Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

Objectives: Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces. Approach: Based on authors' developments, this work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system. Results: Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks. Significance: The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies

Estimation of Phantom Arm Mechanics About Four Degrees of Freedom After Targeted Muscle Reinnervation

The intuitive control of bionic arms requires estimation of amputee's phantom arm movements from residual muscle bio-electric signals. The functional use of myoelectric arms relies on the ability of controlling large sets of degrees of freedom (>3 DOFs) spanning elbow, forearm, and wrist joints. This would assure optimal hand orientation in any environment. As part of this paper we recorded high-density electromyograms with >190 electrodes from the residual stump of a trans-humeral amputee who underwent targeted muscle reinnervation. We employed clustering to determine eight spatially separated sub-sets of channels sampling electromyograms associated to the actuation of four phantom arm DOFs. We created a large-scale musculoskeletal model of the phantom arm encompassing 33 musculo-tendon units. For the first time, this enabled the accurate electromyography-driven simulation of complex phantom joint rotations about elbow flexion-extension, forearm pronation-supination, wrist flexion-extension, and radial-ulnar deviation. These results support the potential for a new class of bionic limbs that are controlled as natural extensions of the body, an important step toward next-generation prosthetics that mimic human biological functionality and robustness

Neuromechanical Model-Based Adaptive Control of Bilateral Ankle Exoskeletons:Biological Joint Torque and Electromyogram Reduction Across Walking Conditions

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. 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. 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 an exomuscle 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

Adaptive model-based myoelectric control for a soft wearable arm exosuit:A new generation of wearable robot control

Despite advances in mechatronic design, the widespread adoption of wearable robots for supporting human mobility has been hampered by 1) ergonomic limitations in rigid exoskeletal structures and 2) the lack of human-machine interfaces (HMIs) capable of sensing musculoskeletal states and translating them into robot-control commands. We have developed a framework that combines, for the first time, a model-based HMI with a soft wearable arm exosuit that has the potential to address key limitations in current HMIs and wearable robots. The proposed framework was tested on six healthy subjects who performed elbow rotations across different joint velocities and lifting weights. The results showed that the model-controlled exosuit operated synchronously with biological muscle contraction. Remarkably, the exosuit dynamically modulated mechanical assistance across all investigated loads, thereby displaying adaptive behavior

Identification of Motor Unit Twitch Properties in the Intact Human In Vivo

Restoring natural motor function in neurologically injured individuals is challenging, largely due to the lack of personalization in current neurorehabilitation technologies. Signal-driven neuro-musculoskeletal models may offer a novel paradigm for devising novel closed-loop rehabilitation strategies according to an individual's physiology. However, current modelling techniques are constrained to bipolar electromyography (EMG), thereby lacking the resolution necessary to extract the activity of individual motor units (MUs) in vivo. In this work, we decoded MU spike trains from high-density (HD)-EMG to obtain relevant neural properties across multiple isometric plantar-dorsiflexion tasks. Then, we sampled MU statistical distributions and used them to reproduce MU specific activation profiles. Results showed bimodal distributions which may correspond to slow and fast MU populations. The estimated activation profiles showed a high degree of similarity to the reference torque (R2>0.8) across the recorded muscles. This suggests that the estimation of MU twitch properties is a crucial step for the translation of neural information into muscle force.Clinical Relevance- This work has multiple implications for understanding the underlying mechanism of motor impairment and for developing closed-loop strategies for modulating alpha motor circuitries in neurologically injured individuals

Myoelectric model-based control of a bi-lateral robotic ankle exoskeleton during even ground locomotion ^∗

Individuals with neuromuscular injuries may fully benefit from wearable robots if a new class of wearable technologies is devised to assist complex movements seamlessly in everyday tasks. Among the most important tasks are locomotion activities. Current human-machine interfaces (HMI) are challenged in enabling assistance across wide ranges of locomoting tasks. Electromyography (EMG) and computational modelling can be used to establish an interface with the neuromuscular system. We propose an HMI based on EMG-driven musculoskeletal modelling that estimates biological joint torques in real-time and uses a percentage of these to dynamically control exoskeleton-generated torques in a task-independent manner, i.e. no need to classify locomotion modes. Proof of principle results on one subject showed that this approach could reduce EMGs during exoskeleton-assisted even ground locomotion compared to transparent mode (i.e. zero impedance). Importantly, results showed that a substantial portion of the biological ankle joint torque needed to walk was transferred from the human to the exoskeleton. That is, while the total human-exoskeleton ankle joint was always similar between assisted and zero-impedance modes, the ratio between exoskeleton-generated and human-generated torque varied substantially, with human-generated torques being dynamically compensated by the exoskeleton during assisted mode. This is a first step towards natural, continuous assistance in a large variety of movements