Real-time Adaptation of Deep Learning Walking Speed Estimators Enables Biomimetic Assistance Modulation in an...

This study introduces a novel continual learning algorithm that incrementally improves the performance of deep-learning-based walking speed estimators during level-ground walking with a powered knee-ankle prosthesis. While user-dependent (DEP) estimators generally outperform user-independent (IND) estimators, they require the pre-collection of DEP training data. In contrast, our real-time algorithm adapts IND estimators to self-labeled DEP data generated during walking, eliminating the need for pre-collected datasets. The algorithm also features a biomimetic scaling mechanism that adjusts prosthetic assistance based on speed estimates. We evaluated our algorithm on novel subjects (N=10) with unilateral above-knee amputations during treadmill and overground walking. For treadmill trials, when adapted with estimated and ground truth labels, estimators achieved mean absolute errors (MAEs) of 0.074 [0.023] (mean, [standard deviation]) and 0.074 [0.018] m/s, respectively, reflecting a significant 28% (p < 0.05) reduction in MAE compared to non-adapted estimators. For overground trials, treadmill-adapted estimators demonstrated a significant 18% (p < 0.05) reduction in MAE compared to non-adapted estimators. Our algorithm significantly reduced speed estimation errors within one minute of walking and delivered biomimetic assistance (r = 0.91) across speeds. This approach allows off-the-shelf powered prostheses to seamlessly adapt to new users, delivering biomimetic assistance through precise, real-time walking speed estimation.