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Introduction: Functional electrical stimulation (FES) induced cycling has been shown to be an effective rehabilitation for those with lower limb movement disorders. However, a consequence of FES is an electromechanical delay (EMD) existing between the stimulation input and the onset of muscle force. The objective of this study is to determine if the cycle crank angle has an effect on the EMD. Methods: Experiments were performed on 10 participants, five healthy and five with neurological conditions resulting in movement disorders. A motor fixed the crank arm of a FES-cycle in 10 degree increments and at each angle stimulation was applied in a random sequence to a combination of the quadriceps femoris and gluteal muscle groups. The EMD was examined by considering the contraction delay (CD) and the residual delay (RD), where the CD (RD) is the time latency between the start (end) of stimulation and the onset (cessation) of torque. Two different measurements were used to examine the CD and RD. Further, two multiple linear regressions were performed on each measurement, one for the left and one for the right muscle groups. Results: The crank angle was determined to be statistically relevant for both the CD and RD. Conclusions: Since the crank angle has a significant effect on both the CD and RD, the angle has a significant effect on the EMD. Therefore, future efforts should consider the importance of the crank angle when modelling or estimating the EMD to improve control designs and ultimately improve rehabilitative treatments. 

Rehabilitation robotics is an emerging tool for motor recovery from various neurological impairments. However, balancing the human and robot contribution is an open problem. While the motor input can reduce fatigue, which is often a limiting factor of functional electrical stimulation (FES) exercises, too much assistance can slow progress. For a person with a neurological impairment, FES can assist by strategically contracting their muscle(s) to achieve a desired limb movement; however, feasibility can be limited due to factors such as subject comfort, muscle mass, unnatural muscle fiber recruitment, and stimulation saturation. Thus, motor assistance in addition to FES can be useful for prolonging exercise while still ensuring physical effort from the person. In this paper, FES is applied to the biceps brachii to perform biceps curls, and motor assistance is applied intermittently whenever the FES input reaches a pre-set comfort threshold. Exponential stability of the human–robot system is proven with a Lyapunov-like switched systems stability analysis. Experimental results from participants with neurological conditions demonstrate the feasibility and performance of the controller. 

Functional electrical stimulation (FES) induced cycling is a common rehabilitative technique applied for those with a movement disorder. An FES cycle system is a nonlinear switched dynamic system that has a potentially destabilizing input delay between stimulation and the resulting muscle force. In this paper, a dual objective control system for a nonlinear, uncertain, switched FES cycle system with an unknown time-varying input delay is developed and a Lyapunov-like dwell-time analysis is performed to yield exponential power tracking to an ultimate bound and global exponential cadence tracking. Preliminary experimental results for a single healthy individual are provided and demonstrate average power and cadence tracking errors of -0.05 ± 0.80 W and -0.05 ± 1.20 RPM, respectively, for a target power of 10 W and a target cadence of 50 RPM. 

A common rehabilitative technique for those with neuro-muscular disorders is functional electrical stimulation (FES) induced exercise such as FES-induced biceps curls. FES has been shown to have numerous health benefits, such as increased muscle mass and retraining of the nervous system. Closed-loop control of a motorized FES system presents numerous challenges since the system has nonlinear and uncertain dynamics and switching is required between motor and FES control, which is further complicated by the muscle having an uncertain control effectiveness. An additional complication of FES systems is that high gain feedback from traditional robust controllers can be uncomfortable to the participant. In this paper, data-based, opportunistic learning is achieved by implementing an integral concurrent learning (ICL) controller during a motorized and FES-induced biceps curl exercise. The ICL controller uses adaptive feedforward terms to augment the FES controller to reduce the required control input. A Lyapunov-based analysis is performed to ensure exponential trajectory tracking and opportunistic, exponential learning of the uncertain human and machine parameters. In addition to improved tracking performance and robustness, the potential of learning the specific dynamics of a person during a rehabilitative exercise could be clinically significant. Preliminary simulation results are provided and demonstrate an average position error of 0.14 ± 1.17 deg and an average velocity error of 0.004 ± 1.18 deg/s. 

Many people are affected by a wide range of neuromuscular disorders, many of which can be improved through the use of Functional Electrical Stimulation (FES) rehabilitative cycling. Recent improvements in nonlinear, Lyapunov-based FES muscle control with motor assistance in unstimulated regions of the cycle-crank rotation have led to a reduction in muscle fatigue, allowing rehabilitation time to be extended. Studies in rehabilitation have shown that the addition of coordinated movement between the upper limbs and lower limbs can have a positive effect on neural plasticity leading to faster restoration of walking in those who have some neurological disorders. In this paper, to implement coordinated motion during rehabilitation, a strongly coupled bilateral telerobotic system is developed between a hand-cycle system driven by the participant’s volitional efforts and a split-crank leg-cycle system driven by the switched application of FES with motor assistance. A variable operator is applied to the leg-cycle’s motor input during the FES stimulation regions to provide assistance as required. Lyapunov-based analysis methods are used on the combined leg and hand-cycle system to prove global exponential stability. Analysis further proves that all switched system inputs are bounded, thus the states of the telerobotic master (i.e., hand-cycle system) are bounded, therefore, the telerobotic system is stable.