As part of the new sensor generation, the EdaMove 4 takes the proven quality of the EdaMove 3 and incorporates the most sought after improvements from extensive discussions with researchers in the field.

The 4th sensor generation offers researchers numerous advantages, including:

  • New design for optimal handling: The improved case offers a sleek design aesthetic offering many practical advantages. The waterproof and dirt-repellent housing coupled with the improved carrying systems make the sensors simpler, more versatile and safer to use.
  • More data collection capabilities: Thanks to the integration of the latest technologies, the 4th generation sensors now incorporate a Gyroscope (Angular Rate Sensor).
  • Improved analysis possibilities: Our highly acclaimed acceleration sensor also received an overhaul, and now records the measurement data at an even higher resolution. Consequently, we’ve achieved significant improvements in the obtainable results, especially in the analysis of sedentary behavior and non-wear detection.
  • Increased data retention: A new Bluetooth buffer ensures the preservation of data during disconnection, with the buffered data transferred upon reconnection; thus guaranteeing a continuous data recording at all times.
  • Broader research applications: Already a class leader in quality data acquisition for many research areas, these improvements expand the EdaMove 4's research capabilities. All the while remaining the best choice for researcher's requiring high quality ambulatory EDA and physical activity data.

The EdaMove 4 provides researchers with the most comprehensive tool for recording and analysing Electrodermal (Galvanic Skin Response) and physical activity. The sensor combines our world-class 4th generation accelerometer, a high-quality EDA sensor and a Bluetooth Smart interface that allows the sensor to interact with our Experience Sampling platform, movisensXS to trigger questionnaires based on changes in physiological parameters.

Capable of capturing up to 4 weeks of data, the EdaMove 4 allows researchers to isolate and understand emotional affect with greater clarity than before. A new electrode based attachment system ensures an efficient and durable connection, recording a high quality signal with minimal effort. In addition to the EDA signal that allows the calculation of secondary parameters like skin conductance level (SCL) and skin conductance responses (SCR), the sensor acquires the raw data of the 3D acceleration of a participant, thus also allowing the calculation of activity parameters such as activity intensity with the movisens DataAnalyzer.

The additional recording of angular rate, temperature and barometric air pressure enable the swift discovery of and isolation of many of the typical artifacts that plague EDA data captured by standard ambulatory measurement systems. The EdaMove 4 connects to a comfortable textile band worn on either the wrist or ankle, increasing participant comfort and compliance, and providing greater measurement quality for researchers.


  • New design with new carrying systems in a waterproof housing
  • Advanced data acquisition through integrated gyroscope
  • New acceleration sensor with higher resolution
  • Meets all relevant EDA standards
  • Live analysis of measurement data
  • Improved data transfer via Bluetooth Smart interface
  • Exact and validated Energy Expenditure calculations and detection of everyday activities
  • Java API for USB (Windows)
  • API: Example implementation for Bluetooth Smart (Android)


  • Interactive ambulatory assessment
  • Mobile long-term monitoring of EDA (elecrodermal activity) / GSR (galvanic skin response)
  • Psycho physiologic monitoring
  • Research of the autonomic nervous system (ANS)
  • Behavioral monitoring
  • Clinical Psychology
  • Affective Computing

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Documentation and Support
External Tools

Technical Data

Power supply


Battery voltage

3,7 V

Number of charging cycles

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

Internal memory

4 GB

Maximum recording capacity

4 Weeks

Battery run time

~ 4 days

Recharging time

~ 1 hour

Size of sensor (W x H x D)

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

Weight of sensor

26 g

Protection rate

Waterproof (IP64)

Internal sensors

EDA Sensor:

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

Resolution: 14 bit, Input range 2 µS -100 µS

Bandwith: DC - 8 Hz

Output rate: 32 Hz

3D acceleration sensor:

Measurement range: +/- 16 g

Output rate: 64 Hz

Rotation rate Sensor:

