Frailty in older adults is caused by functional declines that result in unstable gait. This study analyzed gait in 24 frail and 22 non-frail older adults using acceleration and angular velocity signals from a wireless tri-axial inertial measurement unit (IMU). After noise was removed through Savitzky-Golay and Butterworth filters, gait features correlated with frailty were proposed and evaluated through normality tests and statistical analysis. To evaluate the frailty of older adults based on significant gait features derived from statistical analysis, the primary accuracy achieved is roughly around 84-89 % in k-nearest neighbor, support vector machine, and random forest models. To provide clinicians with a good tool for monitoring frailty and support preventive healthcare and aging-in-place strategies, we propose a gait-based detection system with an optimal feature extraction scheme that can exhaustively enumerate and evaluate potential parameters for optimal performance. This system significantly improved classification metrics (nearly all >95 %) with lower sensitivity and specificity and achieved 96 % accuracy with a portable, low-cost system that uses only one minute of walking data. These findings demonstrate that IMU-based gait analysis improves objectivity and accuracy in frailty classification. The optimal feature extraction scheme further refines performance, offering a scalable and time-efficient solution for community-based frailty detection. This approach highlights the potential of wearable sensors in improving geriatric health assessments.
Keywords: Frailty diagnosis; Gait assessment; Machine learning.
© 2025 The Authors.