As part of the new sensor generation, the Move 4 activity sensor combines the previous benefits of the proven Move 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 Move 4 strengthens its position as the go to device for researchers who care about high quality 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 Move 4's research capabilities. All the while remaining the best choice for researcher's requiring high quality physical activity data.

The Move 4 follows on from the Move 3 as the most precise sensor for the measurement of physical activity. Performing complex calculations and making a detailed analysis of movement and behaviour parameters has never been simpler. Through the continuous recording of up to 4 weeks of data (3D Accelerometer, Gyroscope, Barometric Air Pressure, and Temperature), the Move 4 offers a rich treasure trove of data for further analysis.

Combined with our Analysis-Software DataAnalyzer allows the simple calculation of parameters such as Activity Class, Body Position, Steps, Energy Expenditure and Metabolic Equivalent of Task (MET) in a selectable output interval.

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 carrying systems offer you great flexibility in use and high wearing comfort for the study participant. This leads to improved compliance, higher data quality and thus reduced effort in carrying out a study and lower costs.



Top-Features

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

Applications

  • Interactive Ambulatory Assessment
  • Detection of Sedentary Behaviour
  • Activity Monitoring
  • Behavioural Monitoring
  • Energy Expenditure Calculations
  • Step Recognition
  • Detection of Body Position


Matching products and services

Downloads

Software
Documentation
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

4 weeks

Battery run time

~ 7 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

25 g

Protection rate

Waterproof (IP64)

Internal sensors

3D acceleration sensor:

Measurement range: +/- 16 g

Output rate: 64 Hz

 

Rotation rate sensor:

Measurement range: +/-2000dps

Resolution: 70 mdps

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

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

Hip, Chest, Wrist, Upper Arm, Ankle, Thigh

Wearing systems

Wrist Band, Belt Adapter and Belt

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

Atmospheric pressure:

300 to 1200 hPa absolute

Warranty

1 year

Literature and Validation

  • Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors.
    Manuel Armbruster & Panagiota Anastasopoulou & Stefan Altmann et al. (2018) in: American Journal of Sports Science (6). Read more...
  • A novel algorithm for detecting human circadian rhythms using a thoracic temperature sensor Article history :.
    Aly Chkeir & Farah Mourad-chehade & Jacques Beau et al. (2017) in: Advances in Science, Technology and Engineering Systems Journal (2). Read more...
  • Physical Activity and Depressive Mood in the Daily Life of Older Adults.
    Andrea E. Gruenenfelder-Steiger & Marko Katana & Annika A. Martin et al. (2017) in: GeroPsych (30). Read more...
  • Measuring Fear of Physical Activity in Patients with Heart Failure.
    Jeremia M. Hoffmann & Susan Hellwig & Vincent M. Brandenburg et al. (2017) in: International Journal of Behavioral Medicine. Read more...
  • 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).

You can find more publications here.