Real-Time Ultrasound Imaging of a Human Muscle to Optimize Shared Control in a Hybrid Exoskeleton

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.