Analysis Software

movisens DataAnalyzer is a software for the processing and analysis of sensor data - designed for research applications. The software is tailored for the use with movisens sensors (Move II, ekgMove, and edaMove).

The following analysis modules are available:

  • Physical Activity
  • Energy Expenditure
  • Cardio/HRV
  • Electrodermal Activity

DataAnalyzer allows the calculation of secondary parameter and the generation of result tables (Excel) and reports (PDF).




DataAnalyzer Software, Box

Top features

  • Batch analyze complete studies with one click
  • Free selection of output parameters
  • Configurable output intervals
  • Output is optimized for further processing (Excel, SPSS)
  • Integrated generator for informative reports (PDF)

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 physiologic stress monitoring

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EdaMove 3
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Downloads

Software
Manuals
Data examples
Example reports
External Tools

Description of Modules

Physical Activity-Modul:

With the Physical Activity Module you can analyze physical activity data of the sensors Move2, ekgMove, edaMove. The following output parameters are available:

  • Body Position
  • Acceleration along the body axis
  • Steps
  • Activity class
  • Inclination of body
  • Altitude
  • Vertical speed
  • Physical activity report (PDF)


Energy Expenditure-Modul:

With the Energy Expenditure Module you can analyze physical activity data of the sensors Move2, ekgMove with regard 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)


Cardio/HRV-Modul

With the Cardo/HRV-Modul you can analyze the ECG data of the sensor ekgMove. The following output parameters are available:

  • 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)


EDA-Modul

With the EDA Module you can analyze the EDA data of the sensor edaMove. 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 following requirements have to be me to use DataAnalyzer:

  • 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

Change history

  • 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 parametern, Baevsky 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 availabe in German and English
  • New PDF overview report for pyhsical activity
  • Improved detection of body positions
  • Improved energy expenditure calculation while resting/sitting
  • Improved plots and layout in all reports

Literature and Validation

  • 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...
  • 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...
  • 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 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...
  • 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...
  • 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.
  • 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...
  • 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).
  • 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.