, but is a middle ground and irregular. Walking The participant walks continuously. Moderate Intensity The participant is in a brisk walk, where the activity is higher than \u0026lsquo;Walking\u0026rsquo;, but lower than \u0026lsquo;High intensity\u0026rsquo;. High Intensity The participant runs. Cycling The participant cycles. Description of Categories (columns) in Exported CSV Files # This section contains a detailed description of the activity categories as seen on the column headers in an exported CSV file. You will also find an explanation of the used units of measurement here.\nClick below to go directly to a category\nTime categories:\nUTC Local Unixts Activity categories:\nResting (lying_sitting_rest) Lying or Sitting Movement (lying_sitting_movement) Standing (upright_stand) Sporadic Walking (upright_sporadic_walk) Walking (upright_walk) Moderate Intensity (upright_moderate) High Intensity/Running (upright_run) Biking (cycling) Steps taken (steps, steps2, steps3) Time categories # UTC # Stands for \u0026ldquo;Coordinated Universal Time\u0026rdquo;. It is the primary time standard by which the world regulates times. It is not time zone specific and is therefore used as a reference point for timekeeping. The format is YYYY-MM-DD T hh:mm:ms\nLocal # Shows time in local time zone. The format is YYYY-MM-DD T hh:mm:ms\nUnixts # Stands for \u0026ldquo;Unix timestamp\u0026rdquo;. It is a system for tracking time as a running total of seconds. It represents the number of seconds that have elapsed since 00:00:00 Coordinated Universal Time (UTC), Thursday, 1 January 1970. It is used to manage time across all the different platforms of SENS motion.\nActivity categories # Resting (lying_sitting_rest) # The sensor is in a horizontal position (+- 45 degrees) and the intensity count is less than 2. At higher intensities, the movement may be classified as \u0026rsquo;lying or sitting movement\u0026rsquo;.\nThis category should be seen as an indication of the patient being sedentary, sitting or lying down.\nLying or sitting movement (lying_sitting_movement) # All movements in lying or sitting position (sensor is horizontal +- 45 degrees) and where the intensity count is above 2. Biking might also fall into this category, as the sensor often is horizontal. The signal is further processed, see description of biking.\nStanding (upright_stand) # The sensor is in a vertical position (+- about 45 degrees) and the intensity readings are lower than a minimum movement threshold of about 0.1 G. This category should be seen as an indication of the patient standing still with only minor insignificant movements recognised.\nSporadic walking (upright_sporadic_walk) # All movements in upright position where the vector magnitude is above the movement threshold, but below the moderate intensity and where the movement is not assigned to other categories. This will include epochs where an activity is starting towards the end of the epoch or taking a few steps before again standing still. Also biking slowly for shorter periods will fall into this category.\nWalking (upright_walk) # Assigned when a repeating pattern is detected with:\nAn intensity above the measurement threshold, but below the threshold for moderate activity A frequency above 0.2 Hz Asymmetrical movement, as only one leg takes a step forward at a time This category should be seen as an indication of the patient walking for a period of 5-10 seconds continuously. Smaller walking intervals (trips) below 3-9 seconds will likely be detected as sporadic walking.\nModerate Intensity (upright_moderate) # Assigned to any walking activity above the walking intensity interval for a period of at least 3-9 seconds continuously. The intensity is higher than regular walking, but lower than high intensity activities such as running.\nHigh Intensity/Running (upright_run) # The high intensity category is used for any walking activity above the moderate intensity interval for a period of 3-9 seconds continuously (which is not recognised as biking). This category should be seen as an indication of the patient running or doing high intensity training involving their legs. Smaller running intervals (trips) below 3-9 seconds will likely be detected as moderate intensity or walking.\nBiking (cycling) # A repeating pattern is recognised with a frequency above 0.2 Hz, where both legs move continuously, and the measured movement is symmetrical. This is recognised based on pattern recognition. Also, the intensity must be above the measurement threshold. This category should be seen as an indication of the patient doing a cyclic leg movement for more than 1-2 minutes (with pauses). Smaller biking intervals below 1 minute are not seen often and will likely be detected as walking or sporadic walking.\nSteps taken (steps) # The number of steps is counted during sporadic walking, walking or high intensity activities. The \u0026ldquo;Steps\u0026rdquo; category is further divided into 3 categories depending on the frequency of steps taken.\nCategory Description Steps Steps taken during continuous walking activity and training are based on an analysis in the frequency domain. The characteristic frequency of the walking motion is recognised during a 5 second interval, providing a step count. Steps2 Steps taken during sporadic walking where no continuous frequency can be recognised in the 5 second interval are summarized as 2 steps per 5 second sporadic walking interval, which has proven to be the average number for persons during non-continuous sporadic walking. Steps3 Steps taken during low intensity walking where a continuous frequency can be recognised in the 5 second interval are based on an analysis in the frequency domain. The characteristic frequency of the walking motion is recognised during a 5 second interval, providing a step count. Comparison to wrist band activity trackers: Steps taken with a very low intensity are included in the step count, and as wrist band activity trackers are usually less sensitive, the number of steps taken during low intensity activities such as cooking, or cleaning will be larger with SENS compared to specially wrist band worn activity trackers which tend to underestimate the steps taken during such activities. To compensate for this, using only \u0026ldquo;Step 1\u0026rdquo; will provide a number comparable to wrist band activity trackers.\nUnits of measurement for activity categories # This section details the categorization and definitions for each category, as seen in the column headers in the exported CSV file. The unit of measurement for each column is specified in the header after the second slash, e.g., \u0026lsquo;activity/upright_stand/time\u0026rsquo;.\nWhen exporting a CSV file the categories are measured in two units: \u0026ldquo;count\u0026rdquo; and \u0026ldquo;time\u0026rdquo;.\nUnit Description Time Duration of activity in seconds steps/count Number of steps taken intensity/count Refers to the intensity count. See details below NB! This does not apply to the \u0026ldquo;Non-summarized\u0026rdquo; data sheet. Here, data is not shown as seconds, but as a binary value (0 or 1). A value of 1 in a category indicates that the entire 5-second interval belongs to that category. The intensity count is calculated as on the average overall movement magnitude from the dynamic accelerations within a 5 second interval. The dynamic accelerations are extracted from the measured 3-axis accelerometer data sampled at 12 Hz by removing noise and slow or steady signals—such as gravity or gradual sensor drift. This measurement gives a good indication of the intensity of the measurement. The intensity count in a 5 second interval is normally between 0-100, where 100 is very high intensity.\nCount Category 0-2 Stand 2-10 Sporadic walking (single steps) 10-50 Continuous walk 50-75 Moderate intensity (e.g. slow running) 75-100 High intensity (e.g. fast running) ","externalUrl":null,"permalink":"/en/support/web-application/description-of-activity-categories/","section":"Support","summary":"","title":"Description of activity categories","type":"support"},{"content":" This article describes the different parameters in the \u0026ldquo;Activity \u0026amp; Sleep\u0026rdquo; algorithm. SENS motion can, among others, be used to provide an objective assessment of sleep quality. General # This article describes the different parameters in the \u0026ldquo;Activity \u0026amp; Sleep\u0026rdquo; algorithm.\nSENS motion can, among others, be used to provide an objective assessment of sleep quality. This is done with the \u0026ldquo;Activity \u0026amp; Sleep\u0026rdquo; algorithm, which is based on measuring movement during sleep, sleep quality and sleeping position.\nThe algorithm is built to find the time where a patient goes to sleep between 6 PM and 1 AM, and the time where a patient wakes up between 5 AM and 12 PM. The algorithm only determines sleep at night, and does not regard naps during the day as sleep. Therefore the algorithm will not work for patients with irregular sleeping schedules.