Reprint

Human Activity Recognition (HAR) in Healthcare

Edited by
March 2024
228 pages
  • ISBN978-3-0365-9778-2 (Hardback)
  • ISBN978-3-0365-9779-9 (PDF)

This book is a reprint of the Special Issue Human Activity Recognition (HAR) in Healthcare that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Human activity recognition (HAR) is determined through the acquisition of images or kinematic states, which are analyzed by prediction models. The goal of these systems is to correctly classify the data into one of the following categories: gestures, elementary actions that are components of complex activities, human–object or human–human interactions, and group actions.

HAR plays a fundamental role in health monitoring systems through the acquisition of information about the health and psychological statuses of subjects. The development of new medical and assistive technologies has created environmental conditions for the independent living among the elderly and has contributed to the improvement of their quality of life. In this Special Issue, we present the constant development of new data, applications and analysis techniques.

Format
  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
diet records; eHealth; body mass index; internet of things; social media; HAR; face emotion recognition; face detection; computer vision; deep learning; SNN; ensemble; vectorflow; device displacement; acceleration; wearable devices; data augmentation; patient monitoring; human activity recognition; joint-angle estimation; human motion analysis; deep learning; inertial sensors; human activity recognition (HAR); 3-axial sensor; image type encoding method; WISDM dataset; CNN; adherence; cardiac rehabilitation; machine learning; prediction; exercise; home-based; familiarization phase; telemonitoring; metaverse; virtual reality; animation; digital games; gaming; qualitative research; human activity recognition; WiFi sensing; deep learning; attention mechanism; channel state information; multi-scale analysis; attention mechanism; feature fusion; human action recognition; behaviour analysis; domestic environments; activities of daily living; knowledge discovery in databases; human activity recognition; MLP-Mixer; efficiency; n/a

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