High-level locomotion intent estimation from electromyography and body posture

Objective.Once we learn a reliable gait, we no longer have to consciously contract individual muscles to walk, or think about the fine-grained low-level control of our joints. Instead, we mainly make decisions on where we want to end up, at what pace and through which path. Estimating this high-level (HL) intent may provide the necessary input to wearable robotic devices to adapt to their user's needs. We introduce a continuous representation of locomotion goals and investigate how it may be estimated from muscle signals and body posture.Approach.This study investigated methods to estimate a representation of HL locomotion intent, the horizontal walking path. We collected full-body motion capture and bipolar surface electromyography data from 6 subjects during non-steady-state gait. We trained temporal convolutional networks to causally predict the walking path directly or parametrically with a critically damped trajectory model, using a mixture of muscle and body posture signals.Main results.We achieved a mean trajectory estimation accuracy for a 1-second walking path corresponding tor2=0.89using a multimodal model. We simultaneously provided estimates for current and desired walking velocities as constrained by the walking path model, aiding interpretability of the estimator's output.Significance.Our approach could provide user interfacing in a subject-independent format for wearable robotic devices. Moreover, this HL intent representation is flexible and able to be synthesized in virtual environments, where it can serve as a surrogate for biosignals of simulated intent-driven robotics.