Analysis Software

The movisens DataAnalyzer processes raw sensor data to calculate physiological parameters with a selectable output interval in just a few clicks.

The DataAnalyzer extracts the selected parameters into .csv format, allowing researchers to delve deeper and process the data further in Excel or SPSS. For a big picture overview, our pdf reports provide a great summary of the key information extracted from the sensor in easy to read charts and tables.

The software package works with raw sensor data saved in the unisens format, and complements our Move 3 range of sensors (Move 3, EcgMove 3, LightMove 3, EdaMove 3). The DataAnalyzer also allows the batch processing of an entire study cohort, automatically displaying the available parameters based on the sensor type, and wear position.

As with our sensors, the DataAnalyzer is a one-off purchase with no renewal or subscription fees. Once you pay for it, you own it. It comes standard with algorithms that allow the calculation of physical parameters derived from the data gathered by the class leading accelerometers featured in our Move 3 sensors (Move 3, EcgMove 3, LightMove 3, EdaMove 3).

The following additional modules are available for individual purchase:

  • Energy Expenditure
  • Cardio/HRV
  • Electrodermal Activity



DataAnalyzer Software, Box

Top features

  • Batch analyze complete studies with one click
  • Selectable output parameters
  • Configurable output intervals
  • Optimized output that's suitable for further processing (Excel, SPSS)
  • Integrated generator of informative PDF reports

Applications

  • Analysis of activity, ECG and EDA measurement data
  • Activity recognition and energy expenditure calculation
  • Heart rate and heart rate variability
  • Electrodermal activity
  • Research of the autonomic nervous system (ANS)
  • Behavioral monitoring
  • Psycho-physiological stress monitoring

Matching products and services

Move 3
Activity Sensor
for the assessment of physical activity

EcgMove 3
ECG and Activity Sensor
for the assessment of ECG and physical activity

LightMove 3
Light and Activity Sensor
for the assessment of ambient light and physical activity

EdaMove 3
EDA and Activity Sensor
for the assessment of ambient light and physical activity

Description of Modules

Base-Module:

Included as standard with the DataAnalyzer software licence, the algorithms allow the analysis of the physical activity data collected from the Move 3, LightMove 3, EcgMove 3, and EdaMove 3 sensors. You can select from the following output parameters:

  • Body Position
  • Acceleration along the body axis
  • Steps
  • Activity class
  • Inclination of body
  • Altitude
  • Vertical speed
  • Physical activity report (PDF) - A detailed report including hourly summaries of body position, activity intensity, steps, etc… presented in an easy to read chart format.


Energy Expenditure-Module:

With the Energy Expenditure Module you can analyze physical activity data of the sensors Move 3 and EcgMove 3 with regards to energy expenditure. The following output parameters are available:

  • Activity Energy Expenditure
  • Total Energy Expenditure
  • Metabolic Equivalent of Task / MET
  • Energy expenditure summary
  • Physical activity and energy expenditure report (PDF) – A detailed report displaying the physical activity (activity intensity, body position, steps, etc…) in addition to the energy expenditure of the participant/s. Charts depict an hour by hour analysis of Energy expenditure and MET levels.


Cardio/HRV-Module

With the Cardo/HRV-Module you can analyze the ECG signal data obtained by the EcgMove 3, either by generating a detailed Heart Rate Variability report or extracting information on the following parameters:

  • ECG R peaks
  • Normal beats and intervals
  • Beat by beat heart rate
  • Heart rate
  • HRV parameter Low Frequency (LF)
  • HRV parameter High Frequency (HF)
  • HRV parameter Low to High Frequency Ratio (LF/HF)
  • HRV parameter SDNN
  • HRV parameter RMSSD
  • HRV parameter SD1
  • HRV parameter SD2
  • HRV parameter SD2/SD1
  • HRV report (PDF) - A detailed report displaying a HRV Spectogram in addition to charts displaying Heart Rate, the Baevskii Stress Index, LF to HF ratios, and activity classes of a participant/s. The report concludes with a table displaying the overall HRV parameters and Activity classes of the participant/s.


