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EDA and Activity Sensor

The best tool for measuring electrodermal and physical activity in everyday life!




EdaMove 4 - EDA and Activity Sensor


EdaMove 4 worn

It has never been so easy to capture electrodermal activity and movement data quickly and precisely in everyday life!


The EdaMove 4 combines excellent signal acquisition with unsurpassed convenience, a benefit for both participants and researchers alike. Begin your study assured in the quality of your data's fitness for publication.

High precision

Obtain precise data from our advanced device platform, capturing a multitude of signals at research level quality. Benefit from our tested and validated algorithms or conduct your own analysis from the raw data.


Whatever your approach, our sensors make data acquisition in every-day life easy and efficient. All the data you need, from when you need it.

Everyday life and real time

Analyse your participants in their every-day life with extensive measurement durations.


In addition, movisens offers the unique option of sensor coupling for all our sensors. Whilst recording the participants physiology, our sensors analyze physiological parameters and transmit the results via Bluetooth to a smartphone equipped with movisensXS. Algorithms within movisensXS process this data and can trigger a questionnaire for the participant when the set conditions occur. If you want to investigate physiological changes such as a high level of activity or skin conductance, such events can act as triggers for queries within the study.

Multimodal data analysis

Combine your activity data with experience sampling data.


The EdaMove 4 offers researchers superior flexibility as our analysis algorithms extract accurate activity parameters from a variety of wearing positions. This allows you to find the most convenient solution for your participants and your study. To get the most out of the raw data captured by the sensor, most researchers prefer to analyze it using the movisens DataAnalyzer's, AI-based, validated algorithms.

EdaMove 4
https://www.movisens.com/en/products/eda-and-activity-sensor/
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Documentation of the EdaMove 4

Benefit from our many years of experience in the field of ambulatory assessment and let us put together the right analysis solution for you

Software_DataAnalyzer

DataAnalyzer

Modular software for sensor data analysis and report generation

Smartphone mit movisensXS

movisensXS

The most comprehensive mobile research platform for Experience Sampling

Software DataMerger
DataMerger

Tool for the integration of data assessed with different methods

Our EdaMove 4 is used in the following areas




EDA_Monitoring

EDA Monitoring

  • EDA measurement in everyday life
  • EDA and activity measurements
  • EDA and heart rate variability measurements

  • Recommendations for EDA Monitoring

Interactive Ambulatory Assessment

  • EDA measurement and experience sampling
  • Combining different sensor parameters
  • Trigger algorithms

  • Recommendations for Interactive Ambulatory Assessment



young scientists

Look and Feel - The valid and reliable EDA measurement with movisens

Matching products and services

ECGMove 4
EcgMove 4

Sensor for the acquisition of ECG- and activity data

Move 4 worn
Move 4

Sensor for the acquisition of activity data


Accessories and Consumables

Accessories and Consumables for the movisens sensors

Schulungs-Icon
Consulting

Consulting and Training regarding technologies and methods for ambulatory assessment and mobile monitoring

Customizing-Icon
Customizing

Adaption and extension of movisens products to your requirements

Workshops-Icon
Webinars

We offer webinars on themes related to ambulatory assessment, physiological parameters, and experience sampling

Useful Information

Raw data and live data

Raw data

Also known as primary data, this is obtained directly during an observation, measurement or data collection and remains unprocessed. It represents the original information recorded by sensors. Raw data from acceleration, ECG, and EDA are usually displayed as graphs. Calculating output parameters such as HR, steps, and energy expenditure from these raw signals requires algorithms fit for purpose.
Advantage:
Raw data allows long term storage and the implementation of new analysis methods, e.g. for a new algorithm, new AI and it can also be fed into other software.
Disadvantage:
They contain a large amount of data and require more storage space.

