Human-in-the-Loop Personalization of a Bi-Articular Wearable Robot’s Assistance for Downhill Walking

Wearable robots hold great promise in supporting people during activities of daily living. Personalizing the assistance provided by this technology for not just level ground walking, but also other real-life activities like uphill and downhill walking, will foster its wider adoption. In this paper, we propose an approach to personalize the assistance delivered by a wearable robot – the Myosuit, a tendon-driven soft robot for mobility assistance – for downhill walking. We use a human-in-the-loop method with candidate profiles of 15-steps which aims to minimize knee extensor muscle activity, utilizing the covariance matrix adaptation evolution strategy algorithm (CMA-ES) and a cost function based on normalized RMS of surface EMG signals. By utilizing CMA-ES, we vary the magnitude and timing of Myosuit’s support to find the most appropriate assistance profiles. We compared the optimal profiles to assistance off (transparency) condition and a general trajectory derived using a model-based approach. Relative to the transparency condition, the average muscle activity was reduced by 12.67% (SD: ±8.73; t(20) = 5.25,
$p\leq.01$
) across all participants and muscle groups. The results of our study may support future development of personalized assistance algorithms for wearable robots and lead to better adoption of this technology.