Wearable Mobility Monitoring
Wearable Mobility Monitoring - Related Videos
10:07
Using Fiberless, Wearable fNIRS to Monitor Brain Activity in Real-world Cognitive Tasks
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Cited by 162 •
2015
Monitoring brain activity outside the lab without physical constraints presents methodological challenges. A fiberless, wearable functional Near Infrared Spectroscopy (fNIRS) system was used to measure brain activity during an ecological prospective memory task. It was demonstrated that this system could be used to monitor brain activity during non-lab based experiments.
06:58
An Application for Pairing with Wearable Devices to Monitor Personal Health Status
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Cited by 3 •
2022
The present protocol introduces a non-commercial self-developed application for collecting real-time on-site data, including psychological scales, GPS location, heart rate, and blood-oxygen saturation level, as well as the application's operating procedures. An empirical study conducted in Taiwan in 2020 was used as an application example.
15:00
Setup of Consumer Wearable Devices for Exposure and Health Monitoring in Population Studies
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Cited by 6 •
2023
Commercial smartwatches equipped with wearable sensors are increasingly being used in population studies. However, their utility is often constrained by their limited battery duration, memory capacity, and data quality. This report provides examples of cost-effective solutions to real-life technical challenges encountered during studies involving asthmatic children and elderly cardiac patients.
10:45
A Community-based Stress Management Program: Using Wearable Devices to Assess Whole Body Physiological Responses in Non-laboratory Settings
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Cited by 1 •
2018
Stress is an unavoidable and persistent component of life and holistic approaches for its management are being considered. A standardized methodology was created to demonstrate the feasibility of a breath-based stress management protocol that can be used with commercially available portable technology.
06:57
Electrophoretic Mobility Shift Assay (EMSA)
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2023
The electrophoretic mobility shift assay (EMSA) is a biochemical procedure used to elucidate binding between proteins and nucleic acids. In this assay a radiolabeled nucleic acid and test protein are mixed. Binding is determined via gel electrophoresis which separates components based on mass, charge, and conformation. This video shows the concepts of EMSA and a general procedure, including gel and protein preparation, binding, electrophoresis, and detection. Applications covered in this video...
FAQs
Each sensor captures a different aspect of movement. Accelerometers detect changes in acceleration, gyroscopes measure orientation, and location systems provide information about speed and position. Combining these signals allows analyses to distinguish movement characteristics from broader mobility patterns, supporting measures that include walking, posture, physical activity, and movement through daily environments.
Algorithms analyze sensor signals and translate changes in acceleration, orientation, speed, and position into interpretable measures. These outputs can describe activities such as walking or posture, as well as broader patterns of mobility and physical activity. In behavioral research, the resulting measures make movement patterns available for systematic analysis rather than relying only on laboratory observation.
Daily-life measurement captures how movement occurs in ordinary routines rather than only under controlled laboratory conditions. This broader context can help characterize activity routines and social participation, while also revealing responses to environmental or health-related changes. Repeated monitoring further supports longitudinal assessment, allowing researchers to examine how mobility-related behavior changes over time.
A typical workflow begins by collecting movement and location signals with body-worn devices. The recorded data are then analyzed with algorithms that translate sensor changes into measures of walking, posture, physical activity, speed, position, and mobility patterns. Researchers can use these measures to characterize behavior in daily life and compare mobility-related outcomes across time or conditions.
Researchers can use this approach when they need objective information about movement during everyday life. It is relevant for characterizing activity routines, examining social participation, and assessing responses to environmental or health-related changes. Because monitoring can extend over time, it also supports longitudinal studies that track behavioral patterns beyond a single observation or laboratory session.
The resulting movement measures can inform personalized interventions by showing how an individual’s mobility patterns relate to daily behavior and changing conditions. The same measures provide an objective basis for evaluating mobility-related outcomes, including changes in walking, posture, physical activity, or broader mobility patterns. This connects behavioral observation with assessment of intervention-related change.