Predicting Continuous Locomotion Modes via Multidimensional Feature Learning from sEMG

TL;DR

This paper introduces Deep-STF, a deep learning model that accurately predicts multiple human locomotion modes and transitions from sEMG signals, enhancing the control of walking-assistive devices with high precision and stability.

Contribution

The study presents a novel end-to-end deep learning model for continuous prediction of locomotion modes and introduces the 'stable prediction time' metric for assessing prediction reliability.

Findings

Achieved 96.48% accuracy for 100 ms ahead predictions.

Maintained over 93% accuracy at 500 ms prediction horizon.

Demonstrated robust prediction performance across diverse locomotion modes.

Abstract

Walking-assistive devices require adaptive control methods to ensure smooth transitions between various modes of locomotion. For this purpose, detecting human locomotion modes (e.g., level walking or stair ascent) in advance is crucial for improving the intelligence and transparency of such robotic systems. This study proposes Deep-STF, a unified end-to-end deep learning model designed for integrated feature extraction in spatial, temporal, and frequency dimensions from surface electromyography (sEMG) signals. Our model enables accurate and robust continuous prediction of nine locomotion modes and 15 transitions at varying prediction time intervals, ranging from 100 to 500 ms. In addition, we introduced the concept of 'stable prediction time' as a distinct metric to quantify prediction efficiency. This term refers to the duration during which consistent and accurate predictions of mode transitions are made, measured from the time of the fifth correct prediction to the occurrence of the critical event leading to the task transition. This distinction between stable prediction time and prediction time is vital as it underscores our focus on the precision and reliability of mode transition predictions. Experimental results showcased Deep-STP's cutting-edge prediction performance across diverse locomotion modes and transitions, relying solely on sEMG data. When forecasting 100 ms ahead, Deep-STF surpassed CNN and other machine learning techniques, achieving an outstanding average prediction accuracy of 96.48%. Even with an extended 500 ms prediction horizon, accuracy only marginally decreased to 93.00%. The averaged stable prediction times for detecting next upcoming transitions spanned from 28.15 to 372.21 ms across the 100-500 ms time advances.