Real-Time Constant Monitoring of Fall Risk Index by Means of Fully-Wireless Insoles

Area of Science:

• Biomedical Engineering
• Gerontology
• Rehabilitation Science

Background:

• Gait analysis in real-world conditions is crucial for accurate Fall Risk Index (FRI) assessment.
• Most falls occur outside of controlled clinical settings, limiting current assessment methods.
• Wearable solutions often focus on upper body measurements, neglecting crucial foot-based gait data.

Purpose of the Study:

• To introduce the WIISEL platform for continuous, real-life gait monitoring.
• To present a novel approach for estimating Fall Risk Index (FRI) using wireless insole technology.
• To validate the accuracy and statistical significance of the proposed FRI prediction model.

Main Methods:

• Development of a complete tool-chain including wireless insoles, smartphone app, and server-based analysis.
• Collection of comprehensive gait-related data directly from the feet.
• Personalized combination of gait parameters and data analytics for individual FRI estimation.

Main Results:

• The WIISEL platform enables full-wireless, foot-based gait data acquisition.
• A novel, personalized Fall Risk Index (FRI) was developed and assessed by experts.
• The new FRI demonstrated statistically significant predictive validity compared to existing standards.

Conclusions:

• The WIISEL platform offers a robust solution for real-life gait monitoring and fall risk assessment.
• Personalized FRI estimation using wireless insoles provides valuable insights for patients, caregivers, and clinicians.
• Continuous monitoring facilitates long-term evolution assessment and proactive fall prevention strategies.