Reprint

Wearable Sensors & Gait

Edited by
August 2023
254 pages
  • ISBN978-3-0365-8642-7 (Hardback)
  • ISBN978-3-0365-8643-4 (PDF)

This is a Reprint of the Special Issue Wearable Sensors & Gait that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

Gait analysis has been traditionally conducted in laboratory settings and, therefore, has required specific conditions and expensive equipment. The emergence of wearable sensors has solved the lack of ecology for these measurements and offers a more economical and easier-to-use option to perform gait analysis. Lately, such sensors have allowed the quantification of performance and workload by providing mechanical and physiological parameters, and their popularity has grown exponentially. In this context, more and more wearable sensors are commercially available and, when applied to gait analysis (either walking or running), these devices are able to provide both kinetic and kinematic variables, consequently improving the feasibility and testing time of such assessments and, therefore, becoming a real alternative for clinicians, researchers, and sport practitioners. The incremental growth in big data, cloud computing, and artificial intelligence makes these sensors suitable to connect gait biomechanics with real-life and real-time analysis. All these benefits broaden the possibilities, among others, to provide real-time biofeedback while walking and running, or to integrate sensors with cloud platforms or mobile apps to improve health and/or performance. This Special Issue collected research and contributions on the use and application of wearable sensors for gait assessment and analysis.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
running; kinematics; gait retraining; running; kinematics; surface electromyography; wearables; decision tree; human running; random forest; regression; wearable devices; aerobic; assessment; performance; physiology; technology; training; wearable; diabetic foot; gait; monitoring foot temperature; smart wearable; VO2max; power; running biomechanics; hierarchical cluster analysis; machine learning; triathletes; cycling; mobile power meter; testing; load monitoring; gait analysis; gait parameters; IMU; inertial sensors; orientation-invariant; sensor fusion; team-sports; performance; muscle activation; loaded sprint; sled-push; portable gait rhythmogram; 3-D gait analysis; music therapy; Parkinson’s disease; gait disturbance; data histogram; distance estimation; gait speed; on-ankle path loss; RSSI; step length estimation; strike length estimation; two-term Gaussian distribution; biomechanics; wearable devices; injury; running conditions; gait; gait cycle; ground reaction force; inertial measurement unit; principal component analysis; stroke; synergy; wearable device; malleolar fractures; inertial sensor unit; wearable sensor; walking; spatiotemporal parameters; gait analysis; functional scales; clinical measurement; agreement of measurements; analysis; biomechanics; gait; markerless; testing