Measurement range: +/-2000 dps

Resolution: 70 mdps

Output rate: 64 Hz

Pressure sensor:

Measurement range: 300 - 1100 hPa

Resolution: 0,03 hPa

Output rate: 8 Hz

Temperature sensor:

Output rate: 1 Hz

Live analysis

Skin Conductance Level

Movement Acceleration

Step count


LED, 3-color

Vibration alarm

User Interfaces

Marker (tapping)


Micro-USB, Bluetooth Smart (4.0)


Java API für USB (Windows)

Example for Bluetooth Smart (Android)

Wear locations

Wrist, Ankle

Wearing systems

Wrist Band

Environmental conditions


-20 °C - 60 °C

0 °C - 45 °C during charging

Atmospheric pressure:

300 to 1200 hPa absolute


2 years

Literature und Validation

  • Social Conditioning in Immersive Virtual Reality Elicits a Hypervigilant-Avoidant Response Pattern.
    S. Gado & M. Gamer (2024).
  • Predicting the onset of psychotic experiences in daily life with the use of ambulatory sensor data – A proof-of-concept study.
    F. Strakeljahn & T. Lincoln & K. Krkovic et al. (2024) in: Schizophrenia Research (267). Read more...
    M.J.P. Salazar & F.M.G. Nuño & J. A. S. Margallo et al. (2024) in: British Journal of Surgery (111). Read more...
  • Physiological synchrony in brain and body as a measure of attentional engagement.
    I.-I. Stuhldreher (2024).
  • Understanding the combined effects of sleep deprivation and acute social stress on cognitive performance using a comprehensive approach.
    C. Bottenheft & K. Hogenelst & I. Stuldreher et al. (2023) in: Brain, Behavior, & Immunity - Health (34). Read more...
  • Analysis of EDA and Heart Rate Signals for Emotional Stimuli Responses.
    H. Arabian & R. Schmid & V. Wagner-Hartl et al. (2023) in: Current Directions in Biomedical Engineering (9(1)). Read more...
  • Human uncertainty in interaction with a machine: establishing a reference dataset.
    A. Rother & G. Notni & A. Hasse et al. (2023). Read more...
  • The connection between stress, density, and speed in crowds.
    M. Beermann & A. Sieben (2023) in: scientific Reports (13). Read more...
  • Approaches, Applications and Challenges in Physiological Emotion Recognition—A Tutorial Overview.
    Y. Said Can & B. Mahesh & E. André (2023) in: IEEE. Read more...
  • Untersuchungen zur Integration objektiver Messgrößen in ein Virtual-Reality-Studiendesign zur Evaluation subjsubjekt Eindrücke von Fahrzeuginnenräumen.
    L. Steiert (2023).
  • Pedestrian Crowd Management Experiments: A Data Guidance Paper.
    A.K. Boomers & M. Boltes & J. Adrian et al. (2023) in: Collective Dynamics. Read more...
  • Physiological and neural synchrony in emotional and neutral stimulus processing: A study protocol.
    M. Hollandt & T. Kaiser & M. Mohrmann et al. (2023) in: Front. Psychiatry (14). Read more...
  • Robustness of Physiological Synchrony in Wearable Electrodermal Activity and Heart Rate as a Measure of Attentional Engagement to Movie Clips.
    I.-I. Stuhldreher & J. van Erp & A. Brouwer (2023) in: Sensors (23 (6)). Read more...
  • Tranquillity, transcendence, and retreat: the transformative practice of listening at Evensong.
    K. King (2023) in: Magdalen College, University of Oxford. Read more...
  • At Crossroads in a Virtual City: Effect of Spatial Disorientation on Gait Variability and Psychophysiological Response among Healthy Older Adults.
    C. O. Amaefule & S. Lüdtke & A. Klostermann et al. (2022) in: Gerontology. Read more...
  • The Effects of Stimulus Duration and Group Size on Wearable Physiological Synchrony.
    I.V. Stuldreher & J.B.F. van Erp & A.M. Brouwer (2022) in: Measuring Behavior 2022, Volume 2, 12th International Conference on Methods and Techniques in Behavioral Research, and 6th Seminar on Behavioral Methods (2). Read more...
