A hybrid approach for dynamically training a torque prediction model for devising a human-machine interface control strategy
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Human-machine interfaces (HMI) play a pivotal role in the rehabilitation and daily assistance of lower-limb amputees. The brain of such interfaces is a control model that detects the user's intention using sensor input and generates corresponding output (control commands). With recent advances in technology, AI-based policies have gained attention as control models for HMIs. However, supervised learning techniques require affluent amounts of labeled training data from the user, which is challenging in the context of lower-limb rehabilitation. Moreover, a static pre-trained model does not take the temporal variations in the motion of the amputee (e.g., due to speed, terrain) into account. In this study, we aimed to address both of these issues by creating an incremental training approach for a torque prediction model using incomplete user-specific training data and biologically inspired temporal patterns of human gait. To reach this goal, we created a hybrid of two distinct approaches, a generic inter-individual and an adapting individual-specific model that exploits the inter-limb synergistic coupling during human gait to learn a function that predicts the torque at the ankle joint continuously based on the kinematic sequences of the hip, knee, and shank. An inter-individual generic base model learns temporal patterns of gait from a set of able-bodied individuals and predicts the gait patterns for a new individual, while the individual-specific adaptation model learns and predicts the temporal patterns of gait specific to a particular individual. The iterative training using the hybrid model was validated on eight able-bodied and five transtibial amputee subjects. It was found that, with the addition of estimators fitted to individual-specific data, the accuracy significantly increased from the baseline inter-individual model and plateaued within two to three iterations.
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