Efficient Neural Decoding of Multi-Finger Forces for Continuous Human-Robot Interaction Control

Accurate prediction of multi-finger forces can advance human-robotic interactions, enhancing the dexterity and precision of robotic assistance in various applications. Surface electromyogram (sEMG), which captures electrical signals during muscle activation, is a preferred information source for such predictions. Nevertheless, existing techniques are hindered by either inaccurate decoding capacity or intricate processes.

To address these challenges, our study introduced a novel motor unit (MU)-level probabilistic refinement framework to simultaneously and continuously predict extension and flexion forces of individual fingers. Specifically, we extracted MUs using high-density sEMG data during sequential multi-finger tasks, where numerous non-target MUs compromise decoding accuracy. To overcome this, we quantified the likelihood of each MU being finger-specific by mapping its average firing rates during plateau contractions into a normalized probability distribution using a Softmax-based strategy. An optimization-driven probability threshold was then applied to retain only those MUs with the most reliable and finger-specific contributions for final decoding.

The results showed that our new decoder accurately predicted sequential multi-finger forces during dexterous finger flexion and extension tasks. Our approach outperformed current state-of-the-art neural decoding methods (p < 0.05), with high computational efficiency and robustness over different task variations.

Our MU-level probabilistic refinement effectively reduced non-target MU interference and enables accurate, robust, and efficient continuous decoding of individual-finger forces across flexion and extension.

The outcomes can potentially promote advances in real-time force control during human-robot interactions, thereby fostering more intuitive and efficient collaborations between humans and machines.