  • Can Music-evoked autobiographical memories be triggered through music recommendation.
    K. Noordenbos & P. Yelkenci & J. Oliai et al. (2022) in: Good Good not Bad.
  • Waiting Behavior and Arousal in Different Levels of Crowd Density: A Psychological Experiment with a “Tiny Box”.
    M. Beermann & A. Sieben (2022) in: Journal of Advanced Transportation (7245301). Read more...
  • Current trends and opportunities in the methodology of electrodermal activity measurement.
    C. Tronstad & M. Amini & D. R. Bach et al. (2022) in: Institute of Physics and Engineering in Medicine. Read more...
  • Validation of wearables for electrodermal activity (EdaMove) and heart rate (Wahoo Tickr).
    A. Borovac & I. Stuldreher & N. Thammasan et al. (2021) in: Measuring Behavior 2020-21 (1). Read more...
  • Heart rate variability, postural sway and electrodermal activity in competitive golf putting.
    F. Scalise & D. Margonato & A. Frigerio et al. (2021) in: The Journal of Sports Medicine and physical fitness (July;61(7)). Read more...
  • How does it feel to walk in Berlin? Designing an Urban Sensing Lab to explore walking emotions through EDA sensing.
    W. Blum & P. Fried (2021).
  • Assessing the difficulty of annotating medical data in crowdworking with help of experiments.
    A. Rother & U. Niemann & T. Hielscher et al. (2021) in: PLOS ONE (16(7)). Read more...
  • Towards the Applicability of Measuring the Brain Activity in the Context of Process Model Comprehension.
    D. Waldow (2021).
  • Moments that matter? On the complexity of using triggers based on Skin Conductance to sample arousing events within an Experience Sampling Framework.
    S. van Halem & E. van Roekel & L. Kroencke et al. (2020).
  • Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios.
    B. Hoppenstedt & T. Probst & M. Reichert et al. (2020). Read more...
  • Social anxiety is associated with heart rate but not gaze behavior in a real social interaction.
    L. Rösler & S. Göhring & M. Strunz et al. (2020) in: Journal of Behavior Therapy and Experimental Psychiatry (70).
  • Measuring Behavior 2020-21.
    A. Spink & J. Barski & A.-M. Brouwer et al. (2020) in: 13-15 October 2021, Kraków, Poland. Read more...
  • Physiological synchrony in EEG, electrodermal activity and heart
    rate reflects shared selective auditory attention.
    I.-I. Stuhldreher & N. Thammasan & J.-B.-F. van Erp et al. (2020) in: Journal of Neural Engineering (17). Read more...
  • A Comparison between Laboratory and Wearable Sensors in the Context of Physiological Synchrony.
    J.-J. van Beers & Thammasan N. Stuhldreher I.-V. & A.-M. Brouwer (2020) in: ICMI '20: Proceedings of the 2020 International Conference on Multimodal Interaction. Read more...
  • A Usability Study of Physiological Measurement in School Using Wearable Sensors.
    N. Thammasan & I.V. Stuldreher & E. Schreuders et al. (2020) in: Sensors 2020 (20). Read more...
  • Using Virtual Reality Scenarios Along With Physiological Measures as Intervention Procedure in Patients With Dementia.
    J. Mata-Ferron & M. Roldán-Tapia & F. Nieto-Escamez (2020). Read more...
  • Applicability of Immersive Analytics in Mixed Reality: Usability Study.
    Burkhard Hoppenstedt (2019).
  • Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification.
    Anne Herlan & Jörg Ottenbacher & Johannes Schneider et al. (2018) in: Journal of sleep research (28). 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...
  • 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.
    K. Dorothee & K. Schaaff & J. 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).

You can find more publications here.