Light and Activity Sensor

The LightMove 3 sensor can be used to determine the physical activity of a user and also detect the ambient light of their surroundings. The combination of these two parameters allows information to be calculated about the behaviour, activity, and the environment of a subject in just a few small steps. The collected data can be processed on-board the sensor in real time and then transmitted to a mobile phone via a Bluetooth Smart connection.

In addition to the on board processing, the raw data is recorded for a measurement period of up to 2 months. This data, including the 3D acceleration, barometric pressure, temperature, and the parameters of ambient light can then be further processed as required. Wearing the sensor on a wristband offers a high degree of comfort and unrestricted range of motion, so that the subject can pursue their everyday life without hindrance.

Output parameters such as illumination intensity (lux), brightness (from darkness to sunlight), temperature, colour, activity classes, steps, energy conversion and metabolic equivalents (MET) can be easily calculated by using our DataAnalyzer software. This software allows complex processing of the data and the generation of meaningful reports (PDF) in just a few simple steps. These output parameters can allow a thorough evaluation of the environment of the subject, determining such factors as time indoors vs. outdoors and what level of physical activity the subject has performed.





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Top features

  • Live analysis of data on the sensor
  • Bluetooth-Smart Interface
  • Exact and validated recognition of activities
  • Exact and validated energy conversion
  • Detection of ambient light
  • Analysis of radiation intensity
  • Detection of usage vs. non-wear time
  • Barometric sensor enabling more precise evaluations
  • Sustainable data format
  • Practical and easy to use in studies
  • No restriction of the subjects in everyday activities
  • Java API for USB (Windows)
  • API: Example for Bluetooth Smart (Android)

Applications

  • Interactive ambulatory assessment
  • Sleep analysis
  • Behavioural monitoring
  • Mobile long term monitoring of physical activity
  • Mobile long term monitoring of ambient light
  • Energy estimation and activity detection
  • Step detection
  • Detection of inactivity, sitting, and standing
  • Capable of integration into complex systems

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

Smartphone mit movisensXS

movisensXS
Smartphone-based e-Diary with experience sampling

Downloads

Software
Documentation
Data examples
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)

~ 9 days

Size of sensor (W x H x D)

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

Weight of sensor

26 g

Internal sensors

Ambient light sensor:

Channels: 5 (red, green, blue, clear, ir)

Measurement range: 0 - ~45 000 lux

Resolution: Up to ~0.011 lux (at low end)

Output rate: 1 Hz

 

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 (Bluetooth Smart is disconnected)

Interfaces

Micro-USB, Bluetooth Smart

Environmental conditions

Temperature:

-20 °C to 60 °C

0 °C to 45 °C during charging

 

Humidity:

0 to 75% relative humidity

 

Atmospheric pressure:

300 to 1100 hPa absolute

Literature and Validation

  • 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...
  • 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 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.
  • 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...
  • 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...
  • 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...
  • 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...
  • 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...
  • 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...
  • Contributions à l’élaboration d’un système d’aide médico-sociale à l’aide d’un robot humanoïde.
    Louise Devigne (2015). 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...
  • 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).
  • 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).
  • 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...
  • 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...
  • 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.
  • 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).
  • Situationsadaptive Navigationsassistenz für Menschen mit Demenz.
    Philipp Koldrack & Ron Henkel & Katja Zarm et al. (2015) in: AAL-Kongress 2015. Read more...
  • Aktuelle Messverfahren zur objektiven Erfassung körperlicher Aktivitäten unter besonderer Berücksichtigung der Schrittzahlmessung.
    D. Rosenbaum (2012) in: Bundesgesundheitsblatt - Gesundheitsforschung - Gesundheitsschutz (55). Read more...
  • 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..
  • 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..
  • 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...
  • 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...
  • 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...
  • 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...
  • Fitness, kognitive Leistungsfähigkeit und Wohlbefinden bei jungen Erwachsenen - Interventionsstudien zum Einfluss von Ausdauertraining.
    Katrin Walter (2015). Read more...
  • 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...

You can find more publications here.