As part of the new sensor generation, the EcgMove 4 activity sensor combines the previous benefits of the proven EcgMove 3 with the 4th generation enhancements, allowing us to address a wide range of researchers' needs, implement their requests, and further enhance the quality of the sensors. Thus, the EcgMove 4 strengthens it's position as the go to device for researchers who care about high quality ECG and Activity data.


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 EcgMove 4's research capabilities. All the while remaining the best choice for researcher's requiring high quality ECG and physical activity data.

The EcgMove 4 follows on from the EcgMove 3 as the most precise sensor for the measurement of ECG combined with physical activity. Capable of long term ambulatory recordings without the inconvenience of cables. Through the parallel recording of the ECG and Activity Signals (ECG, 3D Accelerometer, Gyroscope, Barometric Air Pressure, and Temperature), the EcgMove 4 offers a rich treasure trove of data for the detailed analysis of the functioning of the heart, the autonomic nervous system, and additionally behaviour and activity. Whilst recording the raw data stream, the sensor can also analyze certain parameters live on the sensor and transmit the results via Bluetooth smart interface, e.g. to a smartphone.

The sensor is optimized for use in scientific studies and interactive ambulatory assessment. The new attachment systems offer you great flexibility in use and a high wearing comfort for the study participant, with the choice of adhesive electrodes or our acclaimed dry electrode textile chest belt. This leads to improved compliance, higher data volume and quality, and thus reduces the effort of carrying out a study and reduces costs.

Combined with our Analysis-Software DataAnalyzer allows the simple calculation of parameters such as Heart Frequency, Heart Rate Variability, Activity Class, Steps, Energy Expenditure and Metabolic Equivalent of Task (MET).



Top-Features

  • New design with new carrying systems in a waterproof housing
  • Advanced data acquisition through integrated gyroscope
  • New acceleration sensor with higher resolution
  • Attachment detection for automatically starting a measurement
  • Live analysis of measurement data
  • Improved data transfer via Bluetooth Smart interface
  • Combination of ECG- and Activity measurement in a single system
  • Exact and validated Energy Expenditure calculations and detection of everyday activities
  • Java API for USB (Windows)
  • API: Example implementation for Bluetooth Smart (Android)

Applications

  • Interactive Ambulatory Assessment
  • Mobile long term monitoring of Heart rate and Heart Rate Variability
  • Examination of the Autonomic Nervous System
  • Behavioural Monitoring
  • Psycho-physiological Stress-Monitoring
  • Energy Expenditure Calculation and Activity Recognition
  • Affective Computing
  • Integration in complex systems

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Software
Documentation and Support
External Tools

Technical Data

Power supply

Lithium-Polymer-Battery

Battery voltage

3,7 V

Number of charging cycles

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

Internal memory

4 GB

Maximum recording capacity

2 weeks

Battery run time

~ 3 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

ECG sensor:

Resolution: 12 bit, Input range CM = 560 mV, DM = +/-5 mV, 3db bandwidth 1,6 - 33 Hz

Output rate: 1024 Hz

 

3D acceleration sensor:

Measurement range: +/- 16 g

Output rate: 64 Hz

 

Rotation rate sensor:

Measurement range: +/-2000 dps

Output rate: 64 Hz

 

Pressure sensor:

Measurement range: 300 - 1100 hPa

Noise: 0,03 hPa

Output rate: 8 Hz

 

Temperature sensor:

Output rate: 1 Hz

Live analysis

Heart Rate

bpmBxB

NN-List

HRV Rmssd

HRV is valid

Movement Acceleration

Step count

Indicators

LED, 3-color

Vibration alarm

User Interfaces

Marker (tapping)

Interfaces

Micro-USB, Bluetooth Smart (4.0)

API

Java API for USB (Windows)

Example for Bluetooth Smart (Android)

Wear locations

Chest

Wearing systems

Chest Belt, adhesive Electrodes

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

Atmospheric pressure:

300 to 1200 hPa absolute

Warranty

2 years

Literature und Validation

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  • The heart and diving; an alternative way to monitor a diver's heart functioning underwater.
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  • Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.
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  • Integrating biosignals into information systems: A NeuroIS tool for improving emotion regulation.
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  • A personalized and reconfigurable cyberphysical system to handle multi-parametric data acquisition and analysis for mobile monitoring of epileptic patients.
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  • Study protocol: psychological and physiological consequences of exposure to mass media in young women-an experimental cross-sectional and longitudinal study and the role of moderators.
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  • Comparing Objective and Subjective Methods to Support Reflective Learning: an Experiment on the Influence on Affective Aspects.
    Verónica Rivera-Pelayo & Marc Kohaupt (2014). Read more...
  • Emotions and Emotion Regulation in Economic Decision Making.
    Philipp J. Astor (2013). Read more...
  • Komfortgewinn für Passagiere auf Langstreckenflügen durch den Einsatz chronobiologisch angepasster LED-Kabinenbeleuchtung.
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  • A Biofeedback Game for Training Arousal Regulation during a Stressful Task: The Space Investor.
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  • Measuring emotional arousal for online applications: Evaluation of ultra-short term heart rate variability measures.
    Kristina Schaaff & Marc T. P. Adam (2013) in: International Conference on Affective Computing and Intelligent Interaction (ACII).
  • Enhancing mobile working memory training by using affective feedback.
    K. Schaaff (2013) in: IADIS International Conference on Mobile Learning, 14-16 March 2013, Lisbon, Portugal, 2013, Conference Proceedings pp. 269-273..
  • Design and Evaluation of Affective Serious Games for Emotion Regulation Training.
    Petar Jercic (2013).
  • Mobile sensor systems for measurement of stress and physical activity in preventive healthcare applications.
    S. Hey (2012).
  • A Serious Game using Physiological Interfaces for Emotion regulation Training in the Context of Financial Decision-Making.
    P. Jercic & P. J. Astor & M. T. P. Adam et al. (2012) in: Presented at European Conference of Information Systems (ECIS 2012), Barcelona, Spain, 10-13 June 2012. Read more...
  • An approach to automotive ECG measurement validation using a car-integrated test framework.
    Johannes Schneider & Christian Koellner & Stephan Heuer (2012) in: Intelligent Vehicles Symposium (IV), 2012 IEEE. Read more...
  • 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...
  • User Study: Motivation and User Acceptance of Using Physiological Data to Support Individual Reflection.
    A. Fessl & V. Rivera-Pelayo & L. Müller et al. (2011).
  • From Stress Awareness to Coping Strategies of Medical Staff: Supporting Reflection on Physiological Data.
    Lars Müller & Veronica Rivera Pelayo & Christine Kunzmann et al. (2011) in: Second International Workshop on Human Behavior Understanding HBU 2011. Read more...
  • Einsatz sensorgestützter Verfahren im Gesundheitswesen: Herausforderungen und Lösungsansätze.
    D.I.D.S. Saboor & M.F.H.M. Schallhart (2011). Read more...
  • Motivation and User Acceptance of Using Physiological Data to Support Individual Reflection.
    A. Fessl & V. Rivera-Pelayo & L. Müller et al. (2011) in: 2nd International Workshop on Motivational and Affective Aspects in Technology Enhanced Learning (MATEL 11). Read more...
  • Sensor Chest Strap Wirelessly Coupled with an e-Diary for Ambulatory Assessment of Psycho-Physiological Data.
    Jürgen Stumpp & Panagiota Anastasopoulou & Hatem Sghir et al. (2011) in: Assessing Real-World Impact of Clinical Interventions and Outcomes.
  • 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).

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