Portable gait-asymmetry detection using lowcost hardware and machine learning

Gait analysis provides insights into human motion by examining how individuals walk. However, the high cost associated with gait centers prevents conducting gait analysis regularly, reducing opportunities for early detection and prevention of gait-related issues before pain or injuries occur. The approach presented in this paper integrates low-cost yet computationally powerful hardware with signal processing and machine learning to develop a wearable sensor node placed at the pelvis that continuously collects gait data, providing personalized gait analysis. By positioning the wearable at the pelvis, gait asymmetries can be captured accurately. The approach is validated in a laboratory experiment with 15 participants walking on a treadmill and verified in a free-moving environment. Results indicate that the wearable detects gait asymmetries effectively, enhancing applicability in both clinical and non-clinical settings, supporting rehabilitation and preventive care in a cost-efficient manner.