Reprint
Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders
Edited by
October 2020
274 pages
- ISBN978-3-03943-144-1 (Hardback)
- ISBN978-3-03943-145-8 (PDF)
This book is a reprint of the Special Issue Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary
The aging population and the increased prevalence of neurological diseases have raised the issue of gait and balance disorders as a major public concern worldwide. Indeed, gait and balance disorders are responsible for a high healthcare and economic burden on society, thus, requiring new solutions to prevent harmful consequences. Recently, wearable sensors have provided new challenges and opportunities to address this issue through innovative diagnostic and therapeutic strategies. Accordingly, the book “Wearable Sensors in the Evaluation of Gait and Balance in Neurological Disorders” collects the most up-to-date information about the objective evaluation of gait and balance disorders, by means of wearable biosensors, in patients with various types of neurological diseases, including Parkinson’s disease, multiple sclerosis, stroke, traumatic brain injury, and cerebellar ataxia. By adopting wearable technologies, the sixteen original research articles and reviews included in this book offer an updated overview of the most recent approaches for the objective evaluation of gait and balance disorders.
Format
- Hardback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
inertial measurement units; gait analysis; biomedical signal processing; pattern recognition; step detection; physiological signals; Parkinson’s disease; pathological gait; turning analysis; wearable sensors; mobile gait analysis; wearables; inertial sensors; traumatic brain injury; dynamic balance; gait disorders; gait patterns; head injury; gait symmetry; gait smoothness; acceleration; Parkinson’s disease; machine learning; classification; wearables; accelerometer; GAITRite; multi-regression normalization; SVM; random forest classifier; balance; gait; Parkinson’s disease; transcranial direct current stimulation; wearable electronics; IMUs; Parkinson’s disease; cueing; gait; posture; rehabilitation; wearable sensors; inertial sensors; cerebellar ataxia; movement analysis; gait analysis; balance; personalized medicine; rehabilitation; stroke; asymmetry; accelerometer; gait; trunk; reliability; validity; aging; reactive postural responses; yaw perturbation; kinematics; postural stability; dynamic posturography; multiple sclerosis; gait metrics; wearable sensors; test-retest reliability; sampling frequency; accelerometry; autocorrelation; harmonic ratio; six-minute walk; back school; inertial sensor; lower back pain; rehabilitation; stability; timed up and go test; gait assessment; tri-axial accelerometer; CV; healthy subjects; test-retest; trajectory reconstruction; stride segmentation; dynamic time warping; pedestrian dead-reckoning; near falls; loss of balance; pre-impact fall detection; activities of daily life; bio-signals; EEG; EMG; wireless sensors; wearables; balance; posturography; Alzheimer’s disease; Parkinson’s disease; multiple sclerosis; cerebellar ataxia; stroke; vestibular syndrome; Parkinson’s disease; gait analysis; diagnosis; symptoms monitoring; wearable; home-monitoring; machine learning