Wearable Sensors Applied in Movement Analysis

Edited by
November 2022
154 pages
  • ISBN978-3-0365-5860-8 (Hardback)
  • ISBN978-3-0365-5859-2 (PDF)

This book is a reprint of the Special Issue Wearable Sensors Applied in Movement Analysis that was published in

Chemistry & Materials Science
Environmental & Earth Sciences

Recent advances in electronics have led to sensors whose sizes and weights are such that they can be placed on living systems without impairing their natural motion and habits. They may be worn on the body as accessories or as part of the clothing and enable personalized mobile information processing. Wearable sensors open the way for a nonintrusive and continuous monitoring of body orientation, movements, and various physiological parameters during motor activities in real-life settings. Thus, they may become crucial tools not only for researchers, but also for clinicians, as they have the potential to improve diagnosis, better monitor disease development and thereby individualize treatment. Wearable sensors should obviously go unnoticed for the people wearing them and be intuitive in their installation. They should come with wireless connectivity and low-power consumption. Moreover, the electronics system should be self-calibrating and deliver correct information that is easy to interpret. Cross-platform interfaces that provide secure data storage and easy data analysis and visualization are needed.This book contains a selection of research papers presenting new results addressing the above challenges.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
inertial measurement unit; movement analysis; long-track speed skating; validity; IMU; principal component analysis; wearable; scoring; carving; balance assessment; data augmentation; gated recurrent unit; human activity recognition; inertial measurement unit; one-dimensional convolutional neural network; intermittent claudication; vascular rehabilitation; 6 min walking test; functional walking; TUG; kinematics; fall risk; logistic regression; elderly; inertial sensor; artificial intelligence; supervised machine learning; kinematics; head rotation test; neck pain; cerebral palsy; dystonia; choreoathetosis; machine learning; home-based; inertial measurement unit; wearable device; MLP; gesture recognition; flex sensor; model search; neural network; artificial intelligence; machine learning; inertial measurement unit—IMU; movement complexity; sample entropy; trunk flexion; low back pain; lifting technique; camera system; ward clustering method; K-means clustering method; ensemble clustering method; Bayesian neural network; pain self-efficacy questionnaire; n/a

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