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

Transcutaneous Functional Electrical Stimulation Controlled by a System of Sensors for the Lower Limbs: A Systematic Review

In the field of transcutaneous functional electrical stimulation (FES), open-loop and closed-loop control strategies have been developed to restore functions of the lower limbs: walking, standing up, maintaining posture, and cycling. These strategies require sensors that provide feedback information on muscle activity or biomechanics of movement. Since muscle response induced by transcutaneous FES is nonlinear, time-varying, and dependent on muscle fatigue evolution, the choice of sensor type and control strategy becomes critical. The main objective of this review is to provide state-of-the-art, emerging, current, and previous solutions in terms of control strategies. Focus is given on transcutaneous FES systems for the lower limbs. Using Compendex and Inspec databases, a total of 135 review and conference articles were included in this review. Recent studies mainly use inertial sensors, although the use of electromyograms for lower limbs has become more frequent. Currently, several researchers are opting for nonlinear controllers to overcome the nonlinear and time-varying effects of FES. More development is needed in the field of systems using inertial sensors for nonlinear control. Further studies are needed to validate nonlinear control systems in patients with neuromuscular disorders.

Human-Robot Cooperative Strength Training Based on Robust Admittance Control Strategy

A stroke is a common disease that can easily lead to lower limb motor dysfunction in the elderly. Stroke survivors can effectively train muscle strength through leg flexion and extension training. However, available lower limb rehabilitation robots ignore the knee soft tissue protection of the elderly in training. This paper proposes a human-robot cooperative lower limb active strength training based on a robust admittance control strategy. The stiffness change law of the admittance model is designed based on the biomechanics of knee joints, and it can guide the user to make force correctly and reduce the stress on the joint soft tissue. The controller will adjust the model stiffness in real-time according to the knee joint angle and then indirectly control the exertion force of users. This control strategy not only can avoid excessive compressive force on the joint soft tissue but also can enhance the stimulation of quadriceps femoris muscles. Moreover, a dual input robust control is proposed to improve the tracking performance under the disturbance caused by model uncertainty, interaction force and external noise. Experiments about the controller performance and the training feasibility were conducted with eight stroke survivors. Results show that the designed controller can effectively influence the interaction force; it can reduce the possibility of joint soft tissue injury. The robot also has a good tracking performance under disturbances. This control strategy also can enhance the stimulation of quadriceps femoris muscles, which is proved by measuring the muscle electrical signal and interaction force. Human-robot cooperative strength training is a feasible method for training lower limb muscles with the knee soft tissue protection mechanism.

Motorless cadence control of standard and low duty cycle-patterned neural stimulation intensity extends muscle-driven cycling output after paralysis

Background

Stimulation-driven exercise is often limited by rapid fatigue of the activated muscles. Selective neural stimulation patterns that decrease activated fiber overlap and/or duty cycle improve cycling exercise duration and intensity. However, unequal outputs from independently activated fiber populations may cause large discrepancies in power production and crank angle velocity among pedal revolutions. Enforcing a constant cadence through feedback control of stimulus levels may address this issue and further improve endurance by targeting a submaximal but higher than steady-state exercise intensity.

Methods

Seven participants with paralysis cycled using standard cadence-controlled stimulation (S-Cont). Four of those participants also cycled with a low duty cycle (carousel) cadence-controlled stimulation scheme (C-Cont). S-Cont and C-Cont patterns were compared with conventional maximal stimulation (S-Max). Outcome measures include total work (W), end power (P_end), power fluctuation (PFI), charge accumulation (Q) and efficiency (η). Physiological measurements of muscle oxygenation (SmO₂) and heart rate were also collected with select participants.

Results

At least one cadence-controlled stimulation pattern (S-Cont or C-Cont) improved P_end over S-Max in all participants and increased W in three participants. Both controlled patterns increased Q and η and reduced PFI compared with S-Max and prior open-loop studies. S-Cont stimulation also delayed declines in SmO2 and increased heart rate in one participant compared with S-Max.

Conclusions

Cadence-controlled selective stimulation improves cycling endurance and increases efficiency over conventional stimulation by incorporating fiber groups only as needed to maintain a desired exercise intensity. Closed-loop carousel stimulation also successfully reduces power fluctuations relative to previous open-loop efforts, which will enable neuroprosthesis recipients to better take advantage of duty cycle reducing patterns.

