Sensor Based Human Activity Recognition For Assistive Health Technologies
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Sensor-Based Human Activity Recognition for Assistive Health Technologies
Author: Muhammad Adeel Nisar
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2023-02-20
The average age of people has increased due to advances in health sciences, which has led to an increase in the elderly population. This is positive news, but it also raises questions about the quality of independent living for older people. Clinicians use Activities of Daily Living (ADLs) to assess older people's ability to live independently. In recent years, portable computing devices have become more present in our daily lives. Therefore, a software system that can detect ADLs based on sensor data collected from wearable devices is beneficial for detecting health problems and supporting health care. In this context, this book presents several machine learning-based approaches for human activity recognition (HAR) using time-series data collected by wearable sensors in the home environment. In the first part of the book, machine learning-based approaches for atomic activity recognition are presented, which are relatively simple and short-term activities. In the second part, the algorithms for detecting long-term and complex ADLs are presented. In this part, a two-stage recognition framework is also presented, as well as an online recognition system for continuous monitoring of HAR. In the third and final part, a novel approach is proposed that not only solves the problem of data scarcity but also improves the performance of HAR by implementing multitask learning-based methods. The proposed approach simultaneously trains the models of short- and long-term activities, regardless of their temporal scale. The results show that the proposed approach improves classification performance compared to single-task learning.
Enabling Person-Centric Healthcare Using Ambient Assistive Technology, Volume 2
This book focuses more on the transformative impact of person-centric health care, where it explores cutting-edge advancements in integrating artificial intelligence and machine learning to deliver personalized and efficient care. Key topics include the application of predictive models for critical health conditions such as brain stroke, lung cancer, diabetes, and Alzheimer's, as well as the integration of secure frameworks to protect sensitive patient data. The book also covers advanced techniques for recognizing human activities in ambient environments, optimizing patient data clustering, and evaluating deep learning methods for unique use cases like yoga pose classification and resource optimization in smart healthcare. Designed for healthcare professionals, researchers, data scientists, and technologists, this book presents a harmonious blend of technical insights and practical applications, emphasizing person-centric approaches. By focusing on multi-disease prediction, assistive technologies, and enhanced emergency management, this book serves as a vital resource for innovating healthcare delivery in smart environments.
IoT Sensor-Based Activity Recognition
Author: Md Atiqur Rahman Ahad
language: en
Publisher: Springer Nature
Release Date: 2020-07-30
This book offer clear descriptions of the basic structure for the recognition and classification of human activities using different types of sensor module and smart devices in e.g. healthcare, education, monitoring the elderly, daily human behavior, and fitness monitoring. In addition, the complexities, challenges, and design issues involved in data collection, processing, and other fundamental stages along with datasets, methods, etc., are discussed in detail. The book offers a valuable resource for readers in the fields of pattern recognition, human–computer interaction, and the Internet of Things.