Neuromechanical model-based adaptive control of bi-lateral ankle exoskeletons: biological joint torque and electromyogram reduction across walking conditions (2108.00980v2)

Abstract: To enable the broad adoption of wearable robotic exoskeletons in medical and industrial settings, it is crucial they can adaptively support large repertoires of movements. We propose a new human-machine interface to simultaneously drive bilateral ankle exoskeletons during a range of 'unseen' walking conditions and transitions that were not used for establishing the control interface. The proposed approach used person-specific neuromechanical models to estimate biological ankle joint torques in real-time from measured electromyograms (EMGS) and joint angles. A low-level controller based on a disturbance observer translated biological torque estimates into exoskeleton commands. We call this 'neuromechanical model-based control' (NMBC). NMBC enabled six individuals to voluntarily control a bilateral ankle exoskeleton across six walking conditions, including all intermediate transitions, i.e., two walking speeds, each performed at three ground elevations, with no need for predefined torque profiles, nor a priori chosen neuromuscular reflex rules, or state machines as common in literature. A single subject case-study was carried out on a dexterous locomotion tasks involving moonwalking. NMBC always enabled reducing biological ankle torques, as well as eight ankle muscle EMGs both within (22% torque; 12% EMG) and between walking conditions (24% torque; 14% EMG) when compared to non-assisted conditions. Torque and EMG reductions in novel walking conditions indicated that the exoskeleton operated symbiotically, as exomuscles controlled by the operator's neuromuscular system. This opens new avenues for the systematic adoption of wearable robots as part of out-of-the-lab medical and occupational settings.

The paper focuses on the development of a novel control system for bilateral ankle exoskeletons, aiming to facilitate their broader adoption in medical and industrial contexts. This research addresses the need for exoskeletons to support a diverse range of movements in various walking conditions without requiring pre-established torque profiles or specialized neuromuscular setups.

Key Aspects of the Research:

1. Human-Machine Interface: The authors propose a new interface based on neuromechanical models. This interface estimates biological ankle joint torques in real-time using electromyograms (EMGs) and joint angles, thus allowing adaptive control of ankle exoskeletons.
2. Neuromechanical Model-Based Control (NMBC): This method enables the exoskeleton to function seamlessly across different walking conditions, including changes in walking speed and ground elevation. The NMBC does not rely on predefined torque profiles, reflex rules, or state machines typically used in conventional systems.
3. Experimental Validation: The paper tested the NMBC approach with six participants under six different walking conditions. Furthermore, a single subject performed a complex locomotion task (moonwalking) to demonstrate the system's versatility.
4. Performance Outcomes: The use of NMBC significantly reduced biological ankle torques and EMG signals in both within-condition (22% reduction in torque and 12% in EMG) and between-condition scenarios (24% reduction in torque and 14% in EMG). These reductions indicate that the exoskeletons operate symbiotically as "exomuscles," controlled by the user’s neuromuscular system.
5. Implications: The results suggest the potential for wearable robots to be integrated into everyday settings outside laboratory environments, advancing their utility in rehabilitative and occupational settings.

Overall, this paper presents a significant advancement in the adaptive control of wearable exoskeletons, offering evidence of their efficacy in reducing the physical demand on users while adapting to various dynamic conditions.