The nonlinear dynamics required to model walking with multi-joint lower limb exoskeleton assistance results in high computational burden. To address this, we derive a Koopman-based linearized model of the human-exoskeleton system using electromyography and ultrasound-derived metrics of volitional muscle activity during exoskeleton-assisted walking. Data are collected from one participant with spinal cord injury (SCI) and two participants with no disabilities. Various electromyography and ultrasound-derived features in addition to normalized motor currents are used to derive predictive models, and we identify which muscle activation metrics produce the most accurate model for each subject. For both subjects without disabilities, the most accurate model uses only ultrasound-derived echogenicity as a metric of muscle activity, while the most accurate model for the subject with SCI uses only EMG wave length. Furthermore, the inclusion of ground reaction force increases the prediction accuracy of all models for one participant with no disabilities while decreasing the accuracy of most models for the participant with SCI. For all subjects, the most accurate subject-speclfic linear model has a root-mean-square error (averaged across limb segment angles) of < 8°.

A hybrid exoskeleton is a class of wearable robotic technology that simultaneously uses a powered exoskeleton and functional electrical stimulation (FES) to generate assistive joint torques for people with impaired mobility due to neurological disorders such as spinal cord injury (SCI). The hybrid assistive technology benefits from FES that actively elicits force from paralyzed muscles via their neural excitation, leading to muscle strengthening. The main technical barrier to realizing the hybrid technology is to attain stable coordination between FES and the exoskeleton despite the quick onset of FES-induced muscle fatigue, which causes a rapid decline in the muscle force. Current methods to measure the induced fatigue lack direct muscle state measurements and may be ineffective at capturing the muscle force decay due to FES. Instead, ultrasound (US) imaging accurately quantifies FES-related muscle contractility and fatigue due to the direct visualization of muscle fibers. In this article, we use real-time US imaging-derived muscle strain changes as biomarkers of FES-induced fatigue in an optimal controller that modulates exoskeleton assistance and FES dosage. To demonstrate that real-time US imaging is a promising muscle–machine interface technology that can optimize shared control in a hybrid exoskeleton, we perform experiments involving continuous seated knee extension and over-ground walking tasks on two participants with SCI and four participants without disabilities. Furthermore, this work helps design a novel and unprecedented robotic gait technology with the capability to impart FES-associated therapeutic benefits while assisting the gait of neurologically impaired individuals, including those with SCI, stroke, multiple sclerosis, etc.

This article presents an assist-as-needed (AAN) control framework for exoskeleton assistance based on human volitional effort prediction via a Hill-type neuromuscular model. A sequential processing algorithm-based multirate observer is applied to continuously estimate muscle activation levels by fusing surface electromyography (sEMG) and ultrasound (US) echogenicity signals from the ankle muscles. An adaptive impedance controller manipulates the exoskeleton's impedance for a more natural behavior by following a desired intrinsic impedance model. Two neural networks provide robustness to uncertainties in the overall ankle joint-exoskeleton model and the prediction error in the volitional ankle joint torque. A rigorous Lyapunov-based stability analysis proves that the AAN control framework achieves uniformly ultimately bounded tracking for the overall system. Experimental studies on five participants with no neurological disabilities walking on a treadmill validate the effectiveness of the designed ankle exoskeleton and the proposed AAN approach. Results illustrate that the AAN control approach with fused sEMG and US echogenicity signals maintained a higher human volitional effort prediction accuracy, less ankle joint trajectory tracking error, and less robotic assistance torque than the AAN approach with the sEMG-based volitional effort prediction alone. The findings support our hypotheses that the proposed controller increases human motion intent prediction accuracy, improves the exoskeleton's control performance, and boosts voluntary participation from human subjects. The new framework potentially paves a foundation for using multimodal biological signals to control rehabilitative or assistive robots.