Personalized balance evaluation improves hip exoskeleton walking safety
A Personalized Dynamic Balance Evaluation Paradigm for Hip Exoskeleton-Assisted Walking under Unexpected Ground Perturbations
RoboticsMachine Learning
Summary
Walking with a hip exoskeleton can help people keep better balance after sudden slips, but figuring out the best way to assist each person is tricky because balance involves many factors. The authors created a method that combines several measurements of balance into one score that is tailored to each person. This score helps quickly find the best hip assistance settings for different walking conditions, reducing the number of tests needed. Their tests with three people showed this personalized score worked better than simpler methods and helped find safer walking assistance quickly.
What this means in practice
- •For rehabilitation engineers: Optimize hip exoskeleton settings for individuals recovering from gait instability by reducing test time and improving safety.
- •For wearable device developers: Develop adaptive control algorithms for hip exoskeletons that personalize assistance to maintain user balance during unexpected slips.$Commercial implications: Enables commercial hip exoskeletons with personalized adaptive balance assistance, improving user safety and comfort.
Authors
Yun Chen, Oluwasegun T. Akinniyi, Qiang Zhang
Abstract
Hip exoskeletons may improve recovery from unexpected gait perturbations, yet personalizing assistance remains difficult because balance is multidimensional and human-in-the-loop experiments are small-sample and noisy. We present a participant-specific composite balance cost that integrates seven biomechanical sub-metrics spanning margin of stability, center-of-mass dynamics, and whole-body angular momentum. The sub-metrics are converted to direction-aligned, dimensionless cost features, and nonnegative fusion weights are learned on the simplex. Coupled with an empirical-Bayes hierarchical model, the learned-composite selector estimates each tested condition's posterior probability of being best, P(best), and a high-probability candidate set with size $K_{0.8}$. The framework was evaluated with three participants walking at 1.1 m/s during unilateral belt-slip perturbations across 46 hip-assistance conditions. In the full-budget analysis (B = 4 repeats per condition), the selector concentrated 80% of the posterior probability within 1 to 5 of 46 conditions, compared with 2 to 12 for equal-weight fusion and 4 to 37 for principal component analysis fusion. This smaller candidate set could shorten personalization experiments and limit participants' exposure to repeated perturbations in future studies. Selected-condition trials showed lower observed composite costs than no-torque trials, with nominal p < 0.05 for P2 and P3. Leave-one-repeat-out refits yielded positive mean held-out rank correlations for all participants and moderate stability of the learned weights and candidate sets. These proof-of-concept results support participant-specific composite balance evaluation for candidate selection in perturbation-based human-in-the-loop experiments.