\nFor a description of the general activity categories, please refer to this article: Description of activity categories\nDescription of Sleep Parameters # The \u0026ldquo;Activity \u0026amp; Sleep\u0026rdquo; algorithm assesses movements during sleep, duration of sleep and sleeping position. The following table describes the different parameters.\nThe categorization of movement relies on \u0026ldquo;intensity count\u0026rdquo;. Read more about intensity count in this article: Description of activity categories\nBold: categories as seen on the web-app (app.sens.dk)\nItalics: categories as seen on an exported CSV-file\nParameter Description Duration of sleep Estimates when the patient sleeps (time between going to sleep and waking up).\nThe patient is estimated to have gone to bed the last time the patient is up and walking for more than 1 minute between 6 PM and 1 AM.\nThe patient is estimated to have woken up the first time the patient is up and walking for more than 30 seconds between 5 AM and 12 PM. Sleep No Movement\nsleep_no_movement The patient lies horizontally (+- 45 degrees) without movement. Movements with intensity count under 2 are also categorized as Sleep No Movement. Sleep Movement\nsleep_movement The patient lies horizontally (+- 45 degrees) with minimal to moderate movements.\nMovements with an intensity count over 2 is categorized as Sleep Movement.\nThe movement is quantified with +30 seconds before and after the movement. For instance, a movement lasting 10 seconds will be assigned this category with +30 seconds before and after the movement. This means a total of 1 minute and 10 seconds categorized as Sleep Movement.\nNote that movements in this category usually happen during light sleep. Sleep Active\nsleep_active The patient is standing vertically (+- 45 degrees).\nThe movement is categorized as Sleep Active regardless of whether the patient is moving or not, as long as the patient is standing upright. The information above is based on these references:\nJ. B. Webster et al. An Activity Based Sleep Monitor System for Ambulatory Use. J. D. van der Berg et al. Identifying waking time in 24 h accelerometry data in adults using an automated Algorithm. Input from Professor Poul Jennum from Glostrup Hospital Below you see an example of the \u0026ldquo;Activity \u0026amp; Sleep\u0026rdquo; algorithm applied on a real measurement.\n","externalUrl":null,"permalink":"/en/support/web-application/description-of-sleep-parameters/","section":"Support","summary":"","title":"Description of sleep parameters","type":"support"},{"content":" We are currently updating our support articles and some info may be outdated. Read release notes here. GeneralThis article contains a\u0026hellip; . General This article contains a guide to editing participants\u0026rsquo; information, including the monitoring period. \u0026ldquo;Participant\u0026rdquo; refers to the person wearing the SENS motion sensor. To create a new participant, see this article . Edit participant To edit a participant\u0026rsquo;s information, like name or monitor period, click the three dots to the right of a participant and select \u0026ldquo;Edit participant\u0026rdquo;. Select \u0026ldquo;Delete particpant\u0026rdquo; to delete a participant. Participant information Participant information contains information about the participant. Particpant ID/First name does not need to be personally identifiable, and it is optional to enter last name and phone number. \u0026ldquo;Timezone\u0026rdquo; describes the timezone in which the participant has worn the sensor. Sensor information \u0026ldquo;Measurement Method\u0026rdquo; is set to a standard algorithm by default. If you wish to use other algorithms or a dual sensor solution, please contact SENS. Select or change a participant\u0026rsquo;s sensor in the \u0026ldquo;Sensor on Thigh\u0026rdquo; drop down. Available sensors are marked with a green dot before the sensor ID and a green person after the sensor ID: If you wish to have multiple measurements from different sensors connected to the same participant, you need another set-up, kindly contact support for further information. If a sensor\u0026rsquo;s battery has run out during a measurement, or a sensor is lost during the monitor period, you must create a new \u0026ldquo;participant\u0026rdquo; and assign a new sensor. The data from the two sensors can then be concatenated during data processing. Monitor period Select the start- and end time for the monitor period in this modal. The period can always be adjusted, and the defined monitor period does not interfere with the stored data. There are 3 options for selecting a start time: Now The monitor period will start now. Specific time Define the final monitor period by inputting a specific start- and end time. You must select both a time and date. Not Defined Yet The monitor period is not yet known. In this case it is important to return to the particpant and manually input the monitor period. For the end time you can select \u0026ldquo;Specify duration\u0026rdquo; in addition to the options above. Here you can specify the expected number of days, the measurement will last. When the measurement is complete you must adjust the monitor period accordingly. Beware that you can not select an end time if you have not defined a start time. You can not export PDF reports if the start- or end time is not defined yet.\n","externalUrl":null,"permalink":"/en/support/web-application/edit-participant/","section":"Support","summary":"","title":"Edit participant","type":"support"},{"content":"","externalUrl":null,"permalink":"/en/categories/faq/","section":"Categories","summary":"","title":"Faq","type":"categories"},{"content":" Frequently asked questions about SENS motion® This section contains frequently asked questions and answers about SENS motion®.\n","externalUrl":null,"permalink":"/en/support/faq/","section":"Support","summary":"","title":"FAQ","type":"support"},{"content":" Background This article explains how data in an exported CSV file can be made readable in Excel. See this article for a guide on how to\u0026hellip; Background This article explains how data in an exported CSV file can be made readable in Excel. See this article for a guide on how to export data from a sensor in CSV format. Making a CSV file readable After you have downloaded a CSV file from the web app, it can be opened with Excel. The format of the file is not formatted for readability by default. Some settings need to be adjusted to get an overview of the data. Follow this guide to make these adjustments.\nLocate your CSV file and open it with Excel. When first opened, the file will look like this: Mark column A (1) and enter the Data tab in the top menu (Alt + A): Select \u0026ldquo;Text to Columns\u0026rdquo;. This will open a pop-up modal Select \u0026ldquo;Delimited\u0026quot;and click \u0026ldquo;Next\u0026rdquo; Tick off \u0026ldquo;Tab\u0026rdquo; and \u0026ldquo;Comma\u0026rdquo;. Proceed by clicking \u0026ldquo;Next\u0026rdquo;. Click on \u0026ldquo;Advanced\u0026rdquo; Set \u0026ldquo;Decimal separator\u0026rdquo; to \u0026quot; . \u0026quot; (dot) and \u0026ldquo;Thousands separator\u0026rdquo; to \u0026quot; , \u0026quot; (comma). Click \u0026ldquo;OK\u0026rdquo;. Click \u0026ldquo;Finish\u0026rdquo;. The sheet has now been formatted for better readability and will look like this: ","externalUrl":null,"permalink":"/en/support/web-application/format-csv-file-for-readability-in-excel/","section":"Support","summary":"","title":"Format CSV file for readability in Excel","type":"support"},{"content":" Promote increased mobilisation among inpatients Certified medical device (CE-marked) Introduction SENS motion® increases physical activity for admitted patients \u0026#x2714;\u0026#xFE0F;Motivates the patient by visualizing goals and expectations on a tablet next to the bed\u0026#x2714;\u0026#xFE0F;Provides a data-based overview of the patients' physical activity for the healthcare professional\u0026#x2714;\u0026#xFE0F;Improved dialog about physical activity between the healthcare professional, the patient and relatives\u0026#x2714;\u0026#xFE0F;Increased compliance of data-driven management Pilot Package Try SENS motion® for 3 months – It is easy, and support is free of charge\nTry SENS motion® for 3 months and experience the positive effect on patients. It is easy and quick. Complete package for only 19.995 DKK.\nPilot package is an introductory offer that gives the possibility to use SENS motion in a clinical ward for 3 months with up to 120 patients.\n12 Re-usable sensors Loan of 10 Tablets 140 Sensor's Patches Support included Read more about the pilot package Motivational App Motivational visual feedback from the app increases the patient's self-activation \u0026#x2714;\u0026#xFE0F;Motivates the patient to be more active by using elements of gamification and nudging\u0026#x2714;\u0026#xFE0F;The patient walks a visual tour through Copenhagen visiting various attractions\u0026#x2714;\u0026#xFE0F;The patient can continuously track her/his own progress, goals, and expectations\u0026#x2714;\u0026#xFE0F;The patient and healthcare professional can access more information using a detailed history view to facilitate dialog and follow up How to use SENS motion® – For Healthcare 1. A discrete sensor patch is placed on the thigh of the patient at admission. 