Fine-Regularity Feature Extraction of Elderly Behaviors Based on Unsupervised Learning
- Resource Type
- Conference
- Authors
- Shang, Cuijuan; Zhang, Qiaoyun; Chang, Chih-Yung
- Source
- 2023 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) Consumer Electronics - Taiwan (ICCE-Taiwan), 2023 International Conference on. :341-342 Jul, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Medical services
Feature extraction
Behavioral sciences
Older adults
Unsupervised learning
Consumer electronics
fine-regularity
elderly
feature extraction
- Language
- ISSN
- 2575-8284
Behavioral analysis of the elderly is very important for improving the quality of elderly healthcare services. This paper proposes a Fine-Regularity Feature Extraction Mechanism (FRFEM) based on unsupervised learning algorithm, which can extract fine features including occurrence, duration and frequency regularities. The proposed FRFEM can be applied to detect abnormal behaviors and assess the health status of the elderly. Simulation study exhibits the effectiveness of the proposed FRFEM on feature extraction.