Probability-Based Rejection of Decoding Output Improves the Accuracy of Locomotion Detection During Gait.

Prosthetic users need reliable control over their assistive devices to regain autonomy and independence, particularly for locomotion tasks. Despite the potential for myoelectric signals to reflect the users' intentions more accurately than external sensors, current motorized prosthetic legs fail to utilize these signals, thus hindering natural control. A reason for this challenge could be the insufficient accuracy of locomotion detection when using muscle signals in activities outside the laboratory, which may be due to factors such as suboptimal signal recording conditions or inaccurate control algorithms.This study aims to improve the accuracy of detecting locomotion during gait by utilizing classification post-processing techniques such as Linear Discriminant Analysis with rejection thresholds. We utilized a pre-recorded dataset of electromyography, inertial measurement unit sensor, and pressure sensor recordings from 21 able-bodied participants to evaluate our approach. The data was recorded while participants were ambulating between various surfaces, including level ground walking, stairs, and ramps. The results of this study show an average improvement of 3% in accuracy in comparison with using no post-processing (p-value < 0.05). Participants with lower classification accuracy profited more from the algorithm and showed greater improvement, up to 8% in certain cases. This research highlights the potential of classification post-processing methods to enhance the accuracy of locomotion detection for improved prosthetic control algorithms when using electromyogram signals.Clinical Relevance- Decoding of locomotion intent can be improved using post-processing techniques thus resulting in a more reliable control of lower limb prostheses.