Walking Mode-depending Improvements of Locomotion Detection through Rejection Based Post-Processing.

The limited availability of information regarding user intent and the surrounding locomotion environment poses obstacles to achieving intuitive prosthetic control. Despite utilizing machine learning algorithms based on external muscle activity and movement sensors to infer user intention, the stringent reliability requirements for leg prostheses control have not been met. This study seeks to enhance the accuracy of locomotion mode detection by incorporating information in the postprocessing phase following Linear Discriminant Analysis classification of locomotion modes. A locomotion dataset comprising data from 15 able-bodied participants, including electromyography, inertial measurement units, and insole pressure sensors during both level walking and stair/ramp ambulation, was employed. To address uncertainties in classification, a threshold-based rejection postprocessing method was implemented, eliminating classifications falling below the threshold. The rejection threshold significantly improved overall locomotion detection accuracy, with transition locomotion showing more pronounced improvement compared to steady-state locomotion. A subanalysis that specifically examined transition locomotion emphasized that biomechanically similar transitions, such as moving from a slight ramp slope to level walking, demonstrated more significant improvement compared to dissimilar transitions like stair to level walking. These findings underscore the significance of leveraging additional information with postprocessing to refine uncertain locomotion classification for better control for prosthetic users.