Reprint

Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures

Edited by
February 2022
204 pages
  • ISBN978-3-0365-2812-0 (Hardback)
  • ISBN978-3-0365-2813-7 (PDF)

This book is a reprint of the Special Issue Wearable and BAN Sensors for Physical Rehabilitation and eHealth Architectures that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

The demographic shift of the population towards an increase in the number of elderly citizens, together with the sedentary lifestyle we are adopting, is reflected in the increasingly debilitated physical health of the population. The resulting physical impairments require rehabilitation therapies which may be assisted by the use of wearable sensors or body area network sensors (BANs). The use of novel technology for medical therapies can also contribute to reducing the costs in healthcare systems and decrease patient overflow in medical centers. Sensors are the primary enablers of any wearable medical device, with a central role in eHealth architectures. The accuracy of the acquired data depends on the sensors; hence, when considering wearable and BAN sensing integration, they must be proven to be accurate and reliable solutions. This book is a collection of works focusing on the current state-of-the-art of BANs and wearable sensing devices for physical rehabilitation of impaired or debilitated citizens. The manuscripts that compose this book report on the advances in the research related to different sensing technologies (optical or electronic) and body area network sensors (BANs), their design and implementation, advanced signal processing techniques, and the application of these technologies in areas such as physical rehabilitation, robotics, medical diagnostics, and therapy.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
fog computing; cloud computing; e-health; healthcare; Internet of Things; paddle stroke analysis; motion reconstruction; inertial sensor; data fusion; body sensor network; gait analysis; gyroscope; information fusion; hidden Markov model; human activity recognition; out of distribution; anomaly detection; open set classification; physiotherapy; inertial sensors; smart watch; rehabilitation; machine learning; COPD; wearable sensors; SenseWear Armband; physical activity; weekday-to-weekend; energy expenditure; stress; wearable device; machine learning; smart watch; heart rate variability; electrocardiogram; scapula neuromuscular activity and control; rotator cuff related pain syndrome; anterior shoulder instability; scapular dyskinesis; electromyographic biofeedback; cardio-respiratory monitoring; wearable system; wearable device; smart textile; IMU; respiratory rate; heart rate; accelerometers; Bland–Altman plots; gait speed; interclass correlation coefficient; low frequency extension filter; Stepwatch; smart walker; obstacle detection; aging; rehabilitation; n/a