Discriminating Between Fallers and Non-Fallers Using Kinematic Data from the Heel2Toe™ Wearable Sensor.

Area of Science:

• Biomechanics
• Gerontology
• Wearable Technology

Background:

• Falls are a major health concern, often occurring during walking.
• Gait quality, particularly kinematic parameters, is a key factor in fall risk.
• Ankle kinematics are closely linked to common fall-related incidents like trips and slips.

Purpose of the Study:

• To determine the association between ankle angular velocity (AV) during walking and fall history.
• To develop a predictive model for fall risk based on ankle AV metrics.

Main Methods:

• A comparative study analyzing ankle AV metrics in individuals with and without a history of falls.
• Utilized data from the Heel2Toe™ sensor across various settings.
• Employed logistic regression with natural cubic splines to identify discriminating AV parameters.

Main Results:

• Ankle angular velocity at heel strike was identified as a significant discriminator between fallers and non-fallers.
• A novel algorithm was developed using logistic regression parameters to estimate fall risk.
• The algorithm produced fall risk probabilities ranging from 0.0480 to 0.7245, influenced by participant age.

Conclusions:

• Measures of ankle angular velocity during walking show significant potential for fall risk assessment.
• The developed algorithm offers a promising tool for estimating fall risk, warranting further validation.
• Ankle kinematics represent a valuable therapeutic and diagnostic target for fall prevention strategies.