Continuous volitional control of a bionic leg supports diverse walking patterns in both agonist–antagonist muscle interface and bone-anchored prosthesis users

TL;DR

A new control system for bionic legs allows users with different types of prostheses to walk more naturally by adjusting movement based on muscle signals.

Contribution

A neuromechanical model-based HMI enables volitional control of bionic legs for AMI and BAP users across diverse walking conditions.

Findings

Participants achieved 87% accuracy in modulating prosthesis torque timing within target ranges.

The HMI enabled control of walking speed and incline through muscle activation patterns.

The system works for both AMI and BAP users, suggesting generalizability across amputation types.

Abstract

Myoelectric control paradigms have the potential to enable continuous volitional control of bionic limbs in various movement conditions. Although individuals with below knee amputations and an agonist–antagonist muscle interface (AMI) were proven to display a greater degree of continuous volitional control in bionic ankle-foot systems with respect to conventional socket-suspended prosthetic users, it remains unclear how myoelectric interfaces could translate to non-AMI prosthetic users with bone-anchored prostheses (BAP). This preliminary study proposes a human–machine interface (HMI) based on a neuromechanical model to enable volitional, continuous myoelectric control of a bionic leg in AMI and BAP users, walking across various speeds and ground inclinations. Differently from state of the art solutions, the proposed HMI is based on a digital twin of the intact leg, synthesizing the user’s phantom limb musculoskeletal function as controlled by muscle activations measured from the residuum. When embedded in a real-time framework, it enabled the participants to achieve volitional modulation of prosthesis peak plantar-dorsiflexion torques timing and amplitude during overground walking at three speeds (between 1.6 and 3.96 km/h), with case studies provided during calf-raises (30, 45, and 60 bpm) and ramp ascent walking (3 and 5% incline). Before prosthesis control tests, the participants underwent a 2-day gait training session. Results showed that all three subjects learned how to alter initial muscle activation patterns so that an average of 87% of peak activation timing fell within target ranges. The proposed neuromechanical modeling technology opens new avenues toward generalizable HMIs for the volitional control of active prostheses beyond set conditions and amputation types.

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