Measuring Physical Activity and Energy Expenditure

Activity trackers can potentially stimulate users to increase their physical activity behavior. The aim of this study was to examine the reliability and validity of ten consumer activity trackers for measuring step count in both laboratory and free-living conditions. Healthy adult volunteers (n = 33) walked twice on a treadmill (4.8 km/h) for 30 min while wearing ten different activity trackers (i.e. Lumoback, Fitbit Flex, Jawbone Up, Nike+ Fuelband SE, Misfit Shine, Withings Pulse, Fitbit Zip, Omron HJ-203, Yamax Digiwalker SW-200 and Moves mobile application). In free-living conditions, 56 volunteers wore the same activity trackers for one working day. Test-retest reliability was analyzed with the Intraclass Correlation Coefficient (ICC). Validity was evaluated by comparing each tracker with the gold standard (Optogait system for laboratory and ActivPAL for free-living conditions), using paired samples t-tests, mean absolute percentage errors, correlations and Bland-Altman plots. Test-retest analysis revealed high reliability for most trackers except for the Omron (ICC .14), Moves app (ICC .37) and Nike+ Fuelband (ICC .53). The mean absolute percentage errors of the trackers in laboratory and free-living conditions respectively, were: Lumoback (−0.2, −0.4), Fibit Flex (−5.7, 3.7), Jawbone Up (−1.0, 1.4), Nike+ Fuelband (−18, −24), Misfit Shine (0.2, 1.1), Withings Pulse (−0.5, −7.9), Fitbit Zip (−0.3, 1.2), Omron (2.5, −0.4), Digiwalker (−1.2, −5.9), and Moves app (9.6, −37.6). Bland-Altman plots demonstrated that the limits of agreement varied from 46 steps (Fitbit Zip) to 2422 steps (Nike+ Fuelband) in the laboratory condition, and 866 steps (Fitbit Zip) to 5150 steps (Moves app) in the free-living condition. The reliability and validity of most trackers for measuring step count is good. The Fitbit Zip is the most valid whereas the reliability and validity of the Nike+ Fuelband is low.

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Reliability and validity of ten consumer activity trackers

Activity detection and classification are very important for autonomous monitoring of humans for applications, including assistive living, rehabilitation, and surveillance. Wearable sensors have found wide-spread use in recent years due to their ever-decreasing cost, ease of deployment and use, and ability to provide continuous monitoring as opposed to sensors installed at fixed locations. Since many smart phones are now equipped with a variety of sensors, such as accelerometer, gyroscope, and camera, it has become more feasible to develop activity monitoring algorithms employing one or more of these sensors with increased accessibility. We provide a complete and comprehensive survey on activity classification with wearable sensors, covering a variety of sensing modalities, including accelerometer, gyroscope, pressure sensors, and camera- and depth-based systems. We discuss differences in activity types tackled by this breadth of sensing modalities. For example, accelerometer, gyroscope, and magnetometer systems have a history of addressing whole body motion or global type activities, whereas camera systems provide the context necessary to classify local interactions, or interactions of individuals with objects. We also found that these single sensing modalities laid the foundation for hybrid works that tackle a mix of global and local interaction-type activities. In addition to the type of sensors and type of activities classified, we provide details on each wearable system that include on-body sensor location, employed learning approach, and extent of experimental setup. We further discuss where the processing is performed, i.e., local versus remote processing, for different systems. This is one of the first surveys to provide such breadth of coverage across different wearable sensor systems for activity classification.

A Survey on Activity Detection and Classification Using Wearable Sensors

Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems.

Please cite as:
Mercer K, Li M, Giangregorio L, Burns C, Grindrod K
Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis
JMIR Mhealth Uhealth 2016;4(2):e40
DOI: 10.2196/mhealth.4461
PMID: 27122452
PMCID: 4917727

Behavior Change Techniques Present in Wearable Activity Trackers: A Critical Analysis.

Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection.

Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities.