2. The sensor records physical activity and daily rhythm throughout the stay. 3. A bedside tablet lets the patient follow goals and progress in real time. A tool for the patient and healthcare professionals 4. Highlights the importance of activity and tracks motivation and compliance. 5. Provides live and historical activity dashboards for clinical decisions. Case Highlight Bispebjerg Hospital: Increased out-of-bed time of hospitalized patients View video\nBispebjerg Hospital have in a recent study increased time out of bed for elderly hospitalised patients by 51 minutes per day using SENS motion developed by the Danish start-up SENS Innovation.\nMany patients are inactive 18–20 hours a day in the hospital bed, and body functions decline rapidly during hospitalisation. Therefore, rehabilitation while hospitalised is crucial to avoid loss of independency, decline of quality of life and re-hospitalisation.\nReferences:\nBusiness Insights: A patch and an app are getting patients out of bed European Journal of Internal Medicine: Dall CH, Andersen H, Povlsen TM, Henriksen M. Evaluation of a technology assisted physical activity intervention among hospitalised patients: A randomised study. Eur J Intern Med. 2019 Project supported by the Association of Danish Physiotherapists, RegionH VihTek, Bispebjerg Hospital – Department of Physical and Occupational Therapy and SENS Innovation ApS.\nClinical Evidence SENS motion® increases physical activity for admitted patients \u0026#x2714;\u0026#xFE0F;Each year more than 600.000 elderly patients above the age of 65 are hospitalised (2)\u0026#x2714;\u0026#xFE0F;Elderly hospitalised acute patients spend on average 70–90% of their time in bed and spend only 3–5% of their time either standing or walking (1) Key finding: 100 steps per day reduces the risk for readmissions with 10% (9).\nResearch has shown that even shorter periods of inactivity cause a reduction in physical fitness and muscle mass (3). Compared to younger patients, elderly patients have reduced ability to recover after periods of physical inactivity (4). This also affects the patient\u0026rsquo;s ability to perform daily living (ADL) activities as well as degrading their perceived quality of life (5).\nPhysical inactivity is a strong factor prolonging the disease and rehabilitation period (7), increasing the risk of sequelae leading to hospital (re)admission (6). With an increase of just 100 steps taken per day the risk of readmission is reduced with approximately 10% for elderly hospitalised patients (8).\nReferences\nNina Beyer et al. \u0026ldquo;In acutely admitted geriatric patients offering increased physical activity during hospitalization decreases length of stay and can improve mobility\u0026rdquo; (Nov. 2017) Statistics Denmark, February 2019 J Appl Physiol 108: 1034–1040, 2010 J Am Geriatr Soc. 2003;51:451–458 Pedersen, Klarlund B, 2015 J Am Geriatr Soc. 2004;52:1263–1270 Lancet 2009;373:1874–1882, J Nutr Health Aging. 2016;20:738–751 Arch Phys Med Rehabil. September 2016 (Steve R. Fisher et al.) Pilot package Pilot package offer: 19.995 DKK for use in 3 months in a clinical ward.\nCall +45 2623 8234 or fill out the contact form, then we will contact you as soon as possible.\nEquipment \u0026#x2714; Loan of 10 tablets \u0026#x2714; Loan of 12 reusable sensors \u0026#x2714; 140 patches for mounting the sensor \u0026#x2714; The equipment is delivered in a practical suitcase Implementation \u0026 Service \u0026#x2714; Training of staff \u0026#x2714; Local setup of the system \u0026#x2714; Free data export \u0026#x2714; Data analysis \u0026#x2714; Quality management \u0026#x2714; Support is included Specifications \u0026#x2714; Reusable sensor \u0026#x2714; Accurate measurements \u0026#x2714; No sensor charging \u0026#x2714; User-friendly design \u0026#x2714; Discreet patch that can last up to 14 days \u0026#x2714; Automatic data transfer from sensor to database The normal price for the pilot package is 30.000 DKK based on 3 months of operation with 120 patients. After the end of the period, we will contact you and discuss extension and adaptation to the department\u0026rsquo;s needs. If you do not want to extend the period, you simply return the equipment. Contact us to hear more ","externalUrl":null,"permalink":"/en/healthcare/","section":"Healthcare","summary":"","title":"Healthcare","type":"healthcare"},{"content":" Privacy Policy SENS Innovation ApS This Privacy Policy describes how SENS Innovation ApS (\u0026ldquo;we\u0026rdquo;, \u0026ldquo;us\u0026rdquo;, or \u0026ldquo;our\u0026rdquo;) collects, uses, and protects personal data when you use our websites, mobile applications, and services.\nWe process personal data in accordance with the EU General Data Protection Regulation (GDPR) and applicable Danish data protection law.\nBy using our services, you agree to the collection and use of information as described in this Privacy Policy.\nScope of This Privacy Policy # This Privacy Policy applies to all services operated by SENS Innovation ApS, including but not limited to the following services and mobile applications:\nSENS motion Fibion SENS ErgoConnect Motus – Work Move Measure DEMOS-10 DEMOS-25 Lifetrack This Privacy Policy also applies to future applications and services provided by SENS Innovation ApS, unless otherwise stated.\nData Controller # SENS Innovation ApS\nCVR: DK36024860\nNannasgade 28\n2200 Copenhagen N\nDenmark\nEmail: contact@sens.dk\nWe have not appointed a Data Protection Officer (DPO). All privacy-related enquiries can be directed to the email address above.\nDepending on the specific project or customer relationship, SENS Innovation ApS may act as either data controller or data processor. Where we act as a data processor, processing is governed by a written Data Processing Agreement and carried out only on documented instructions from the data controller.\nInformation Collection and Use # Website: https://sens.dk # When you visit our website, we collect limited, aggregated usage data to understand how the website is used and to improve its performance.\nThis includes:\nPages visited Referring websites Device type, operating system, and browser We do not use cookies or cross-site tracking technologies.\nLegal basis: Legitimate interest (GDPR Article 6(1)(f))\nRetention period: Aggregated statistics are retained for up to 24 months.\nContact and Support # When you contact us via the website or through our support channels, we collect the information you provide, such as name, email address, and message content. We may also collect technical metadata such as IP address and browser user agent.\nThis data is used solely to:\nRespond to enquiries Provide customer support Prevent spam and abuse Legal basis: Legitimate interest (GDPR Article 6(1)(f))\nRetention period: Up to 12 months after last contact, unless a longer retention period is required due to an ongoing customer relationship.\nMobile Applications and Activity Sensors # When using our mobile applications in combination with SENS motion or related activity sensors, the system collects measured activity data generated by the sensors.\nThis data may include movement and activity measurements produced by sensor hardware. The data is used solely for the purposes defined by the specific healthcare, research, or service context in which the solution is deployed.\nDepending on the specific project or customer agreement:\nSENS Innovation ApS acts as data processor, processing activity data on behalf of a healthcare or research organisation acting as data controller, or SENS Innovation ApS acts as data controller, where activity data is collected directly for service operation, development, or explicitly consented use. Where SENS Innovation ApS acts as a data processor, processing is governed by a written Data Processing Agreement.\nActivity data may, depending on context, constitute health-related personal data under GDPR Article 9. In such cases, processing is carried out in accordance with applicable GDPR requirements.\nWe do not use activity data for marketing or profiling purposes.\nDepending on the application, we may also collect:\nPhone number (if you choose to log in) Bluetooth name of the phone We do not collect location data unless explicitly stated within a specific application.\nLegal basis:\nPerformance of a contract (GDPR Article 6(1)(b)) Explicit consent or applicable healthcare or research basis (GDPR Article 9(2)), where required Retention period:\nAccount data: until account deletion or termination of the service Activity data: according to instructions from the relevant data controller or applicable project agreements Log Data # In the event of errors or technical issues, we collect log data for troubleshooting and service improvement purposes.\nLog data may include:\nIP address Device name Operating system version Application configuration Time and date of use Legal basis: Legitimate interest (GDPR Article 6(1)(f))\nRetention period: Up to 30 days\nLegal Basis for Processing # We process personal data based on one or more of the following legal bases:\nGDPR Article 6(1)(b) – performance of a contract GDPR Article 6(1)(f) – legitimate interests GDPR Article 9(2)(a) – explicit consent, where applicable GDPR Article 9(2)(h) or (j) – healthcare or scientific research purposes, where processing is carried out on behalf of a data controller The applicable legal basis depends on the specific service, project, and role of SENS Innovation ApS.