Voluntary control of wearable robotic exoskeletons by patients with paresis via neuromechanical modeling.

BACKGROUND: Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. METHODS: We have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time. RESULTS: We demonstrated patients' control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients. CONCLUSIONS: Continuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots

Robust real-time musculoskeletal modeling driven by electromyograms

Objective: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses and current biomechanical models cannot be used for real-time control in man-machine interfaces. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human. Methods: We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual’s anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system. Results: The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e. predicting accurate joint moments during six unseen tasks and one unseen DOF. Conclusion: The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems. Significance: The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient-machine interfacing

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

Real-time lumbosacral joint loading estimation in exoskeleton-assisted lifting conditions via electromyography-driven musculoskeletal models

Lumbar joint compression forces have been linked to the development of chronic low back pain, which is specially present in occupational environments. Offline methodologies for lumbosacral joint compression force estimation are not commonly integrated in occupational or medical applications due to the highly time-consuming and complex post-processing procedures. Hence, applications such as real-time adjustment of assistive devices (i.e., back-support exoskeletons) for optimal modulation of compression forces remains unfeasible. Here, we present a real-time electromyography (EMG)-driven musculoskeletal model, capable of estimating accurate lumbosacral joint moments and plausible compression forces. Ten participants performed box-lifting tasks (5 and 15 kg) with and without the Laevo Flex back-support exoskeleton using squat and stoop lifting techniques. Lumbosacral kinematics and EMGs from abdominal and thoracolumbar muscles were used to drive, in real-time, subject-specific EMG-driven models, and estimate lumbosacral joint moments and compression forces. Real-time EMG-model derived moments showed high correlations (R2 = 0.76 - 0.83) and estimation errors below 30% with respect to reference inverse dynamic moments. Compared to unassisted lifting conditions, exoskeleton liftings showed mean lumbosacral joint moments and compression forces reductions of 11.9 - 18.7 Nm (6 - 12% of peak moment) and 300 - 450 N (5 - 10%), respectively. Our modelling framework was capable of estimating in real-time, valid lumbosacral joint moments and compression forces in line with in vivo experimental data, as well as detecting the biomechanical effects of a passive back-support exoskeleton. Our presented technology may lead to a new class of bio-protective robots in which personalized assistance profiles are provided based on subject-specific musculoskeletal variables.Support Biomechanical Engineerin

Toward higher-performance bionic limbs for wider clinical use

Funding Information: We were supported by the Academy of Finland (I.V.), Austrian Federal Ministry of Science (A.S. and O.C.A.), Bertarelli Foundation (S.M.), the European Union (A.S., D.F., K.-P.H., O.C.A., R.B. and S.M.), the European Research Council (A.S., D.F. and O.C.A.), German Federal Ministry of Education and Research BMBF (K.-P.H. and T.S.), the German National Research Foundation (T.S.), the Royal British Legion (A.M.J.B.), the Swedish Innovation Agency (VINNOVA) (R.B.), the Swedish Research Council (R.B.), the Swiss National Competence Center in Research (NCCR) in Robotics (S.M.), US Department of Defense (R.B. and H.H.), US Department of Veterans Affairs (D.T.), US Department of Veterans Affairs Rehabilitation Research and Development Service (R.F.ff.W.), US National Institute on Disability, Independent Living and Rehabilitation Research (H.H. and T.K.), US National Institutes of Health (D.T., H.H., L.J.H. and R.F.ff.W.), US National Institute on Neurological Disorders and Stroke (R.F.ff.W.), USNational Institute on Bioimaging and Bioengineering (R.F.ff.W.) and US National Science Foundation (H.H.). Publisher Copyright: © 2021, Springer Nature Limited.Most prosthetic limbs can autonomously move with dexterity, yet they are not perceived by the user as belonging to their own body. Robotic limbs can convey information about the environment with higher precision than biological limbs, but their actual performance is substantially limited by current technologies for the interfacing of the robotic devices with the body and for transferring motor and sensory information bidirectionally between the prosthesis and the user. In this Perspective, we argue that direct skeletal attachment of bionic devices via osseointegration, the amplification of neural signals by targeted muscle innervation, improved prosthesis control via implanted muscle sensors and advanced algorithms, and the provision of sensory feedback by means of electrodes implanted in peripheral nerves, should all be leveraged towards the creation of a new generation of high-performance bionic limbs. These technologies have been clinically tested in humans, and alongside mechanical redesigns and adequate rehabilitation training should facilitate the wider clinical use of bionic limbs.Peer reviewe