Real-Time Balancing of Stability and Plasticity in Continual Learning Enables Adaptive Speed Estimation for Lower-Limb Prostheses.

A primary challenge in continual learning (CL) for wearable robotics, especially prosthetics, is balancing the need to retain learned knowledge (stability) with the necessity to adapt to new information (plasticity). This balance is crucial for online adaptation, enabling systems to transition between tasks without losing prior knowledge. In this paper, we introduce a novel online optimizer-based framework designed to manage the stability-plasticity balance through strategic datapoint replay and learning-rate adjustments of a deep neural network. We applied this framework to speed estimation systems for transfemoral prostheses (TFA users), conducting offline validation tests using data from 10 individuals with TFA, and online tests with three TFA and six able-bodied (AB) participants. Our results demonstrate statistically significant improvements: in offline settings, our method showed a 39.2% increase in stability and a 35.2% boost in plasticity over traditional CL approaches during leave-one-subject-out validation. Similarly, in real-time trials with AB participants, we observed statistically significant gains in handling both previously encountered and new walking speeds. Finally, trials with individuals with TFA showed that the system improved the plasticity of the baseline model by 67.45% and the stability of the traditional CL approach by 31.36%; reducing overall average walking speed estimation error by 19.47%.