\nThird-Party Service Providers # We use carefully selected third-party service providers to help us operate, support, and improve our services. These providers process personal data only on our behalf and in accordance with our instructions.\nRelevant third-party service providers include:\nPlausible Analytics – Website analytics without cookies or cross-site tracking\nhttps://plausible.io/privacy\nFormspark – Handling of website contact form submissions\nhttps://formspark.io/privacy\nZendesk – Customer support and communication management\nhttps://www.zendesk.com/privacy/\nSentry – Error logging and application monitoring\nhttps://sentry.io/privacy/\nAll third-party service providers are subject to appropriate data processing agreements where required.\nData Transfers # Where personal data is processed outside the EU/EEA, appropriate safeguards are applied in accordance with GDPR requirements, such as standard contractual clauses or equivalent mechanisms.\nYour Rights # Under the GDPR, you have the right to:\nAccess your personal data Rectify inaccurate or incomplete data Request erasure of your data Restrict processing Object to processing Receive your data in a portable format (where applicable) Withdraw consent at any time (where processing is based on consent) To exercise your rights, please contact us at contact@sens.dk.\nYou also have the right to lodge a complaint with the Danish Data Protection Agency (Datatilsynet):\nhttps://www.datatilsynet.dk\nChildren’s Privacy # Our services are intended for adults only. We do not knowingly collect personal data from children.\nIf you believe that a child has provided us with personal data, please contact us and we will take appropriate action.\nChanges to This Privacy Policy # We may update this Privacy Policy from time to time. Any changes will be published on this page.\nLast updated: February 2026\nEffective as of: February 2026\nContact Us # If you have any questions about this Privacy Policy or our data protection practices, please contact us at\ncontact@sens.dk.\n","externalUrl":null,"permalink":"/en/privacy-policy/","section":"Privacy Policy","summary":"","title":"Privacy Policy","type":"privacy-policy"},{"content":" Background # This article contains examples for the initial loading of raw accelerometer data to RStudio and Python. Both examples are processing a .bin file. For exporting the .bin file, see this article.\nRStudio # Below is an example script in RStudio for importing and organising the accelerometer data.\nR Script Copy file_path \u0026lt;- \u0026#34;export_1_acc.bin\u0026#34; # Record layout (bytes) TIMESTAMP_BYTES \u0026lt;- 6 AXIS_BYTES \u0026lt;- 2 RECORD_SIZE \u0026lt;- TIMESTAMP_BYTES + 3 * AXIS_BYTES # ---- READ BINARY FILE ---- file_size \u0026lt;- file.info(file_path)$size if (file_size %% RECORD_SIZE != 0) { stop(\u0026#34;File size is not a multiple of record size.\u0026#34;) } con \u0026lt;- file(file_path, \u0026#34;rb\u0026#34;) raw_data \u0026lt;- readBin(con, what = \u0026#34;raw\u0026#34;, n = file_size) close(con) # Arrange raw bytes into records data_matrix \u0026lt;- matrix(raw_data, ncol = RECORD_SIZE, byrow = TRUE) # ---- TIMESTAMP CONVERSION ---- # Convert 6-byte big-endian timestamp (ms since Unix epoch) convert_timestamp \u0026lt;- function(bytes) { sum(as.integer(bytes) * 256^(rev(seq_along(bytes)) - 1)) } timestamps_numeric \u0026lt;- apply(data_matrix[, 1:TIMESTAMP_BYTES], 1, convert_timestamp) timestamps \u0026lt;- as.POSIXct(timestamps_numeric / 1000, origin = \u0026#34;1970-01-01\u0026#34;, tz = \u0026#34;GMT\u0026#34;) # ---- SENSOR AXES ---- decode_axis \u0026lt;- function(mat, scale = 0.0078125) { readBin(as.raw(t(mat)), what = \u0026#34;integer\u0026#34;, size = 2, signed = TRUE, endian = \u0026#34;big\u0026#34;, n = nrow(mat)) * scale } x \u0026lt;- decode_axis(data_matrix[, 7:8]) y \u0026lt;- decode_axis(data_matrix[, 9:10]) z \u0026lt;- decode_axis(data_matrix[, 11:12]) # ---- FINAL DATA FRAME ---- df_final \u0026lt;- data.frame(time = timestamps, x = x, y = y, z = z) write.csv(df_final, \u0026#34;cleaned_data_2.csv\u0026#34;, row.names = FALSE) Python # The following Python function reads a .bin file and stores it in a NumPy array with 4 columns. Column 0 is the timestamp, and column 1-3 are x, y, z. The x, y and z must be scaled with 0.0078125 G.\nPython Function Copy def read_bin_file(self, filename): s = struct.Struct(\u0026#39;\u0026gt;qhhh\u0026#39;) sz = os.path.getsize(filename) data = numpy.zeros([int(sz/12), 4]) i = 0 f = open(filename, \u0026#39;rb\u0026#39;) while True: d = f.read(12) if len(d) == 0: break data[i, :] = s.unpack(b\u0026#39;\\0\\0\u0026#39; + d) #ts, x, y, z = s.unpack(b\u0026#39;\\0\\0\u0026#39; + d) i += 1 return data ","externalUrl":null,"permalink":"/en/support/web-application/processing-of-raw-accelerometer-data/","section":"Support","summary":"","title":"Processing of raw accelerometer data","type":"support"},{"content":" Publications: Peer-reviewed articles for SENS motion # Below is a list of articles where SENS motion® has either been validated or where SENS motion® is included as a supplier in a research project for measuring physical activity.\nArticles # [1] Validation study\nValidation of an accelerometer system for measuring physical activity and sedentary behavior in healthy children and adolescents\nMilther, C.; Winther, L.; Stahlhut, M.; Curtis, D. J.; Aadahl, M.; Kristensen, M. T.; Sørensen, J. L.; Dall, C. H.\nEuropean Journal of Pediatrics (2023) 182:3639–3647\nDOI: https://doi.org/10.1007/s00431-023-05014-z\n[2] Validation study\nCriterion Validity of Linear Accelerations Measured with Low-Sampling-Frequency Accelerometers during Overground Walking in Elderly Patients with Knee Osteoarthritis\nGhaffari, A.; Rahbek, O.; Lauritsen, R.E.K.; Kappel, A.; Kold, S.; Rasmussen, J.\nSensors 2022, 22, 5289.\nDOI: https://doi.org/10.3390/s22145289\n[3] Validation study\nValidation of Two Activity Monitors in Slow and Fast Walking Hospitalized Patients\nBritt Stævnsbo Pedersen, Morten Tange Kristensen, Christian Ohrhammer Josefsen, Kasper Lundberg Lykkegaard, Line Rokkedal Jønsson, Mette Merete Pedersen\nRehabilitation Research and Practice, vol. 2022, Article ID 9230081, 14 pages, 2022.\nDOI: https://doi.org/10.1155/2022/9230081\n[4] TAPAS, Bispebjerg Hospital\nEvaluation of a technology assisted physical activity intervention among hospitalised patients: A randomised study\nChristian Have Dall, Helle Andersen, Tina Myung Povlsen, Marius Henriksen\nEur J Intern Med. 2019 Nov;69:50–56. Epub 2019 Sep 4.\nDOI: https://doi.org/10.1016/j.ejim.2019.08.019\n[5] Children\nOutdoor Kindergartens: A Structural Way to Improve Early Physical Activity Behaviour?\nJeanett Friis Rohde, Sofus Christian Larsen, Mathilde Sederberg, Anne Bahrenscheer, Ann-Kristine Nielsen, Berit Lilienthal Heitmann and Ina Olmer Specht\nInt. J. Environ. Res. Public Health 2023, 20(6), 5131.\nDOI: https://doi.org/10.3390/ijerph20065131\n[6] GAPE, Rigshospitalet Glostrup\nStudy Protocol: The effect of graded activity and pain education (GAPE): an early post-surgical rehabilitation programme after lumbar spinal fusion—study protocol for a randomized controlled trial\nHeidi Tegner, Bente Appel Esbensen, Marius Henriksen, Rachid Bech-Azeddine, Louise Nielsen and Nanna Rolving\nBMC (2020) 21:791.\nDOI: https://doi.org/10.1186/s13063-020-04719-y\n[7] Cooperation Bispebjerg Hospital\nAssociation Between Weight Loss and Spontaneous Changes in Physical Inactivity in Overweight/Obese Individuals With Knee Osteoarthritis: An Eight-Week Prospective Cohort Study\nCecilie Bartholdy, R Christensen, Lars Erik Kristensen, H Gudbergsen, Henning Bliddal, Anders Overgaard, Marianne U Rasmussen, Marius Henriksen\nArthritis Care \u0026amp; Research\nDOI: https://acrjournals.onlinelibrary.wiley.com/doi/epdf/10.1002/acr.23868\n[8] Cooperation Bispebjerg Hospital\nChanges in physical inactivity during supervised educational and exercise therapy in patients with knee osteoarthritis: A prospective cohort study\nCecilie Bartholdy, Søren T. Skou, Henning Bliddal, Marius Henriksen\nPublished: November 13, 2020\nDOI: https://doi.org/10.1016/j.knee.2020.09.007\n[9] Cooperation Bispebjerg Hospital\nPhysical inactivity and current treatments of knee osteoarthritis\nPhD Thesis, Cecilie Bartholdy; Principal Supervisor: Marius Henriksen\nThis thesis has been submitted to the Graduate School of Health and Medical Sciences, University of Copenhagen, 31 January 2019.