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

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.

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.

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).

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.