Robustification of Bayesian-Inference-Based Gait Estimation for Lower-Limb Wearable Robots

Lower-limb wearable robots designed to assist people in everyday activities must reliably recover from any momentary confusion about what the user is doing. Such confusion might arise from momentary sensor failure, collision with an obstacle, losing track of gait due to an out-of-distribution stride, etc. Systems that infer a user's walking condition from angle measurements using Bayesian filters (e.g., extended Kalman filters) have been shown to accurately track gait across a range of activities. However, due to the fundamental problem structure and assumptions of Bayesian filter implementations, such estimators risk becoming ‘lost’ with little hope of a quick recovery. In this letter, we 1) introduce a Monte Carlo-based metric to quantify the robustness of pattern-tracking gait estimators, 2) propose strategies for improving tracking robustness, and 3) systematically evaluate them against this new metric using a publicly available gait biomechanics dataset. Our results, aggregating 2,700 trials of simulated walking of 10 able-bodied subjects under random perturbations, suggest that drastic improvements in robustness (from 8.9% to 99%) are possible using relatively simple modifications to the estimation process without noticeably degrading estimator accuracy.

I. Introduction

Lower-limb wearable robots, such as powered prostheses and exoskeletons, have great potential to enhance mobility for people with lower-limb disabilities. By inferring their user's intent (such as walking or climbing stairs) from onboard sensors and applying the corresponding torques to the user's biological or prosthetic joints, these devices aim to mechanically compensate for the disability and to allow users to perform the tasks comfortably. However, since typical onboard sensors of such robots, for example, their inertial measurement units (IMUs), joint encoders, and force/torque sensors, can only offer a limited picture of the user's true intent [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], the inference of human intent is challenging, and its failure can lead to unreliable device behavior. For safety-critical applications with a prosthetic leg, this unreliability is a major obstacle to system acceptance and adoption. As a result, reliably tracking the various activities of daily living has become a key challenge for lower-limb wearable robot controllers.