Fusion of Bilateral Lower-Limb Neuromechanical Signals Improves Prediction of Locomotor Activities.

Abstract

Wearable lower-limb assistive devices have the potential to dramatically improve the walking ability of millions of individuals with gait impairments. However, most control systems for these devices do not enable smooth transitions between locomotor activities because they cannot continuously predict the user's intended movements. Intent recognition is an alternative control strategy that uses patterns of signals detected before movement completion to predict future states. This strategy has already enabled amputees to walk and transition seamlessly and intuitively between activities (e.g., level ground, stairs, ramps) using control signals from mechanical sensors embedded in the prosthesis and muscles of their residual limb. Walking requires interlimb coordination because the leading and trailing legs have distinct biomechanical functions. For unilaterally-impaired individuals, these differences tend to be amplified because they develop asymmetric gait patterns; however, state-of-the-art intent recognition approaches have not been systematically applied to bilateral neuromechanical control signals. The purpose of this study was to determine the effect of including contralateral side signals for control in an intent recognition framework. First, we conducted an offline analysis using signals from bilateral lower-limb electromyography (EMG) and joint and limb kinematics recorded from 10 able-bodied subjects as they freely transitioned between level ground, stairs, and ramps without an assistive device. We hypothesized that including information from the contralateral side would reduce classification errors. Compared to ipsilateral sensors only, bilateral sensor fusion significantly reduced error rates; moreover, only one additional sensor from the contralateral side was needed to achieve a significant reduction in error rates. To the best of our knowledge, this is the first study to systematically investigate using simultaneously recorded bilateral lower-limb neuromechanical signals for intent recognition. These results provide a device-agnostic benchmark for intent recognition with bilateral neuromechanical signals and suggest that bilateral sensor fusion can be a simple but effective modular strategy for enhancing the control of lower-limb assistive devices. Finally, we provide preliminary offline results from one above-knee amputee walking with a powered leg prosthesis as a proof-of-concept for the generalizability and benefit of using bilateral sensor fusion to control an assistive device for an impaired population.