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

  • Lightweight Visual Data Analysis on Mobile Devices - Providing Self-Monitoring Feedback.
    Simon Butscher & Yunlong Wang (2016) in: VVH 2016 - 1st International Workshop on "Valuable visualization of healthcare information": from the quantified self data to conversations (in conjunction with AVI '16). Read more...
  • Contributions à l’élaboration d’un système d’aide médico-sociale à l’aide d’un robot humanoïde.
    Louise Devigne (2015). Read more...
  • Situationsadaptive Navigationsassistenz für Menschen mit Demenz.
    Philipp Koldrack & Ron Henkel & Katja Zarm et al. (2015) in: AAL-Kongress 2015. Read more...
  • Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.
    Dimitrios Triantafyllopoulos & Panagiotis Korvesis & Iosif Mporas et al. (2015) in: Journal of Medical Systems (40). Read more...
  • Bewegungsangst bei chronischer Herzinsuffizienz – Erste Ergebnisse zur Validierung eines Messinstruments..
    Susan; Hennig Diane; Hoffmann Jeremy M.; Anastasopoulou Panagiota; Hey Stefan Spaderna Heike; Hellwig (2015) in: 12. Kongress der Fachgrupppe Gesundheitspsychologie - Abstracts. Read more...
  • Fitness, kognitive Leistungsfähigkeit und Wohlbefinden bei jungen Erwachsenen - Interventionsstudien zum Einfluss von Ausdauertraining.
    Katrin Walter (2015). Read more...
  • Validation and comparison of two methods to assess human energy expenditure during free-living activities.
    Panagiota Anastasopoulou & Mirnes Tubic & Steffen Schmidt et al. (2014) in: PLOS (PLoS ONE 9(2): e90606). Read more...
  • Erfassung körperlicher Aktivität mittels Akzelerometrie - Möglichkeiten und Grenzen aus technischer Sicht.
    Stefan Hey & Panagiota Anastasopoulou & Birte von Haaren (2014) in: Bewegungstherapie und Gesundheitssport (30(02)). 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...
  • Detection of Parameters to Quantify Neurobehavioral Alteration in Multiple Sclerosis Based on Daily Life Physical Activity and Gait Using Ambulatory Assessment.
    Layal Shammas & Birte von Haaren & Angela Kunzler et al. (2014) in: Zeitschrift für Neuropsychologie (25). 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...
  • The Association between Short Periods of Everyday Life Activities and Affective States: A Replication Study Using Ambulatory Assessment.
    Thomas Bossmann & Martina Kanning & Susanne Koudela-Hamila et al. (2013) in: Frontiers in Psychology (4). Read more...
  • Characteristics of the activity-affect association in inactive people: an ambulatory assessment study in daily life.
    B. von Haaren & S.N. Loeffler & S. Haertel et al. (2013) in: Frontiers in Movement Science and Sport Psychology (4).
  • Acute and medium term effects of a 10-week running intervention on mood state in apprentices.
    Katrin Walter & Birte von Haaren & Simone Löffler et al. (2013) in: Frontiers in Movement Science and Sport Psychology (4). 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...
  • Measurement of daily mobility under fampridine-therapy with Movisens-system in patients with multiple sclerosis.
    R. Kempcke & T. Schultheiß & S. Sobek et al. (2012) in: 28th European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS).
  • 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...
  • Aktuelle Messverfahren zur objektiven Erfassung körperlicher Aktivitäten unter besonderer Berücksichtigung der Schrittzahlmessung.
    D. Rosenbaum (2012) in: Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz (55). Read more...
  • Kindergarten in Bewegung. Zur Qualität von Bewegungskindergärten..
    R. Schwarz (2012) in: Kita aktuell.
  • Assessment der Mobilität im Alltag zur Unterstützung von MS-Patienten.
    Shammas, L. & Bachis, S. & Anastasopoulou, P. et al. (2012) in: 15. Jahrestagung der dvs-Kommission Gesundheit, Leipzig..
  • 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...
  • Trends und Möglichkeiten zur Erfassung körperlicher Aktivität im Alltag.
    S. Hey & U. Großmann & J. Ottenbacher et al. (2011) in: Kinder bewegen - wissenschaftliche Energien bündeln. Jahrestagung der dvs-Kommission Gesundheit, Karlsruhe.
  • Einsatz sensorgestützter Verfahren im Gesundheitswesen: Herausforderungen und Lösungsansätze.
    D.I.D.S. Saboor & M.F.H.M. Schallhart (2011). Read more...
  • Bewegungskindergärten: empirische Befunde und praktisches Wissen.
    R. Schwarz (2011) in: S. Baadte, K. Bös, S. Scharenberg, R. Stark, A. Woll (Hrsg.), Kinder bewegen - Energien nutzen (S. 65-75). Landau: VEP..
  • 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).

Weitere Publikationen finden Sie hier.