EDA and Physical Activity Sensor

The EdaMove 3 provides researchers with the most comprehensive tool for recording and analysing Electrodermal (Galvanic Skin Response) and physical activity. Capable of capturing up to 4 weeks of data, the EdaMove 3 allows researchers to isolate and understand emotional affect with greater clarity than before.

The EdaMove 3 combines our acclaimed accelerometers (featured in all our Move 3 range sensors), a high quality EDA sensor, and a Bluetooth Smart interface that allows the sensor to interact with our class leading Experience Sampling Platform movisensXS to trigger questionnaires based on changes in physiological parameters.

The sensor acquires the raw data of the EDA and the 3D acceleration of a test subject allowing secondary parameters like skin conductance level (SCL), skin conductance responses (SCR), and activity intensity to be calculated with the movisens DataAnalyzer software.

Capturing motion, barometric pressure and temperature allows a more precise analysis of the data. These parameters allow artefacts that normally hinder the analysis of EDA data in an ambulatory setting to be identified, and isolated accordingly. The sensor is worn with a wristband on either the wrist or ankle and comes with multi-use, non-polarizing sintered Ag/AgCl electrodes.





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Top-Features

  • Live analysis of data on the sensor
  • Bluetooth-Smart Interface
  • Complies with all relevant EDA-standards
  • Perfect signal quality in everyday life
  • Includes 3D acceleration sensor for the acquisition of physical activity and context information
  • Sustainable data format
  • Practical and easy to use in studies
  • Java API for USB (Windows)
  • API: Example for Bluetooth Smart (Android)

Applications

  • Interactive ambulatory assessment
  • Mobile long-term monitoring of EDA (elecrodermal activity) / GSR (galvanic skin resonse)
  • Psycho physiologic monitoring
  • Research of the autonomic nervous system (ANS)
  • Behavioral monitoring
  • Work and Organisational Psychology
  • Clinical psychology
  • Affective computing
  • Integration into complex systems possible

Matching products and services

DataAnalyzer Software, Box

DataAnalyzer
Software for the analysis of sensor data

Accessories
and consumables for the sensors

SensorTrigger
Solution for Interactive Ambulatory Assessment

Smartphone mit movisensXS

movisensXS
Smartphone-based e-Diary with experience sampling

Downloads

Software
Documentation
Beispieldaten
External Tools

Technical data

Power supply

Lithium-Polymer-Battery

Battery voltage

3,0 - 4,2 V

Number of charging cycles

300 (with 1C/1C > 80%)

Internal memory

4 GB

Maximum recording capacity

4 weeks

Battery run time

~ 5 days

Recharging time

~ 1 hour

Size of sensor (W x H x D)

62,3 x 38,6 x 11,5 mm

Weight of sensor

31 g

Internal sensors

EDA sensor:

Exosomatic method, constant voltage, DC, 0,5V

Resolution: 14 bit, Input range 2 µS up to100 µS

Bandwith: DC to 8 Hz

Output rate: 32 Hz

3D acceleration sensor:

Measurement range: +/- 8 g

Noise: 4 mg

Output rate: 64 Hz

Pressure sensor:

Measurement range: 300 - 1100 hPa

Noise: 0,03 hPa

Output rate: 1 Hz

Temperature sensor:

Output rate: 1 Hz

Live analysis

EDA SCL mean

Temp mean

Movement Acceleration

Step count

charging

State of charge

Indicators

LED, 3-color

Vibration alarm

Marker

Interfaces

Micro-USB, Bluetooth Smart (4.0)

API

Java API for USB (Windows)

Example for Bluetooth Smart (Android)

Wear location

Wrist, Ankle

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

Humidity:

0 to 75 % Relative Humidity

Atmospheric pressure:

300 to 1100 hPa absolute

Warranty

1 year

Literature

  • 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...
  • Estudo piloto em câmara climática: efeito da luz natural em aspectos de saúde e bem-estar não relacionados à visão.
    Cintia Akemi Tamura & Eduardo Leite Krüger (2016) in: Ambiente Construído (16). Read more...
  • Detecting cognitive underload in train driving: A physiological approach.
    Dan Basacik & Sam Waters & Nick Reed (2015). Read more...
  • Mobile Sensors for Multiparametric Monitoring in Epileptic Patients.
    Stefan Hey & Panagiota Anastasopoulou & André Bideaux et al. (2015) in: Cyberphysical Systems for Epilepsy and Related Brain Disorders: Multi-parametric Monitoring and Analysis for Diagnosis and Optimal Disease Management. Read more...
  • A personalized and reconfigurable cyberphysical system to handle multi-parametric data acquisition and analysis for mobile monitoring of epileptic patients.
    A. Bideaux & P. Anastasopoulou & S. Hey et al. (2014) in: Sensing and Control S&C BArcelona, Spain. Read more...
  • Evaluation of environmental effects on the measurement of electrodermal activity under real-life conditions.
    Dorothee Kapp & Kristina Schaaff & Jörg Mathias Ottenbacher et al. (2014) in: Biomedical Engineering / Biomedizinische Technik (59).
  • Komfortgewinn für Passagiere auf Langstreckenflügen durch den Einsatz chronobiologisch angepasster LED-Kabinenbeleuchtung.
    A. Leder & J. Krajewski & S. Schnieder (2013) in: Deutscher Luft- und Raumfahrtkongress 2013, Stuttgart. Read more...
  • Publication recommendations for electrodermal measurements.
    WALTON T ROTH & MICHAEL E DAWSON & DIANE L FILION (2012) in: Psychophysiology (49).

Weitere Publikationen finden Sie hier.