Move 4 – Activity Sensor
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
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Technical Data
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Power supply |
Lithium-Polymer-Battery |
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Battery voltage |
3,7 V |
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Number of charging cycles |
300 (with 1 C / 1 C > 80%) |
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Internal memory |
4 GB |
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Maximum recording capacity |
4 weeks |
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Battery run time |
~ 7 days |
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Recharging time |
~ 1 hour |
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Size of sensor (W x H x D) |
62,3 mm x 38,6 mm x 11,5 mm |
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Weight of sensor |
25 g |
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Protection rate |
Waterproof (IP64) |
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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 |
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Live analysis |
Movement Acceleration Step count |
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Indicators |
LED, 3-color Vibration alarm |
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User Interfaces |
Marker (tapping) |
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Interfaces |
Micro-USB, Bluetooth Smart (4.0) |
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API |
Java API for USB (Windows) Example for Bluetooth Smart (Android) |
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Wear locations |
Hip, Chest, Wrist, Upper Arm, Ankle, Thigh |
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Wearing systems |
Wrist Band, Belt Adapter and Belt |
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Environmental conditions |
Temperature: -20 °C to 60 °C 0 °C to 45 °C during charging Atmospheric pressure: 300 to 1200 hPa absolute |
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Warranty |
2 years |
Literature and Validation
- Actigraph-Measured Movement Correlates of Attention-Deficit/Hyperactivity Disorder (ADHD) Symptoms in Young People with Tuberous Sclerosis Complex (TSC) with and without Intellectual Disability and Autism Spectrum Disorder (ASD)..
- Accuracy of Sedentary Behavior–Triggered Ecological Momentary Assessment for Collecting Contextual Information: Development and Feasibility Study.
- Mood and dysfunctional cognitions constitute within-subject antecedents and consequences of exercise in eating disorders..
- OREBA: A Dataset for Objectively Recognizing Eating Behaviour and Associated Intake..
- Improving mobility and participation of older people with vertigo, dizziness and balance disorders in primary care using a care pathway: feasibility study and process evaluation.
- Fear of Physical Activity, Anxiety, and Depression. Barriers to Physical Activity in Outpatients With Heart Failure?.
- The Freiburg sport therapy program for eating disorders: a randomized controlled trial..
- Using Acceleration Data for Detecting Temporary Cognitive Overload in Health Care Exemplified Shown in a Pill Sorting Task.
- Sedentary behavior in everyday life relates negatively to mood: An Ambulatory Assessment study.
- Promotion of physical activity-related health competence in physical education: study protocol for the GEKOS cluster randomized controlled trial.
- Dynamics of Intraindividual Variability in Everyday Life Affect Across
Adulthood and Old Age. - Real-Time Detection of Spatial Disorientation in Persons with Mild Cognitive Impairment and Dementia.
- Neural correlates of individual differences in affective benefit of real-life urban green space exposure.
- Energy Expenditure During Incline Walking – Benefits of Integrating a Barometer into Activity Monitors.
- Individual Differences in the Competence for Physical-Activity-Related Affect Regulation Moderate the Activity–Affect Association in Real-Life Situations.
- Embodied learning in the classroom: Effects on primary school children's attention and foreign language vocabulary learning.
- Intermittent Fasting (Alternate Day Fasting) in Healthy, Non-obese Adults: Protocol for a Cohort Trial with an Embedded Randomized Controlled Pilot Trial.
- A novel algorithm for detecting human circadian rhythms using a thoracic temperature sensor Article history :.
- Physical Activity and Depressive Mood in the Daily Life of Older Adults.
- Measuring Fear of Physical Activity in Patients with Heart Failure.
- Lightweight Visual Data Analysis on Mobile Devices - Providing Self-Monitoring Feedback.
- Contributions à l’élaboration d’un système d’aide médico-sociale à l’aide d’un robot humanoïde.
- Situationsadaptive Navigationsassistenz für Menschen mit Demenz.
- Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients.
- Bewegungsangst bei chronischer Herzinsuffizienz – Erste Ergebnisse zur Validierung eines Messinstruments..
- Fitness, kognitive Leistungsfähigkeit und Wohlbefinden bei jungen Erwachsenen - Interventionsstudien zum Einfluss von Ausdauertraining.
- Validation and comparison of two methods to assess human energy expenditure during free-living activities.
- Erfassung körperlicher Aktivität mittels Akzelerometrie - Möglichkeiten und Grenzen aus technischer Sicht.
- Home-based system for physical activity monitoring in patients with multiple sclerosis (Pilot study)..
- Detection of Parameters to Quantify Neurobehavioral Alteration in Multiple Sclerosis Based on Daily Life Physical Activity and Gait Using Ambulatory Assessment.
- Using Support Vector Regression for Assessing Human Energy Expenditure Using a Triaxial Accelerometer and a Barometer.
- A Comparison of Two Commercial Activity Monitors for Measuring Step Counts During Different Everyday Life Walking Activities.
- The Association between Short Periods of Everyday Life Activities and Affective States: A Replication Study Using Ambulatory Assessment.
- Characteristics of the activity-affect association in inactive people: an ambulatory assessment study in daily life.
- Acute and medium term effects of a 10-week running intervention on mood state in apprentices.
- Classification of Human Physical Activity and Energy Expenditure Estimation by Accelerometry and Barometry.
- Measurement of daily mobility under fampridine-therapy with Movisens-system in patients with multiple sclerosis.
- Assessment of Human Gait Speed and Energy Expenditure Using a Single Triaxial Accelerometer.
- Aktuelle Messverfahren zur objektiven Erfassung körperlicher Aktivitäten unter besonderer Berücksichtigung der Schrittzahlmessung.
- Kindergarten in Bewegung. Zur Qualität von Bewegungskindergärten..
- Assessment der Mobilität im Alltag zur Unterstützung von MS-Patienten.
- A new method to estimate energy expenditure using accelerometry and barometry-based energy models.
- Estimation of energy expenditure using accelerometers and activity-based energy models - validation of a new device.
- Trends und Möglichkeiten zur Erfassung körperlicher Aktivität im Alltag.
- Einsatz sensorgestützter Verfahren im Gesundheitswesen: Herausforderungen und Lösungsansätze.
- Bewegungskindergärten: empirische Befunde und praktisches Wissen.
- Energieumsatzmessung mit Aktivitätssensoren – Validität des kmsMove-Akzelerometers.
- Validity of the kmsMove-sensor in calculating energy expenditure during different walking intensities.
You can find more publications here.