For an individual suffering from a neurological disorder such as spinal cord injury, traumatic brain injury, or stroke, motorized functional electrical stimulation (FES) cycling is a rehabilitation strategy which offers numerous health benefits. Motorized FES cycling is an example of physical humanrobot interaction in which both systems must be controlled; the human is actuated by applying neuromuscular electrical stimulation to the large leg muscle groups and the cycle is actuated through its onboard electric motor. While the rider is stimulated using a robust sliding-mode controller, the cycle utilizes an admittance controller to preserve rider safety. The admittance controller is shown to be passive with respect to the rider, and the cadence controller is shown to be globally exponentially stable through a Lyapunov-like switched systems stability analysis. Experiments are conducted on three ablebodied participants and four participants with neurological conditions (NCs) to demonstrate the efficacy of the developed controller and investigate the effect of manipulating individual admittance parameters. Results demonstrate an average admittance cadence error of -0.06±1.47 RPM for able bodied participants and -0.02±0.93 RPM for participants with NCs.

Closed-loop control of functional electrical stimulation coupled with motorized assistance to induce cycling is a rehabilitative strategy that can improve the mobility of people with neurological conditions (NCs). However, robust control methods, which are currently pervasive in the cycling literature, have limited effectiveness due to the use of high stimulation intensity leading to accelerated fatigue during cycling protocols. This paper examines the design of a distributed repetitive learning controller (RLC) that commands an independent learning feedforward term to each of the six stimulated lower-limb muscle groups and an electric motor during the tracking of a periodic cadence trajectory. The switched controller activates lower limb muscles during kinematic efficient regions of the crank cycle and provides motorized assistance only when most needed (i.e., during the portions of the crank cycle where muscles evoke a low torque output). The controller exploits the periodicity of the desired cadence trajectory to learn from previous control inputs for each muscle group and electric motor. A Lyapunov-based stability analysis guarantees asymptotic tracking via an invariance-like corollary for nonsmooth systems. The switched distributed RLC was evaluated in experiments with seven able-bodied individuals and five participants with NCs. A mean root-mean-squared cadence error of 3.58 ± 0.43 revolutions per minute (RPM) (0.07 ± 7.35% average error) and 4.26 ± 0.84 RPM (0.1 ± 8.99% average error) was obtained for the healthy and neurologically impaired populations, respectively.

Motorized functional electrical stimulation (FES) cycling is a rehabilitation therapy in which electrical stimulation is used to activate lower body muscles to pedal a cycle in conjunction with motorized assistance. FES cycling has been demonstrated to improve cardiovascular parameters, muscle mass, and motor control of people with neurological conditions (NCs). A common FES cycling objective is simultaneous cadence and power (torque) tracking; however, it is unclear how to best coordinate the FES and motor contributions and if power should be tracked instantaneously or averaged over a period of time. This article develops a new FES cycling controller using a switched Lyapunov-like dwell-time analysis for the nonlinear, uncertain cycling system to conclude global exponential cadence tracking and uniformly ultimately bounded power tracking. To evaluate the performance of the developed controller, comparisons are made with two previously developed FES cycling controllers through experiments on seven able-bodied participants and six participants with NCs. For a desired cadence of 50 RPM and a desired power of 10 W, the developed controller demonstrated the smallest tracking errors with an average cadence and power error of 0.01±1.03 RPM and 0.00±0.94 W, respectively, in the able-bodied population and an average cadence and power error of 0.02±1.87 RPM and 0.00±2.46 W, respectively, in the population of people with NCs. Results suggest that the electric motor should be used to track cadence and the FES induced muscle torques should be used to track instantaneous power.

Functional electrical stimulation (FES) can help individuals with physical disabilities by assisting limb movement; however, the change in muscle geometry associated with limb movement may affect the response to stimulation. The aim of this paper was to quantify the effects of elbow flexion and stimulation site on muscle torque production. Contraction torque about the elbow was measured in 12 healthy individuals using a custom elbow flexion testbed and a transcutaneous electrode array. Stimulation was delivered to six distinct sites along the biceps brachii over 11 elbow flexion angles. Flexion angle was found to significantly influence the optimal (i.e., torque-maximizing) stimulation site (χ ² (10, N = 24) = 135.75, p = 3.12 × 10 ^-24 ), with post hoc analysis indicating a proximal shift in optimal stimulation site with increased flexion. Similarly, the biceps stimulation site was found to significantly influence the flexion angle atwhich peaktorque occurred (χ ² (5, N = 24) = 101.82,p = 2.18 × 10 ^-20 ), with post hoc analysis indicating an increase in peak-torque flexion angle as stimulation site is moved proximally up the biceps. Since maximizing muscle force per unit stimulation is a common goal in rehabilitative FES, future efforts could examine methods which compensate for the shift in optimal stimulation site during FES-induced limb movement.

For an individual affected by a neuromuscular condition (NC), functional electrical stimulation (FES)-induced cycling provides a means of functional restoration and therapeutic exercise. Although FES-cycling has been shown to have numerous benefits, there are challenges to implementing closed-loop FES control for coordinated motion. For example, there exists a potentially destabilizing input delay between the application (or removal) of stimulation and the resulting muscle force. Moreover, switching between multiple actuators (such as FES or motor control) can also be destabilizing. This brief develops delay-dependent switching conditions and a robust control method to account for an unknown time-varying input delay of a switched system. A Lyapunov-like analysis is performed to yield semiglobal exponential cadence tracking to an ultimate bound. Experiments were performed on six able-bodied participants and four participants with NCs to validate the developed controller. The proposed controller resulted in an average cadence error of 0.01 ± 2.00 revolutions per minute (RPM) for the able-bodied participants and 0.01 ± 2.72 RPM for participants with NCs.

Motorized functional electrical stimulation (FES) cycling is a promising rehabilitation strategy for individuals with movement disorders, particularly when the pedals of the FES cycle are decoupled to measure and address asymmetries. In this article, a rehabilitation robot, i.e., a split-crank FES cycle, is developed which utilizes a combined admittance-cadence controller to address rider asymmetries through adaptation, ensure rider safety, and electrically stimulate the rider’s leg muscles to pedal the cycle at the desired cadence. The theoretical development of the controllers is based on a combined Lyapunov-passivity switched systems stability analysis. Experiments were conducted on one able-bodied participant and three participants with various movement disorders, resulting in an average admittance tracking error of −0.13 ± 1.77 RPM with adaptation and −0.03 ± 4.05 RPM without adaptation. The split-crank FES cycle successfully admits to the rider, preserves rider safety, and offers a promising robotic rehabilitation strategy for individuals affected by movement disorders.