EDA-Module

With the EDA Module you can analyze the EDA data captured by the sensor EdaMove 3. The following output parameters are available:

  • Electrodermal Activity report as text file
  • Skin conductance level
  • SCR amplitudes
  • SCR rise times
  • SCR Energies
  • SCR half recovery times
  • Number of SCR
  • Mean of SCR Amplitudes
  • Mean of SCR rise times
  • Mean of SCR energy
  • Mean of SCR recovery times

System Requirements

The DataAnalyzer needs the following:

  • A PC with Microsoft Windows XP or higher
  • Microsoft Excel for reports in Excel format
  • Administrator rights during installation
  • A minimum of 450 MB free space on hard disc

Downloads

Software
Manuals
Data examples
Example reports
External Tools

Change history

  • Added new module Sleep with sleep/wake detection
  • Added support for ambient light sensor LightMove3
  • Added algorithms for ambient light: illuminance, color temperature, lights out detection
  • Improved ECG RR filter
  • Improved wear detection algorithm
  • New output EDA SCL signal
  • Added NonWear, MVPA and MVPA bouts to Summary report
  • Added support for unisens-CSV format
  • Improved colors in reports for better readability
  • Improved report for MET level
  • Improved tables in reports
  • Better support of different sensor locations in reports
  • Improved body position detection for sensor location thigh
  • Fixed bug for missing values at the end of Results.xslx
  • New PDF reports
  • Support for new sensors and sensor positions
  • New report generator for PDF reports
  • Added wear time detection algorithms
  • Improved filtering of RR intervals
  • Improved HRV parameter RMSSD
  • New algorithms for ambient light sensors (illumination and color temperature)
  • No need of LaTeX and Excel as system requirements
  • Improved filtering of RR list
  • Improved HRV spectral parameter calculation
  • Improved AEE, TEE and MET Calculation by using additional Model for slopes
  • Bugfixes in PDF reports
  • Support for new sensors
  • Improved EDA SCR detection
  • Added missing parameter EdaScl (Skin Conductance Level)
  • New parameter ECG derived respiration (EDR)
  • Considering output interval for EdaArousal
  • Added sitting/standing detection (sensor position thigh)
  • Added temperature parameter
  • Added HRV Parameter pNN50
  • Output of all selected parameters as Excel including clearly laid out column descriptions (Results.xslx)
  • New module for Cardio/HRV with all common parameters, Baevskii Stress Index and a new HRV report as PDF
  • New module for EDA with all relevant EDA parameters, including new arousal parameter
  • All PDF reports revised and now available in German and English
  • New PDF overview report for physical activity
  • Improved detection of body positions
  • Improved energy expenditure calculation while resting/sitting
  • Improved plots and layout in all reports

Literature and Validation

  • A Comparison of Two Commercial Activity Monitors for Measuring Step Counts During Different Everyday Life Walking Activities.
    Panagiota Anastasopoulou & Sascha Härtel & Stefan Hey (2013) in: International Journal of Sports Science and Engineering (Vol. 07 (2013) No. 01). Read more...
  • Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer.
    Panagiota Anastasopoulou & Sascha Härtel & Mirnes Tubic et al. (2013) in: Wireless Mobile Communication and Healthcare.
  • A new method to estimate energy expenditure using accelerometry and barometry-based energy models.
    Panagiota Anastasopoulou & Layal Shammas & Jürgen Stumpp et al. (2011) in: 45. DGBMT Jahrestagung. Freiburg.
  • Classification of Human Physical Activity and Energy Expenditure Estimation by Accelerometry and Barometry.
    P. Anastasopoulou & M. Tansella & J. Stumpp et al. (2012) in: 34th Annual International Conference of the Engineering in Medicine and Biology Sciety, EMBC 2012, San Diego USA. Read more...
  • Assessment of Human Gait Speed and Energy Expenditure Using a Single Triaxial Accelerometer.
    Panagiota Anstasopoulou & Shammas Layal & Stefan Hey (2012) in: Wearable and Implantable Body Sensor Networks (BSN), 2012 Ninth International Conference on. Read more...
  • Energieumsatzmessung mit Aktivitätssensoren – Validität des kmsMove-Akzelerometers.
    B. von Haaren & J.-P. Gnam & S. Härtel et al. (2011) in: Kinder bewegen - wissenschaftliche Energien bündeln..
  • Validity of the kmsMove-sensor in calculating energy expenditure during different walking intensities.
    B. von Haaren & J.-P. Gnam & S. Helmholdt et al. (2011).
  • Does a 20-week aerobic exercise training programme increase our capabilities to buffer real-life stressors? A randomized, controlled trial using ambulatory assessment.
    Birte von Haaren & Joerg Ottenbacher & Julia Muenz et al. (2015) in: European Journal of Applied Physiology. Read more...
  • Estimation of energy expenditure using accelerometers and activity-based energy models - validation of a new device.
    S. Härtel & J. P Gnam & S. Löffler et al. (2011) in: European Review of Aging and Physical Activity (Volume 8). Read more...
  • A mixed-methods study of physiological reactivity to domain-specific problem solving: methodological perspectives for process-accompanying research in VET.
    Tobias Kärner (2017) in: Empirical Research in Vocational Education and Training (9). Read more...
  • Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study)..
    Layal Shammas & Tom Zentek & Birte von Haaren et al. (2014) in: Biomedical engineering online (13). Read more...
  • Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology..
    (1996) in: Circulation (93).

You can find more publications here.