State-of-the-Art Wearable Sensors and Possibilities for Radar in Fall Prevention

Area of Science:

• Engineering
• Gerontology
• Computer Science

Background:

• Falls are a significant health concern for adults aged 65+, with one in three experiencing a fall annually.
• Gait analysis is crucial for fall prevention interventions, with a focus on wearable sensors and radar technology.
• Current fall prevention strategies require advancement to address the needs of an aging population.

Purpose of the Study:

• To review existing sensors for gait analysis and their application in technology-based fall prevention.
• To identify knowledge gaps in wearable radar development, signal processing, and machine learning for fall risk assessment.
• To explore the potential of radar technology for gait monitoring and real-time obstacle detection.

Main Methods:

• A comprehensive literature review was conducted on sensors for gait analysis.
• The review focused on wearable devices and radar technology applications in fall prevention.
• Identified knowledge gaps related to wearable radar, signal processing, and machine learning algorithms.

Main Results:

• Wearable radar presents a promising technology for gait parameter measurement and fall risk assessment.
• Machine learning algorithms are key for classification and risk assessment in radar-based gait monitoring.
• Radar technology can also be utilized for real-time monitoring of environmental obstacles.

Conclusions:

• Radar technology, especially wearable systems, holds significant potential for fall prevention in older adults.
• Further research is needed in wearable radar development, specialized signal processing, and AI for gait analysis.
• Integrating radar for gait monitoring in natural environments can enhance safety and independence for the elderly.