\nPDF: https://www.fysio.dk/globalassets/documents/fafo/afhandlinger/phd/2019/ceciliebartholdy_phd-afhandling.pdf\n[10] Cooperation Bispebjerg Hospital\nEffectiveness of text messages for decreasing inactive behaviour in patients with knee osteoarthritis: a pilot randomized controlled study\nCecilie Bartholdy, Henning Bliddal and Marius Henriksen\nBartholdy et al. Pilot and Feasibility Studies (2019) 5:112\nDOI: https://doi.org/10.1186/s40814-019-0494-6\n[11] Cooperation Bispebjerg Hospital\nReliability and Construct Validity of the SENS Motion Activity Measurement System as a Tool to Detect Sedentary Behaviour in Patients with Knee Osteoarthritis\nCecilie Bartholdy, Henrik Gudbergsen, Henning Bliddal, Morten Kjærgaard, Kasper Lundberg Lykkegaard and Marius Henriksen\nHindawi Arthritis Volume 2018, Article ID 6596278, 9 pages\nDOI: https://doi.org/10.1155/2018/6596278\n[12] MyoPAD, Cooperation Manchester University and Manchester University Hospital\nA review of accelerometer-derived physical activity in the idiopathic inflammatory myopathies\nAlexander Oldroyd, Max A. Little, William Dixon and Hector Chinoy\nOldroyd et al. BMC Rheumatology (2019) 3:41\nDOI: https://doi.org/10.1186/s41927-019-0088-1\n[13] MyoPAD, Manchester University and Manchester University Hospital\nPatient insights on living with idiopathic inflammatory myopathy and the limitations of disease activity measurement methods – a qualitative study\nAlexander Oldroyd, William Dixon, Hector Chinoy and Kelly Howells\nOldroyd et al. BMC Rheumatology (2020) 4:47\nDOI: https://doi.org/10.1186/s41927-020-00146-3\n[14] Cooperation Nordsjællands Hospital – Hillerød\nAdherence to recommended physical activity restrictions due to threatened preterm delivery – a descriptive multi-center study.\nBendix, J.M., Backhausen, M.G., Hegaard, H.K. et al.\nBMC Pregnancy Childbirth 23, 59 (2023).\nDOI: https://doi.org/10.1186/s12884-023-05371-5\n[15] Cooperation Aalborg University Hospital\nAccelerations Recorded by Simple Inertial Measurement Units with Low Sampling Frequency Can Differentiate between Individuals with and without Knee Osteoarthritis: Implications for Remote Health Care.\nGhaffari A, Rasmussen J, Kold S, Lauritsen REK, Kappel A, Rahbek O.\nSensors. 2023; 23(5):2734.\nDOI: https://doi.org/10.3390/s23052734\n[16] Aalborg University Hospital\nExploring the Feasibility and Usability of Smartphones for Monitoring Physical Activity in Orthopedic Patients: Prospective Observational Study\nArash Ghaffari; Rikke Emilie Kildahl Lauritsen; Michael Christensen; Trine Rolighed Thomse; Harshit Mahapatra; Robert Heck; Søren Kold; Ole Rahbek\nJMIR Mhealth Uhealth 2023;11:e44442.\nDOI: https://pubmed.ncbi.nlm.nih.gov/37283228/\n[17] Bispebjerg Hospital and Copenhagen University Hospital Hvidovre\nVery low levels of physical activity among a broad group of patients hospitalized following hip fracture: A prospective cohort study (the HIP-ME-UP cohort study)\nMaria Swennergren Hansen, Morten Tange Kristensen, Camilla Kampp Zilmer, Anja Løve Berger, Jeanette Wassar Kirk, Kira Marie Skibdal, Thomas Kallemose, Thomas Bandholm, Mette Merete Pedersen, The HIP-ME-UP Collaborative Group\nDOI: https://doi.org/10.1101/2024.02.09.24302483\n[18] University of Southern Denmark and National Institute of Public Health\nPhysical activity, sedentary behavior and sleep: Results from monitoring with accelerometers in \u0026ldquo;The Danes\u0026rsquo; Health 2023\u0026rdquo;\nMartin Eghøj, Sofie Rossen Møller, Rasmus Kildedal, Mette Rasmussen, Mikkel Brandt Petersen, Nidhi Gupta, Ola Ekholm, Anne Illemann Christesen, Christina Bjørk Petersen\nISBN: 978-87-7899-649-7. The report is only available in Danish.\nPDF: https://www.sdu.dk/sif/-/media/images/sif/udgivelser/2024/fysisk_aktivitet_stillesiddende_adfaerd_og_soevn_www_1.pdf\n[19] The National Research Centre for the Working Environment\nA Novel System for the Device-Based Measurement of Physical Activity, Sedentary Behavior, and Sleep (Motus): Usability Evaluation\nPatrick Crowley, Rasmus Kildedal, Simon Overvad Vindelev, Sandra Schade Jacobsen, Jon Roslyng Larsen, Peter J Johansson, Mette Aadahl, Leon Straker, Emmanuel Stamatakis, Andreas Holtermann, Paul Jarle Mork, Nidhi Gupta\nJMIR Form Res 2023;7:e48209.\nDOI: https://formative.jmir.org/2023/1/e48209\n[20] The National Research Centre for the Working Environment\nThe Surveillance of Physical Activity, Sedentary Behavior, and Sleep: Protocol for the Development and Feasibility Evaluation of a Novel Measurement System\nPatrick Crowley, Erika Ikeda, Sheikh Mohammed Shariful Islam, Rasmus Kildedal, Sandra Schade Jacobsen, Jon Roslyng Larsen, Peter J Johansson, Pasan Hettiarachchi, Mette Aadahl, Paul Jarle Mork, Leon Straker, Emmanuel Stamatakis, Andreas Holtermann, Nidhi Gupta\nJMIR Res Protoc 2022;11(6):e35697.\nDOI: https://pubmed.ncbi.nlm.nih.gov/35666571/\n[21] The National Research Centre for the Working Environment\nSurPASS: AUTOMATING DEVICE-BASED MEASUREMENT IN COHORT STUDY RESEARCH\nNidhi Gupta, Andreas Holtermann\nPresentation at 4th ProPASS conference 2022.\n[22] VIA11 – The Danish High Risk and Resilience Study\nInflammatory markers, somatic complaints, use of medication and health care in 11-year-old children at familial high risk of schizophrenia or bipolar disorder compared with population-based controls. The Danish High Risk and Resilience Study – via 11\nAnne Søndergaard, Maja Gregersen, Martin Wilms, Julie Marie Brandt, Carsten Hjorthøj, Jessica Ohland, Sinnika Birkehøj Rohd, Nicoline Hemager, Anna Krogh Andreassen, Christina Bruun Knudsen, Lotte Veddum, Mette Falkenberg Krantz, Aja Greve, Vibeke Bliksted, Ole Mors, Kasper Lykkegaard, Peter Krustrup, Anne E. Thorup, Merete Nordentoft\nNordic Journal of Psychiatry, 26 June 2024.\nDOI: https://pubmed.ncbi.nlm.nih.gov/38923920/\n[23] Thigh-worn accelerometry\nThigh-worn accelerometry: a comparative study of two no-code classification methods for identifying physical activity types\nClaas Lendt, Theresa Braun, Bianca Biallas, Ingo Froböse \u0026amp; Peter J. Johansson\nInternational Journal of Behavioral Nutrition and Physical Activity, 17 July 2024.\nDOI: https://doi.org/10.1186/s12966-024-01627-1\n[24] KneeActivity: The patient\u0026rsquo;s perspective\nThe patient\u0026rsquo;s perspective on rehabilitation with wireless accelerometers, activity tracking and motivational feedback following knee replacement: A qualitative study prior to a randomised controlled trial (KneeActivity)\nCecilie D Skov, Anders Holsgaard-Larsen, Uffe Kock Wiil, Martin Lindberg-Larsen, Claus Varnum, Charlotte M Jensen\nInt J Med Inform. 2024 Dec;192:105624.\nDOI: https://doi.org/10.1016/j.ijmedinf.2024.105624\n[25] KneeActivity: Impact of motivational feedback\nImpact of motivational feedback on levels of physical activity and quality of life by activity monitoring following knee arthroplasty surgery – protocol for a randomized controlled trial nested in a prospective cohort (Knee-Activity)\nCecilie Dollerup Skov, Martin Lindberg-Larsen, Uffe Kock Wiil, Claus Varnum, Hagen Schmal, Charlotte Myhre Jensen, Anders Holsgaard-Larsen\nBMC Musculoskelet Disord. 2024 Oct 2;25(1):778.\nDOI: https://doi.org/10.1186/s12891-024-07878-0\n[26] Motus feasibility study\nA Feasibility Study of the “Motus” System for Wearable-Based Movement Behaviors at Scale. Journal of Physical Activity and Health\nGupta, Nidhi, Eghøj, Martin, Pedersen Ludvigsen, Tonje, Larsen, Jon Roslyng, Wester, Christian Tolstrup, Kildedal, Rasmus, Stamatakis, Emmanuel, Straker, Leon, Aadahl, Mette, Johansson, Peter J., Mork, Paul Jarle, Petersen, Christina Bjørk, \u0026amp; Holtermann, Andreas. (2025).\nBMC Musculoskeletal Disorders 25, Article number: 778 (2024).\nDOI: https://journals.humankinetics.com/view/journals/jpah/aop/article-10.1123-jpah.2024-0832/article-10.1123-jpah.2024-0832.xml\n[27] Actigraphy in Alzheimer\u0026rsquo;s disease\nDiagnostic performance of actigraphy in Alzheimer\u0026rsquo;s disease using a machine learning classifier – a cross-sectional memory clinic study\nMathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen\nAlzheimers Res Ther. 2025 May 21;17(1):111.\nDOI: https://doi.org/10.1186/s13195-025-01751-5\n[28] Deep phenotyping and registry follow-up study of Inter99\nProtocol for the combined cardiometabolic deep phenotyping and registry-based 20-year follow-up study of the Inter99 cohort\nKirsten Schroll Bjørnsbo, Charlotte Brøns, Mette Aadahl, Freja Bach Kampmann, Camilla Friis Bryde Nielsen, Bjørn Lundbergh, Rasmus Wibaek, Line Lund Kårhus, Anja Lykke Madsen, Christian Stevns Hansen, Kirsten Nørgaard, Niklas Rye Jørgensen, Charlotte Suetta, Michael Kjaer, Niels Grarup, Jørgen Kanters, Michael Larsen, Lars Køber, Klaus Fuglsang Kofoed, Ruth JF Loos, Torben Hansen, Allan Linneberg, Allan Vaag\nBMJ Open 2024;14:e078501.\nDOI: https://doi.org/10.1136/bmjopen-2023-078501\n[29] The PANORAMA trial\nEffectiveness of promotion and support for physical activity maintenance post total hip arthroplasty—study protocol for a pragmatic, assessor-blinded, randomized controlled trial (the PANORAMA trial)\nTheresa Bieler, S Peter Magnusson, Volkert Siersma, Mie Rinaldo, Morten Torrild Schmiegelow, Torben Beck, Anne-Mette Krifa, Birgitte Hougs Kjær, Henrik Palm, Julie Midtgaard\nBMC, 13 August 2022.\nDOI: https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-022-06610-4\n[30]\nEffectiveness of a digital health intervention for improving medication adherence among patients with chronic diseases: A randomized controlled trial\nCecilie D. Skov, Anders Holsgaard-Larsen, Uffe Kock Wiil, Martin Lindberg-Larsen, Claus Varnum, Charlotte M. Jensen\nInternational Journal of Medical Informatics, 2024.