Live data

In addition to offline calculation, the EdaMove 4 allows the live analysis of skin conductance and physical activity parameters in conjunction with movisensXS. In live mode, Movement Acceleration, Step Count, and Body Position can be measured, calculated and transmitted via the Bluetooth Smart interface at the rate of 1 value per minute.

Technical Data

Power supply

Lithium-Polymer-Battery

Battery voltage

3,7 V

Number of charging cycles

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

Internal memory

4 GB

Maximum recording capacity

4 Wochen

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

Indicators

LED, 3-color

Vibration alarm

User Interfaces

Marker (tapping)

Interfaces

Micro-USB, Bluetooth Smart (4.0)

API

Java API für USB (Windows)

Example for Bluetooth Smart (Android)

Wear locations

Wrist, Ankle

Wearing systems

Wrist Band

Environmental conditions

Temperature:

-20 °C - 60 °C

0 °C - 45 °C during charging

Atmospheric pressure:

300 to 1200 hPa absolute

Warranty

2 years

Literature and Validation

  • User Acceptance in Automated Vehicles: An Investigation of Electrodermal Activity Using Wearables.
    C. Stephanidis & M. Antona & S. Ntoa et al. (2025) (2525). Read more...
  • Monitoring audience engagement using electrodermal activity during an inaugural lecture.
    I. Stuldreher & A-M. Brouwer (2025) in: Plos One. Read more...
  • Predicting experiences of paranoia and auditory verbal hallucinations in daily life with ambulatory sensor data – A feasibility study.
    F. Strakeljahn & T. Lincoln & B. Schlier (2025) in: Psychological Medicine (55). Read more...
  • Effect of task nature during short digital deprivation on time perception and psychophysiological state.
    Q. Meteier & A. Délèze & S. Chappuis et al. (2025) in: Scientific Reports (15). Read more...
  • Virtual Reality-Based Approach to Evaluate Emotional Everyday Scenarios for a Digital Health Application.
    V. Wunsch & E.F. Picka & H. Schumm et al. (2024) in: Multimodal Technol. Interact. (8(12)). Read more...
  • The effect of social anxiety on social attention in naturalistic situations.
    S. Gado & J. Teigeler & K. Kümpel et al. (2024) in: Anxiety, Stress, & Coping.
  • A momentary approach to understanding subjective well-being: Daily motives, personality, and affect.
    S. van Halem (2024). Read more...
  • Evaluating Team Workload Through Physiological Synchrony: An Exploratory Study Using MdRQA Data to Assess Teams in Action.
    J. Braun & M. Hogh & S. Kubowitsch (2024) in: Proceedings of the 7th European Conference on Industrial Engineering and Operations Management
    Augsburg
    . Read more...
  • Applying artificial intelligence on EDA sensor data to predict stress on minimally invasive robotic-assisted surgery.
    D. Caballero & M.J. Pérez-Salazar & J.A. Sánchez-Margallo et al. (2024) in: Int J CARS. Read more...
  • The use of music for Solace, its connection to Openness and its moderating effects on music listening and stress.
    S. Gorgi (2024). Read more...
  • Studying the Influence of Single Social Interactions on Approach and Avoidance Behavior – A Multimodal Investigation in Immersive Virtual Reality.
    S. Gado & M. Gamer (2024). Read more...
  • Seven robust and easy to obtain biomarkers to measure acute stress.
    K. Hogenelst & S. Özsezen & R. Kleemann et al. (2024) in: Brain, Behavior, & Immunity - Health (38). Read more...
  • Social Conditioning in Immersive Virtual Reality Elicits a Hypervigilant-Avoidant Response Pattern.
    S. Gado & M. Gamer (2024). Read more...
  • 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...
  • 3. OBJECTIVE ANALYSIS AND COMPARISON OF STRESS LEVEL DURING ROBOTIC AND CONVENTIONAL LAPAROSCOPIC SURGERY.
    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). Read more...
  • 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). Read more...
  • 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). Read more...
  • 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). Read more...
  • 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).

All publications can be find here.

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