Subsensory electrical noise stimulation applied to the lower trunk improves postural control during visual perturbations.

BACKGROUND: Low levels of sensory noise applied to the skin through electrical stimulation (ES) can improve balance control through a mechanism called stochastic resonance (SR). Little is known regarding the extent subsensory ES can improve reactive control of balance after unanticipated balance perturbations and the best location where to apply the stimulation. RESEARCH QUESTIONS: How efficient is subsensory ES in improving reactive control of balance following visual perturbations delivered in a virtual reality (VR) environment? 2) Does lower trunk stimulation have greater effects than lower legs stimulation? METHODS: Eighteen healthy young adults stood on a force plate while wearing a Valve Index VR headset in eyes closed (EC), eyes open (EO), eyes open with anteroposterior visual perturbations (AP) and eyes open with mediolateral visual perturbations (ML) conditions. No-stimulation (NS), leg stimulation (LS), or trunk stimulation (TS) equal to 90% of the sensory threshold (ST) was applied. The 95% confidence ellipse area (95%EA), the lengths of AP and ML sway path (APPath, MLPath), and the AP and ML 50% and 95% power frequencies (APPF50, MLPF50, APPF95, and MLPF95) were calculated. Repeated-measures ANOVA and Tukey post-hoc tests were used to analyze the main and interaction effects of stimulation and visual conditions. RESULTS: During AP perturbations, participants showed higher frequencies, longer paths, and larger ellipse areas. TS caused lower APPF50, MLPF50, MLPF95, APPath and EA while LS caused lower MLPF50 and EA. During ML perturbations, TS reduced APPF50 and both LS and TS caused reduction of MLPF95. Higher instability following AP perturbations was associated with greater effects of TS and LS. SIGNIFICANCE: The application of subsensory ES improved postural control during AP perturbations and TS reduced postural sway more effectively than LS. TS may be an effective strategy to enhance balance control during reactive postural tasks, thus potentially reducing fall risk.

FES Cycling in Stroke: Novel Closed-Loop Algorithm Accommodates Differences in Functional Impairments.

OBJECTIVE: The objective of this paper was to develop and test a novel control algorithm that enables stroke survivors to pedal a cycle in a desired cadence range despite varying levels of functional abilities after stroke. METHODS: A novel algorithm was developed which automatically adjusts 1) the intensity of functional electrical stimulation (FES) delivered to the leg muscles, and 2) the current delivered to an electric motor. The algorithm automatically switches between assistive, uncontrolled, and resistive modes to accommodate for differences in functional impairment, based on the mismatch between the desired and actual cadence. Lyapunov-based methods were used to theoretically prove that the rider's cadence converges to the desired cadence range. To demonstrate the controller's real-world performance, nine chronic stroke survivors performed two cycling trials: 1) volitional effort only and 2) volitional effort accompanied by the control algorithm assisting and resisting pedaling as needed. RESULTS: With a desired cadence range of 50-55 r/min, the developed controller resulted in an average rms cadence error of 1.90 r/min, compared to 6.16 r/min during volitional-only trials. CONCLUSION: Using FES and an electric motor with a two-sided cadence control objective to assist and resist volitional efforts enabled stroke patients with varying strength and abilities to pedal within a desired cadence range. SIGNIFICANCE: A protocol design that constrains volitional movements with assistance and resistance from FES and a motor shows potential for FES cycles and other rehabilitation robots during stroke rehabilitation.

Distributed Repetitive Learning Control for Cooperative Cadence Tracking in Functional Electrical Stimulation Cycling.

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.

Controlling the Cadence and Admittance of a Functional Electrical Stimulation Cycle.

For an individual suffering from a neurological condition, 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 human-robot 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 able-bodied 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.

Position and torque control via rehabilitation robot and functional electrical stimulation.

Two common rehabilitation therapies for individuals possessing neurological conditions are functional electrical stimulation (FES) and robotic assistance. This paper focuses on combining the two rehabilitation strategies for use on the biceps brachii muscle group. FES is used to elicit muscle contractions to actuate the forearm and a rehabilitation robot is used to challenge the muscle group in its efforts. Two controllers were developed and implemented to accomplish the multifaceted objective, both of which achieve global exponential stability for position and torque tracking as proven through a Lyapunov stability analysis. Experiments performed on one able bodied individual demonstrate an average RMS error of 5.8 degrees for position tracking and 0.40 Newton-meters for torque tracking.