Sensor-Based Human Activity Recognition for Assistive Health Technologies
Muhammad Adeel Nisar
Cite this publication as
Muhammad Adeel Nisar, Sensor-Based Human Activity Recognition for Assistive Health Technologies (2023), Logos Verlag, Berlin, ISBN: 9783832583750
Description / Abstract
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.
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.
Table of content
- BEGINN
- Acknowledgements
- Abstract
- Zusammenfassung
- Table of contents
- Introduction
- Fundamental Concepts
- Vision-Based HAR
- Sensor-Based HAR
- Applications in Health Monitoring
- Problem Statement and Motivation
- Own Contribution
- Overview
- Recognition of Short-Term Activities
- Problem Statement
- Related Work
- Own Contribution
- Dataset Description
- Codebook Approach
- Deep Learning Approach
- Summary
- Recognition of Activities of Daily Living
- Problem Statement
- Related Work
- Own Contribution
- Hierarchical Framework for Activity Recognition
- Rank Pooling Approach
- Deep Learning Approach
- Discussion for Real-World Settings
- Summary
- Multitask Learning for Human Activity Recognition
- Problem Statement
- Overview of the Proposed Approach
- Related Work
- Own Contribution
- Methodology
- Evaluation
- Summary
- Conclusion and Future Work
- Summary
- Conclusion and Limitations
- Future Work
- Bibliography
- List of own publications
- List of abbreviations
- List of tables
- List of figures
- Curriculum Vitae