Synergy-Driven Musculoskeletal Modeling to Estimate Muscle Excitations and Joint Moments at Different Walking Speeds in Individuals with Transtibial Amputation.

The main requirement for an amputee is to regain the function of the lost limb. In order to fully benefit from powered prosthetic legs, the user must rely on the dynamic control of the device. Progress in high-level control for powered prosthetic legs is currently challenged by the inability of current control schemes to generalize to large repertoires of movements as well as adapting to external mechanical demands. This ultimately leads the user to adopt compensatory movements, lack of comfort, higher energy requirements during walking and standing. This study uses a feedforward model of muscle activation and force generation that applies mathematical formulations of muscle synergies to generate synthetic activation profiles underlying walking across different speeds. Estimated activation profiles are used to drive forward subject-specific numerical models of the lower extremity musculoskeletal system. The model was validated on one individual with uni-lateral transtibial amputation and its predictions were compared to experimental torques from inverse dynamic calculations. Results showed that a generic muscle synergy driven personalized musculoskeletal model can fit the ankle torques of the intact limb of a person with transtibial amputation (RMSD = 0.1329±0.02). The estimated moments might be suitable as the control signal to drive powered prostheses to ultimately improve physical interaction between the user and a powered prostheses during dynamic motor tasks.

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review.

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

A Wearable Real-time Kinematic and Kinetic Measurement Sensor Setup for Human Locomotion.

Current laboratory-based setups (optical marker cameras + force plates) for human motion measurement require participants to stay in a constrained capture region which forbids rich movement types. This study established a fully wearable system, based on commercially available sensors (inertial measurement units + pressure insoles) that can measure both kinematic and kinetic motion data simultaneously and support wireless frame-by-frame streaming. In addition, its capability and accuracy were tested against a conventional laboratory-based setup. An experiment was conducted, with 9 participants wearing the wearable measurement system and performing 13 daily motion activities, from slow walking to fast running, together with vertical jump, squat, lunge and single-leg landing, inside the capture space of the laboratory-based motion capture system. The recorded sensor data were post-processed to obtain joint angles, ground reaction forces (GRFs), and joint torques (via multi-body inverse dynamics). Compared to the laboratory-based system, the established wearable measurement system can measure accurate information of all lower limb joint angles (Pearson's r = 0.929), vertical GRFs (Pearson's r = 0.954), and ankle joint torques (Pearson's r = 0.917). Center of pressure (CoP) in the anterior-posterior direction and knee joint torques were fairly matched (Pearson's r = 0.683 and 0.612, respectively). Calculated hip joint torques and measured medial-lateral CoP did not match with the laboratory-based system (Pearson's r = 0.21 and 0.47, respectively). Furthermore, both raw and processed datasets are openly accessible (https://doi.org/10.5281/zenodo.6457662). Documentation, data processing codes, and guidelines to establish the real-time wearable kinetic measurement system are also shared (https://github.com/HuaweiWang/WearableMeasurementSystem).

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.

Estimation of Time-Varying Ankle Joint Stiffness Under Dynamic Conditions via System Identification Techniques.

An important goal in the design of next-generation exoskeletons and limb prostheses is to replicate human limb dynamics. Joint impedance determines the dynamic relation between joint displacement and torque. Joint stiffness is the position-dependent component of joint impedance and is key in postural control and movement. However, the mechanisms to modulate joint stiffness are not fully understood yet. The goal of this study is to conduct a systematic analysis on how humans modulate ankle stiffness. Time-varying stiffness was estimated for six healthy subjects under isometric, as well as quick and slow dynamic conditions via system identification techniques; specifically, an ensemble-based algorithm using short segments of ankle torque and position recordings. Our results show that stiffness had the lowest magnitude under quick dynamic conditions. Under isometric conditions, with fixed position and varying muscle activity, stiffness exhibited a higher magnitude. Finally, under slow dynamic conditions, stiffness was found to be the highest. Our results highlight, for the first time, the variability in stiffness modulation strategies across conditions, especially across movement velocity.

Model-Based Estimation of Ankle Joint Stiffness During Dynamic Tasks: a Validation-Based Approach.

Joint stiffness estimation under dynamic conditions still remains a challenge. Current stiffness estimation methods often rely on the external perturbation of the joint. In this study, a novel 'perturbation-free' stiffness estimation method via electromyography (EMG)-driven musculoskeletal modeling was validated for the first time against system identification techniques. EMG signals, motion capture, and dynamic data of the ankle joint were collected in an experimental setup to study the ankle joint stiffness in a controlled way, i.e. at a movement frequency of 0.6 Hz as well as in the presence and absence of external perturbations. The model-based joint stiffness estimates were comparable to system identification techniques. The ability to estimate joint stiffness at any instant of time, with no need to apply joint perturbations, might help to fill the gap of knowledge between the neural and the muscular systems and enable the subsequent development of tailored neurorehabilitation therapies and biomimetic prostheses and orthoses.

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

OBJECTIVE: 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: 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. MAIN 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.

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms.

OBJECTIVE: Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses with biomechanical models not operating in real-time for man-machine interfacing. 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.