Automatic Detection of Fatigued Gait Patterns in Older Adults: An Intelligent Portable Device Integrating Force and Inertial Measurements with Machine Learning.

Purpose: This study aimed to assess the feasibility of early detection of fatigued gait patterns for older adults through the development of a smart portable device.

Methods: The smart device incorporated seven force sensors and a single inertial measurement unit (IMU) to measure regional plantar forces and foot kinematics. Data were collected from 18 older adults walking briskly on a treadmill for 60 min. The optimal feature set for each recognition model was determined using forward sequential feature selection in a wrapper fashion through fivefold cross-validation. The recognition model was selected from four machine learning models through leave-one-subject-out cross-validation.

Results: Five selected characteristics that best represented the state of fatigue included impulse at the medial and lateral arches (increased, p = 0.002 and p < 0.001), contact angle and rotation range of angle in the sagittal plane (increased, p < 0.001), and the variability of the resultant swing angular acceleration (decreased, p < 0.001). The detection accuracy based on the dual signal source of IMU and plantar force was 99%, higher than the 95% accuracy based on the single source. The intelligent portable device demonstrated excellent generalization (ranging from 93 to 100%), real-time performance (2.79 ms), and portability (32 g).

Conclusion: The proposed smart device can detect fatigue patterns with high precision and in real time.

Significance: The application of this device possesses the potential to reduce the injury risk for older adults related to fatigue during gait.