An Iterative Learning Controller for a Switched Cooperative Allocation Strategy during Sit-to-Stand Tasks with a Hybrid Exoskeleton

A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions via FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this paper, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.

Muscle Electrical Impedance Properties and Activation Alteration After Functional Electrical Stimulation-Assisted Cycling Training for Chronic Stroke Survivors: A Longitudinal Pilot Study

Electrical impedance myography (EIM) is a sensitive assessment for neuromuscular diseases to detect muscle inherent properties, whereas surface electromyography (sEMG) is a common technique for monitoring muscle activation. However, the application of EIM in detecting training effects on stroke survivors is relatively few. This study aimed to evaluate the muscle inherent properties and muscle activation alteration after functional electrical stimulation (FES)-assisted cycling training to chronic stroke survivors. Fifteen people with chronic stroke were recruited for 20 sessions of FES-assisted cycling training (40 min/session, 3–5 sessions/week). The periodically stimulated and assessed muscle groups were quadriceps (QC), tibialis anterior (TA), hamstrings (HS), and medial head of gastrocnemius (MG) on the paretic lower extremity. EIM parameters [resistance (R), reactance (X), phase angle (θ), and anisotropy ratio (AR)], clinical scales (Fugl-Meyer Lower Extremity (FMA-LE), Berg Balance Scale (BBS), and 6-min walking test (6MWT)] and sEMG parameters [including root-mean square (RMS) and co-contraction index (CI) value] were collected and computed before and after the training. Linear correlation analysis was conducted between EIM and clinical scales as well as between sEMG and clinical scales. The results showed that motor function of the lower extremity, balance, and walking performance of subjects improved after the training. After training, θ value of TA (P = 0.014) and MG (P = 0.017) significantly increased, and AR of X (P = 0.004) value and AR of θ value (P = 0.041) significantly increased on TA. The RMS value of TA decreased (P = 0.022) and a significant reduction of CI was revealed on TA/MG muscle pair (P < 0.001). Significant correlation was found between EIM and clinical assessments (AR of X value of TA and FMA-LE: r = 0.54, P = 0.046; X value of TA and BBS score: 0.628, P = 0.016), and between sEMG and clinical scores (RMS of TA and BBS score: r = −0.582, P = 0.029). This study demonstrated that FES-assisted cycling training improved lower limb function by developing coordinated muscle activation and facilitating an orderly myofiber arrangement. The current study also indicated that EIM can jointly evaluate lower extremity function alteration with sEMG after rehabilitation training.

Clinical Trail Registration: The study was registered on the Clinical Trial Registry (trial registration number: NCT 03208439, https://clinicaltrials.gov/ct2/show/NCT03208439).

Encouraging Volitional Pedaling in Functional Electrical Stimulation-Assisted Cycling Using Barrier Functions

Stationary motorized cycling assisted by functional electrical stimulation (FES) is a popular therapy for people with movement impairments. Maximizing volitional contributions from the rider of the cycle can lead to long-term benefits like increased muscular strength and cardiovascular endurance. This paper develops a combined motor and FES control system that tasks the rider with maintaining their cadence near a target point using their own volition, while assistance or resistance is applied gradually as their cadence approaches the lower or upper boundary, respectively, of a user-defined safe range. Safety-ensuring barrier functions are used to guarantee that the rider's cadence is constrained to the safe range, while minimal assistance is provided within the range to maximize effort by the rider. FES stimulation is applied before electric motor assistance to further increase power output from the rider. To account for uncertain dynamics, barrier function methods are combined with robust control tools from Lyapunov theory to develop controllers that guarantee safety in the worst-case. Because of the intermittent nature of FES stimulation, the closed-loop system is modeled as a hybrid system to certify that the set of states for which the cadence is in the safe range is asymptotically stable. The performance of the developed control method is demonstrated experimentally on five participants. The barrier function controller constrained the riders' cadence in a range of 50 ± 5 RPM with an average cadence standard deviation of 1.4 RPM for a protocol where cadence with minimal variance was prioritized and used minimal assistance from the motor (4.1% of trial duration) in a separate protocol where power output from the rider was prioritized.