\nDOI: https://doi.org/10.1016/j.ijmedinf.2024.105624\n[31]\nExploring the impact of telehealth on patient outcomes in mental health: A systematic review\nSandra Plachta-Danielzik, Lena Grasskemper, Karen Schmidt, Stefan Schreiber, Bernd Bokemeyer\nJournal: Journal of Medical Internet Research, 2024.\nDOI: https://doi.org/10.2196/42574\n[32]\nThe role of digital interventions in supporting self-management of chronic diseases: A randomized controlled trial\nLouise L. Kjeldsen, Uffe Læssøe, Jane Marie Bendix, Rikke D. Maimburg\nSAGE Open Medicine, 2024.\nDOI: https://doi.org/10.1016/j.srhc.2024.101059\n[33] Maternal positions in childbirth\nMaternal positions in childbirth – A cohort study of labouring women\u0026rsquo;s movements and body positions the last 24 hours before birth\nLouise L Kjeldsen, Uffe Læssøe, Jane Marie Bendix, Rikke D Maimburg.\nSex Reprod Healthc. 2025 Mar;43:101059. Epub 2024 Dec 18. PMID: 39729687.\nDOI: https://doi.org/10.1016/j.srhc.2024.101059\n[34]\nValidation and comparative study of the Motus system for accurately identifying movement behaviours using different sampling frequencies.\nLudvigsen, T.P., Hørlück, S.A.S., Larsen, J.R. et al. Sci Rep 15, 42377 (2025).\nDOI: https://doi.org/10.1038/s41598-025-26373-7\n[35]\nImpact of physical activity on sleep in adults recovering from substance use disorders: a protocol for an N-of-1 observational study\nThal S, Richardson C, McVeigh J, et al. BMJ Open 2025.\nDOI: https://bmjopen.bmj.com/content/15/12/e111485.full\n[36]\nEvaluating Changes in Physical Activity and Clinical Outcomes During Post-Hospitalisation Rehabilitation for Persons with COPD: A Prospective Observational Cohort Study\nAnna L. Stoustrup, Phillip K. Sperling, Lars P. Thomsen, Thorvaldur S. Palsson, Kristina K. Christensen, Jane Andreasen and Ulla M. Weinreich. Sensors 2026, 26(2), 384.\nDOI: https://doi.org/10.3390/s26020384\n[37]\nThe impact of a school garden program on children’s food literacy, climate change literacy, school motivation, and physical activity: A study protocol\nAnna Stage, Marie Caroline Vermund, Mads Bølling, Camilla Roed Otte, Alberte Laura Oest Müllert, Peter Bentsen, Glen Nielsen, Peter Elsborg. PLoS One 20(4): e0320574.\nDOI: https://doi.org/10.1371/journal.pone.0320574\n[38]\nValidation of sleep-wake estimation from thigh-worn accelerometers against polysomnography in adolescents with and without mental disorders\nMartin Wilms, Anne Søndergaard, Poul Jørgen Jennum, Peter J. Johansson, Pasan Hettiarachchi, Sinnika Birkehøj Rohd, Anette Faurskov Bundgaard, Andreas Faergemand Laursen, Marta Schiavon, Doris Helena Bjarnadóttir Streymá, Maja Gregersen, Mette Falkenberg Krantz, Lotte Veddum, Aja Neergaard Greve, Nicoline Hemager, Ole Mors, Anne Amalie Elgaard Thorup, Merete Nordentoft and Lone Baandrup. 25(1):990.\nDOI: https://doi.org/10.1186/s12888-025-07304-2\n[39]\nValidation of an accelerometer system for activity monitoring in children with functional disabilities\nAnne Stage, Emil Buch Fromber, Peter Elsborg, Mette Røn Kristensen, Silje Mikkelsen, Mads Bølling, Mette Aadahl, Michelle Stahlhut. 185(2).\nDOI: https://doi.org/10.1007/s00431-025-06679-4\n[40]\nEvaluation of the SENS Motion Activity Algorithm to Classify Postures and Movement Among Children Aged 3–14 Years\nCharlotte Lund Rasmussen, Danica Hendry, Amber Beynon, Sarah Stearne, Juliana Zabatiero, Paul Davey, Andres Lloyd Rohl, Leon Straker, Amity Campbell. jmpb.2025-0029.\nDOI: https://doi.org/10.1123/jmpb.2025-0029\n","externalUrl":null,"permalink":"/en/publications/","section":"Publications","summary":"","title":"Publications","type":"publications"},{"content":" Activity Monitoring in Large Scale Research Projects Introduction SENS motion® – For Research enables the use of SENS motion activity sensor for collecting activity data or raw accelerometer data in research projects. SENS motion® – For Research consists of: \u0026#x2714;\u0026#xFE0F;The SENS motion activity sensor\u0026#x2714;\u0026#xFE0F;Smartphone app for automatically transferring data to the secure cloud\u0026#x2714;\u0026#xFE0F;Web-application for managing sensors, viewing or exporting data What we measure Raw Sensor Data # XYZ acceleration Temperature Analysed Activity: # Rest time Standing time Active time (walking, running, cycling) Step count Motion intensity Sleep # Estimated sleep and wake time Movements during sleep How to use SENS motion® – For Research 1. Provide sensors to the people being activity-monitored. 2. The user-friendly patch and instructions allow each individual to replace the patch as required. 3. The activity and sleep is monitored 24/7 Data automatically transferred to the cloud 4. Data from the sensor is automatically and securely transferred to the cloud using the person\u0026#39;s own smartphone. 5. The status of sensors and measurements can be easily monitored using our online tool. 6. The minimal time needed for each sensor and measurements allows monitoring thousands of people. Case Highlight VIA 11 The Danish High Risk and Resilience Study SENS motion® provides more detailed monitoring of more than 500 children, including activity and sleep pattern monitoring, to look for early warning indicators for psychiatric health.\nAbout the project:\nThe purpose of the VIA 11 study is to examine how your child feels when it is 11 years old. We want to gain better insight into how upbringing and hereditary conditions affect each other in relation to mental health. We want to shed light on the factors that give the child resistibility and resilience in relation to mental problems and illness. The results of the study can be used to increase the possibility of preventing mental illness in the future and to understand more about how mental illness occurs. Research is crucial for science to develop new treatments and gain a better understanding of disease and health.\n","externalUrl":null,"permalink":"/en/research/","section":"Research","summary":"","title":"Research","type":"research"},{"content":" We\u0026rsquo;re excited to introduce version 3.9 with major improvements to navigation, sensor management, and file handling. This release focuses\u0026hellip; We\u0026rsquo;re excited to introduce version 3.9 with major improvements to navigation, sensor management, and file handling. This release focuses on making your workflow more efficient and providing better visibility into your sensor data. What\u0026rsquo;s New in 3.9 Reorganised Navigation – Three main sections (Workspace, Sensors, Files) for easier access to key features Enhanced Breadcrumbs – Quick navigation between project and participant views Improved Sensor Overview – Battery level info, sensor state, pending actions, and bulk operations Battery Lifetime Estimation – See estimated days remaining for each sensor battery Streamlined File Export – Export multiple files without closing the menu Expandable Participant Tables – View detailed information without leaving the table Memory Status Indicators – Quickly identify sensors with memory issues ActiMotus 2.1 Algorithm Support – Multi-sensor measurements and enhanced diary features Participant Details Enhancements – New Steps section added Security Improvements – Stricter password policies and enhanced two-factor authentication Expanded Country Support – More countries available for selection Streamlined UI – Overall interface improvements for better usability Reorganised Main Navigation Sections New Feature: We\u0026rsquo;ve reorganised the main navigation to make it easier to find what you need. Three dedicated sections make up the main navigation: Workspace – Manage your projects and participants all in one place Sensors – View and manage all your sensors with enhanced battery and status information Files – Monitor and download your exported files What you need to know: These sections replace the previous navigation structure and provide a more intuitive way to access your most-used features. You\u0026rsquo;ll find all project and participant management tools in Workspace, sensor monitoring in Sensors, and file exports in Files. Additionally, the change organisation menu is now located directly in the header, next to profile settings, making it quicker to switch between organisations. Enhanced Navigation with Breadcrumbs New Feature: Breadcrumb navigation has been added throughout the application to help you understand where you are and quickly navigate between related views. What you need to know: When viewing project or participant details, you\u0026rsquo;ll see breadcrumbs at the top showing your current location. You can click on any breadcrumb to jump directly to that section—for example, from a participant\u0026rsquo;s detail page back to the participant overview or project list. This makes it much faster to navigate between related pages without using the back button. How to use it: Simply click on any item in the breadcrumb trail to navigate to that level. The breadcrumbs automatically update based on your current location. Sensor Overview Improvements Battery Level Information New Feature: The sensor overview now displays comprehensive battery information