Falls management: detection and prevention, using a waist-mounted triaxial accelerometer

Area of Science:

• Gerontology
• Biomedical Engineering
• Digital Health

Background:

• Falls are a major health concern for community-dwelling elderly individuals, leading to significant morbidity and reduced independence.
• Existing falls management systems often lack real-time capabilities or comprehensive risk tracking in unsupervised living environments.

Purpose of the Study:

• To present a distributed falls management system for real-time fall detection and remote monitoring of falls risk parameters.
• To enable proactive falls prevention through self-administered assessments and facilitate targeted interventions.
• To improve emergency response and enhance the independence of the elderly population.

Main Methods:

• Development of a waist-mounted triaxial accelerometer system for continuous data collection.
• Implementation of a self-administrable falls risk assessment tool.
• Creation of a web-interface for clinician monitoring and intervention compliance tracking.

Main Results:

• The system provides real-time falls detection in unsupervised settings.
• Longitudinal tracking of falls risk parameters allows for early identification of increased risk.
• Clinician monitoring and exercise compliance tracking are facilitated via a web interface.

Conclusions:

• The described system offers a comprehensive approach to falls management for community-dwelling elderly individuals.
• Real-time detection and early risk identification enable prompt interventions, reducing negative health outcomes.
• This technology has the potential to significantly improve the safety and independence of older adults living at home.