The purpose of this study was to validate a commercially available inertial measurement unit (IMU) system against a standard lab-based motion capture system for the measurement of shoulder elevation, elbow flexion, trunk flexion/extension, and neck flexion/extension kinematics. The validation analyses were applied to 6 surgical faculty members performing a standard, simulated surgical training task that mimics minimally invasive surgery. Three-dimensional joint kinematics were simultaneously recorded by an optical motion capture system and an IMU system with 6 sensors placed on the head, chest, and bilateral upper and lower arms. The sensor-to-segment axes alignment was accomplished manually. The IMU neck and trunk IMU flexion/extension angles were accurate to within 2.9 ± 0.9 degrees and 1.6 ± 1.1°, respectively. The IMU shoulder elevation measure was accurate to within 6.8 ± 2.7° and the elbow flexion measure was accurate to within 8.2 ± 2.8°. In the Bland-Altman analyses, there were no significant syst...

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Validation of Inertial Measurement Units for Upper Body Kinematics

A progressive reduction in muscle fiber conduction velocity is typically observed during fatiguing muscle contraction Although the exact causes of the conduction velocity decrease have not yet been fully established, increasing evidence suggests that changes in extracellular potassium concentration may be largely responsible In this study, a mathematical model was developed to examine the effect of extracellular potassium concentration on the muscle fiber action potential and conduction velocity The model was used to simulate changes in extracellular potassium concentration at a range of temperatures and extracellular potassium accumulation during repetitive stimulation of the muscle fiber at 37 degrees C The action potential broadened, and its amplitude and conduction velocity decreased as extracellular potassium concentration increased The potassium-induced changes in action potential shape and conduction velocity were eliminated when the inward rectifier channels were removed from the model The results support the hypothesis that accumulation of extracellular potassium ions may be a major contributor to the reduction in muscle fiber conduction velocity and loss of membrane excitability during fatiguing contractions They additionally suggest that inward rectifier currents play a critical role in potassium-induced membrane depolarization, leading to increased sodium inactivation and resulting in the observed reduction in conduction velocity and membrane excitability

Effect of Extracellular Potassium Accumulation on Muscle Fiber Conduction Velocity: A Simulation Study

Patient compliance is important when assessing movement, particularly in a free-living environment when patients are asked to don their own accelerometers. Reducing the number of accelerometers could increase patient compliance. The aims of this study were (1) to determine and compare the validity of different accelerometer combinations and placements for a previously developed posture and dynamic movement identification algorithm. Custom-built activity monitors, each containing one tri-axial accelerometer, were placed on the ankles, right thigh, and waist of 12 healthy adults. Subjects performed a protocol in the laboratory including static orientations of standing, sitting, and lying down, and dynamic movements of walking, jogging, transitions between postures, and fidgeting to simulate free-living activity. When only one accelerometer was used, the thigh was found to be the optimal placement to identify both movement and static postures, with a misclassification error of 10%, and demonstrated the greatest accuracy for walking/fidgeting and jogging classification with sensitivities and positive predictive value (PPVs) greater than 93%. When two accelerometers were used, the waist-thigh accelerometers identified movement and static postures with greater accuracy than the thigh-ankle accelerometers (with a misclassification error of 11% compared to 17%). However, the thigh-ankle accelerometers demonstrated the greatest accuracy for walking/ fidgeting and jogging classification with sensitivities and PPVs greater than 93%. Movement can be accurately classified in healthy adults using tri-axial accelerometers placed on one or two of the following sites: waist, thigh, or ankle. Posture and transitions require an accelerometer placed on the waist and an accelerometer placed on the thigh.

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Emma Fortune

Papers

Validity of using tri-axial accelerometers to measure human movement - Part I: Posture and movement detection.

Validity of using tri-axial accelerometers to measure human movement - Part II: Step counts at a wide range of gait velocities.

Validation of Inertial Measurement Units for Upper Body Kinematics

Effect of Extracellular Potassium Accumulation on Muscle Fiber Conduction Velocity: A Simulation Study

Posture and Movement Classification: The Comparison of Tri-Axial Accelerometer Numbers and Anatomical Placement