to help you monitor sensor lifetime. Each sensor now shows: Battery voltage – The current voltage reading from the sensor (when hovering over the icon) Estimated days left – A calculation of how many days of battery life remain based on the sensor\u0026rsquo;s runtime and battery What you need to know: The estimated days left is calculated based on how long the sensor has been active and its current battery voltage. This helps you plan ahead for sensor replacements and ensures you don\u0026rsquo;t lose data due to depleted batteries. How to use it: Look for the battery icon and information under each sensor in the Sensors section. The estimated days left will help you identify sensors that need attention soon. To see battery voltage, hover the mouse cursor over the battery icon. Sensor State and Pending Actions New Feature: The sensor overview now clearly displays both the current sensor state and any pending actions. What you need to know: Sensor State shows what the sensor is currently doing (on, off, etc.) based on the last time it connected to the mobile app Pending Action shows any requested action (like \u0026ldquo;turn on\u0026rdquo; or \u0026ldquo;turn off\u0026rdquo;) that is waiting for the sensor to connect to the mobile app This dual display helps you understand both the current status and what will happen next when the sensor syncs. How to use it: Check the sensor state column to see current status, and the pending action column to see what\u0026rsquo;s queued. Pending actions will be completed automatically when the sensor connects to the SENS motion mobile app. Improved Sensor Action Accessibility New Feature: Turning sensors on and off is now more easily accessible from the sensor overview page, and pending actions are now explicitly displayed. What you need to know: Sensor on/off actions are now available directly from the actions dropdown in the sensor overview table, making them quicker to access. How to use it: Use the actions dropdown (three dots) in the sensor overview table to turn sensors on or off. After initiating an action, you\u0026rsquo;ll see it displayed in the \u0026ldquo;Pending Action\u0026rdquo; column. The action will be completed automatically when the sensor connects to the SENS motion mobile app. You can cancel pending actions from the actions dropdown if needed. Bulk Actions New Feature: Bulk actions are now available at the top of the sensor overview, making it easy to manage multiple sensors at once. What you need to know: You can now select multiple sensors and perform actions on all of them simultaneously: Turn multiple sensors on or off Cancel pending actions for multiple sensors How to use it: Click the \u0026ldquo;Bulk Edit\u0026rdquo; button in the toolbar at the top of the Sensors page. Select the sensors you want to modify, then choose your action from the bulk actions toolbar that appears. Battery Lifetime Estimation New Feature: The system now calculates and displays estimated battery lifetime for each sensor. What you need to know: The estimated battery lifetime shows how many days are left before the sensor\u0026rsquo;s battery is expected to run out. This calculation is based on: How long the sensor has been active The current battery voltage Expected battery consumption patterns Sensor batteries are typically supported for 120 days. After this period, sensors may stop working at any moment. How to use it: Monitor the \u0026ldquo;Est. X days left\u0026rdquo; indicator in the sensor overview. When this number gets low (especially below 10 days), do not use the sensor for any measurements meant to run for longer. Improved File Export Workflow New Feature: The file export process has been streamlined to make it easier to export multiple files without navigating back and forth. What you need to know: When you send a file to export, the export menu no longer closes automatically. Instead, a helpful success popup appears with a direct link to the Files Overview page. This means you can: Export multiple files for the same participant in quick succession Continue working in the export menu without interruption Click the link in the popup to go directly to your Files Overview when ready How to use it: After selecting your export options and clicking export, you\u0026rsquo;ll see a success notification with a \u0026ldquo;Files Overview\u0026rdquo; link. Click it to view your queued files, or continue exporting more files—the menu stays open for your convenience. Project Management Improvements User-Created Projects New Feature: Users can now create new projects directly within the Workspace, removing the need for administrative setup. What you need to know: This update makes it significantly easier to manage larger studies, onboarding new cohorts, or running multiple project streams at once. Previously, project creation required assistance from administrators or support. With this release, any authorised user can set up projects independently. How to use it: Navigate to the Workspace section and click Add Project . From there, you can enter project details, assign participants, and configure settings as needed. Permissions continue to follow organisational roles to ensure project creation remains secure and controlled. Participant Table Enhancements Expandable Rows New Feature: Participant table rows can now be expanded to reveal additional information without navigating to a separate details page. What you need to know: Each participant row can be expanded to show: Additional participant information Measurement details Sensor information Status messages You can expand individual rows by clicking the expand icon, or use the \u0026ldquo;Expand All\u0026rdquo; button in the table header to expand all rows at once for a comprehensive view. How to use it: Look for the expand/collapse icon (chevron) in the first column of the participant table. Click it to expand that row, or click the icon in the header to expand or collapse all rows simultaneously. Memory Status Indicator New Feature: A new memory status column has been added to the participant table to help you quickly identify sensors with memory issues. What you need to know: The memory status shows the estimated amount of data stored on the sensor. When this starts to get full, the sensor needs to connect with the SENS motion mobile app for a while to synchronise data and free up space. How to use it: Check the memory status column in the participant table. If you see a sensor approaching full capacity, you can: Identify which participants need to sync their sensors Resolve sync issues before data is lost Quickly spot missing data that may be due to memory constraints The status is displayed as \u0026ldquo;Est. X used (of 10)\u0026rdquo; to give you a clear picture of memory utilisation. Measurement Start Date Filter New Feature: You can now filter participants by measurement start date, making it easier to find and manage participants who started on the same day. What you need to know: The measurement start date filter allows you to quickly identify and group participants based on when their measurements began. This is particularly useful for: Managing cohorts of participants who started together Tracking progress for participants who began on specific dates Organising participants by measurement start timeline How to use it: Click the Expand button on the right top of the participant overview toolbar. Use the date filter to select a specific measurement start date. The table will automatically update to show only participants whose measurements started on that date, making it easy to find and manage participants who started on the same day. ActiMotus Support New Feature: Enhanced support for ActiMotus algorithm and project-specific questionnaires. ActiMotus 2.1 Algorithm Support New Feature: The system now supports the ActiMotus 2.1 algorithm, enabling multi-sensor measurements for more comprehensive data collection. What you need to know: The ActiMotus 2.1 algorithm allows you to use multiple sensors simultaneously for a single measurement, providing richer data and more accurate results. This is particularly useful for complex studies that require data from multiple sensor sources. How to use it: When setting up measurements, you can now configure multi-sensor setups that leverage the ActiMotus 2.1 algorithm. The system will automatically process data from all configured sensors together. Motus Diary Under Participant Details New Feature: Motus diary entries are now accessible directly from the participant details page. What you need to know: You can now view and manage Motus diary entries for each participant without needing to export. This provides a more integrated view of participant data, combining sensor measurements with diary entries in one place. How to use it: Navigate to a participant\u0026rsquo;s details page and look for the Diary section. Here you\u0026rsquo;ll find all Motus diary entries associated with that participant, making it easier to correlate diary data with sensor measurements. Participant Details Page Enhancements Steps Section New Feature: A new Steps section has been added to the participant details page. What you need to know: