Activity Sensor

The Move 3 is the most precise mobile sensor for the acquisition of physical activity available today. Now equipped with a Bluetooth Smart interface, the sensor can transmit a live analysis of data calculated on board the sensor. It is designed and optimized for research applications and for interactive ambulatory assessment.

The sensor records the raw data of 3D acceleration, barometric air pressure and temperature for up to 2 months.

From this data secondary parameters like activity class, body position, steps, energy expenditure, metabolic equivalents (MET) and additional reports (pdf) can all be easily calculated with the movisens DataAnalyzer software.

The sensor can be fixed with a clip at the hip (Move 3 Clip, Art.Nr. 10111) as well as with a band at the wrist (Move 3 Wrist, Art.Nr. 10112).





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Top features

  • Live analysis of data on the sensor
  • Bluetooth Smart interface
  • Exact and validated recognition of everyday activities
  • Exact and validated energy expenditure calculation
  • Better results due to barometric altitude sensor
  • Sustainable data due to open file format
  • Operation optimized for studies
  • Java API for USB (Windows)
  • API: Example for Bluetooth Smart (Android)

Applications

  • Interactive Ambulatory Assessment
  • Behavioral monitoring
  • Energy expenditure calculation
  • Activity recognition
  • Step detection
  • Acquisition of body positions
  • Recognition of inactivity, sitting and standing


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

movisensXS
Smartphone-based e-Diary with experience sampling

Downloads

Software
Documentation
Example data
Example reports
External Tools

Technical data

Power supply

Lithium-Polymer-Battery

Supply voltage

3 V

Battery voltage

3,0 - 4,2 V

Number of charging cycles

300 with 1C/1C > 80%

Maximum recording capacity

~2 months

Battery run time (recording)

~7 days

Size of sensor

(W x H x D)

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

Weight of sensor

25 g

Internal sensors

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

Indicators

LED, 3-color

(operation and charging status)

Vibration alarm

(start and end of measurement)

Interfaces

Micro-USB, Bluetooth Smart

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

Humidity:

0 to 75% RH relative humidity

Atmospheric pressure:

300 to 1100 hPa absolute

Literature and validation

  • 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.