Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Area of Science:

• Computer Science
• Biomedical Engineering
• Artificial Intelligence

Background:

• Human Activity Recognition (HAR) is crucial for healthcare, security, and human-computer interaction.
• Growing elderly populations increase demand for monitoring daily living activities (ADL).
• Wearable sensors offer a promising technology for advanced HAR in healthcare.

Purpose of the Study:

• To conduct a comprehensive survey of HAR systems based on wearable sensors.
• To analyze applications, challenges, datasets, approaches, and system components.
• To provide systematic categorizations, comparisons, and qualitative evaluations of existing HAR approaches.

Main Methods:

• Review and analysis of existing literature on HAR using wearable sensors.
• Categorization of HAR research based on data acquisition, methods, and activity complexity.
• Qualitative evaluation of HAR approaches based on defined system criteria.

Main Results:

• Identified key components of wearable sensor-based HAR: sensor location, data preprocessing, and recognition methods.
• Provided a systematic architecture and coherent categorizations for HAR systems.
• Offered comprehensive reviews and comparisons of various HAR approaches.

Conclusions:

• Wearable sensor-based HAR is a rapidly evolving field with significant potential in healthcare and security.
• This survey offers a more extensive and coherent review than previous studies.
• Highlights the need for continued research into sensor placement, data processing, and recognition algorithms.