The Steps section provides a detailed view of step count data for participants. This information helps you track participant activity levels and correlate them with other measurement data. How to use it: Navigate to a participant\u0026rsquo;s details page and choose the Steps view in the graph dropdown. Here you\u0026rsquo;ll see step count for each interval in the day. Overall Improvements Streamlined UI New Feature: The overall user interface has been streamlined for better usability and a cleaner appearance. What you need to know: Throughout the application, we\u0026rsquo;ve made numerous UI improvements including: Cleaner layouts with better spacing More intuitive button placement Improved visual hierarchy Enhanced colour schemes for better readability Smoother transitions and animations How to use it: These improvements are visible throughout the application. You\u0026rsquo;ll notice a more polished and consistent experience across all pages and features. Security Improvements Stricter Password Policies New Feature: Administrators can now enforce stricter password policies for enhanced security. What you need to know: New password policy options allow you to require: Longer minimum password lengths More complex password requirements (uppercase, lowercase, numbers, special characters) Password expiration policies Password history requirements to prevent reuse How to use it: Please contact our support to make sure your project is configured for the correct policies. Enhanced Country Support New Feature: The list of available countries has been expanded. What you need to know: More countries are now available for selection throughout the application for participant profiles that require phone number for login features. How to use it: When selecting a country in any form or profile, you\u0026rsquo;ll see the expanded list of available countries. Summary Version 3.9 brings significant improvements across navigation, sensor monitoring, file management, and security. The reorganised main sections make it easier to find what you need, breadcrumbs improve navigation efficiency, and enhanced sensor information helps you stay on top of battery and memory status. The improved file export workflow and expandable participant tables make daily operations smoother and more efficient. ActiMotus 2.1 algorithm support enables multi-sensor measurements, while custom questionnaires and diary integration provide more comprehensive data collection options. Security enhancements with stricter password policies and improved two-factor authentication help protect your data, and the streamlined UI creates a more polished user experience throughout the application. We hope these improvements make your work with the SENS platform more productive. As always, we welcome your feedback and suggestions for future releases.\n","externalUrl":null,"permalink":"/en/support/web-application/sens-motion-webapp-39-release-notes/","section":"Support","summary":"","title":"SENS motion Webapp 3.9 - Release Notes","type":"support"},{"content":" Find help and guides for the SENS motion® smartphone application This section contains support articles and documentation for the SENS motion® smartphone app.\n","externalUrl":null,"permalink":"/en/support/app-smartphone/","section":"Support","summary":"","title":"SENS motion® App for Smartphone","type":"support"},{"content":" Information about the SENS motion® sensor and patch system This section contains support articles and documentation for the SENS motion® sensor and patch system.\n","externalUrl":null,"permalink":"/en/support/sensor-patch/","section":"Support","summary":"","title":"Sensor and Patch","type":"support"},{"content":"","externalUrl":null,"permalink":"/en/categories/sensor-patch/","section":"Categories","summary":"","title":"Sensor-Patch","type":"categories"},{"content":"","externalUrl":null,"permalink":"/en/series/","section":"Series","summary":"","title":"Series","type":"series"},{"content":" Support Articles # Find help and documentation for SENS motion® products SENS motion® App for Smartphone Find help and guides for the SENS motion® smartphone application 0 articles View all → Web Application Documentation and support for the SENS motion® web application Add a participant Battery life Data export Description of activity categories Description of sleep parameters Edit participant +4 more 10 articles View all → Sensor and Patch Information about the SENS motion® sensor and patch system 0 articles View all → FAQ Frequently asked questions about SENS motion® 0 articles View all → ","externalUrl":null,"permalink":"/en/support/","section":"Support","summary":"","title":"Support","type":"support"},{"content":"","externalUrl":null,"permalink":"/en/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":" Thank you for your message We have received your message and will get back to you as soon as possible.\nBack to home page\n","externalUrl":null,"permalink":"/en/contact/thank-you/","section":"Contact Us","summary":"","title":"Thank you for your message","type":"contact"},{"content":" We are currently updating our support articles and some info may be outdated. Read release notes here. Background # Each sensor can be in different \u0026lsquo;states\u0026rsquo;. In order for the sensor to be turned on and ready for monitoring, the sensor must first be turned on and be synced with the SENS motion app.\nThe sensor overview clearly displays both the current sensor state and any pending actions. Sensor State shows what the sensor is currently doing (on, off, etc.) based on the last time it connected to the SENS motion app. Pending Action shows any requested action (like \u0026ldquo;turn on\u0026rdquo; or \u0026ldquo;turn off\u0026rdquo;) that is waiting for the sensor to connect to the SENS motion app. This dual display helps you understand both the current status and what will happen next when the sensor syncs with the SENS motion app. Check the sensor state column to see current status, and the pending action column to see what\u0026rsquo;s queued.\nPending actions will be completed automatically when the sensor connects to the participant\u0026rsquo;s mobile app.\nWe estimate how long a sensor\u0026rsquo;s battery should last (default about 120 days). We track how long it has been running and when it was last seen. If it\u0026rsquo;s still running, time since last seen is counted against the battery. If we have a recent voltage reading, we adjust the remaining days based on voltage thresholds. This is so we can get a more realistic estimate, in case something has happened to the battery. Very low voltage forces the remaining days to near-zero; normal voltage leaves the estimate as-is. If the sensor hasn\u0026rsquo;t been seen in a very long time (years) or has run far beyond its expected lifetime, we show \u0026ldquo;Battery expired\u0026rdquo; in UI. If the battery data is older than 10 days, we determine voltage is \u0026ldquo;unknown.\u0026rdquo;, and only show the estimated days left (not the voltage) in the UI. How to turn on the sensor # You can either turn the sensors on, one by one or in bulk. See both ways below.\nLog on to https://app.sens.dk/r/login Turn on sensors one by one: # Enter the \u0026ldquo;Sensors\u0026rdquo; tab, click the three dots on the far right and click \u0026ldquo;Turn sensor on\u0026rdquo;: Turn on several sensors at once (bulk): # Click \u0026ldquo;Bulk actions\u0026rdquo;, and select the sensors you want to turn on, followed by clicking \u0026ldquo;Turn on the selected sensors\u0026rdquo;: The sensors will change under \u0026ldquo;Pending action\u0026rdquo; to: Open the SENS motion app on your mobile/tablet (accept bluetooth and location services when you download first time): Shake the sensor a few times to activate the accelerometer to connect the sensor with the SENS motion app.\nCheck on the website if the status has changed to \u0026ldquo;On\u0026rdquo;:\nTo turn off the sensors, it is exactly the same procedure, but by clicking \u0026ldquo;Turn sensor off\u0026rdquo; instead.\nTo save battery life time, it is important to turn off the sensors after use. NOTE: The sensor is only switched on when the status changes to \u0026ldquo;On\u0026rdquo; and only turned off when the state indicates \u0026ldquo;Off\u0026rdquo; ","externalUrl":null,"permalink":"/en/support/web-application/turn-the-sensor-onoff/","section":"Support","summary":"","title":"Turn the sensor on/off","type":"support"},{"content":" Documentation and support for the SENS motion® web application Add a participant Battery life Data export Description of activity categories Description of sleep parameters Edit participant Format CSV file for readability in Excel Processing of raw accelerometer data SENS motion Webapp 3.9 - Release Notes Turn the sensor on/off sensor settings power control ","externalUrl":null,"permalink":"/en/support/web-application/","section":"Support","summary":"Documentation and support for the SENS motion® web application","title":"Web Application","type":"support"},{"content":"","externalUrl":null,"permalink":"/en/categories/web-application/","section":"Categories","summary":"","title":"Web-Application","type":"categories"}]