This paper presents the analytical study of flexible stimulation waveforms in muscle fatigue reduction for functional electrical stimulator (FES)-assisted hemiplegic muscle activities. The major challenge of muscle contraction induced by FES is early muscle fatigue which greatly limits activities such as FES-assisted standing and walking. The fixed stimulation pattern applied on a same motor unit has resulted the motor unit to be overworked and fatigue easily. Therefore, in this work, the stimulus parameters, which include the pulse width duration and the frequency were varied to create a few flexible stimulation waveforms using MATLAB/Simulink. The pulse width duration was modulated from 100µs -500µs to generate five types of flexible stimulation waveforms such as Rectangular, Trapezoidal, Ramp Up, Ramp Down and Triangular. Concurrently, a few ranges of stimulus frequency were also used, which include 20Hz, 30Hz and 50Hz. The generated flexible stimulation waveforms were applied onto a humanoid muscle model to investigate and analyse the muscle output response and early muscle fatigue reduction. From the conducted simulation results and analyses, it was observed that flexible stimulation waveforms such as Triangular, Ramp Up and Ramp Down could reduce early muscle fatigue phenomenon by having lower average of negative slope, in the range of 0.012 to 0.013 for the muscle fitness. In contrast, the Rectangular and Trapezoidal shapes were found to have higher negative slope of muscle fitness in the range of 0.028 to 0.031. The Ramp Down shape was found to have the lowest average of negative slope (0.012) while Rectangular was found to have the highest average of negative slope (0.031). Therefore, it can be concluded that flexible stimulation waveforms such Ramp Down, Ramp Up and Triangular shapes could reduce early muscle fatigue phenomenon with Ramp Down shape having the highest muscle fatigue reduction.

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Functional electrical stimulation (FES) device has been widely used by spinal cord injury (SCI) patients in their rehab exercises to restore motor function to their paralysed muscles. The major challenge of muscle contraction induced by FES is early muscle fatigue due to the open-loop stimulation strategy. To reduce the early muscle fatigue phenomenon, a closed-loop FES system is proposed to track the angle of the limb’s movement and provide an accurate amount of charge according to the desired reference angle. Among the existing feedback controllers, fuzzy logic controller (FLC) has been found to exhibit good control performance in handling complex non-linear systems without developing any complex mathematical model. Recently, there has been considerable interest in the implementation of FLC in hardware embedded systems. Therefore, in this paper, a digital fuzzy feedback controller (FFC) embedded in a field-programmable gate array (FPGA) board was proposed. The digital FFC mainly consists of an analog-to-digital converter (ADC) Data Acquisition and FLC sub-modules. The FFC was designed to monitor and control the progress of knee extension movement by regulating the stimulus pulse width duration to meet the target angle. The knee is expected to extend to a maximum reference angle setting (70°, 40° or 30°) from its normal position of 0° once the stimulus charge is applied to the muscle by the FES device. Initially, the FLC was modelled using MATLAB Simulink. Then, the FLC was hardcoded into digital logic using hardware description language (HDL) Verilog codes. Thereafter, the performance of the digital FLC was tested with a knee extension model using the HDL co-simulation technique in MATLAB Simulink. Finally, for real-time verification, the designed digital FFC was downloaded to the Intel FPGA (DE2-115) board. The digital FFC utilized only 4% of the total FPGA (Cyclone IV E) logic elements (LEs) and required 238 µs to regulate stimulus pulse width data, including 3 µs for the FLC computation. The high processing speed of the digital FFC enables the stimulus pulse width duration to be updated every stimulation cycle. Furthermore, the implemented digital FFC has demonstrated good control performance in accurately controlling the stimulus pulse width duration to reach the desired reference angle with very small overshoot (1.4°) and steady-state error (0.4°). These promising results are very useful for a real-world closed-loop FES application.

Functional electrical stimulation (FES) has been widely used to treat spinal cord injury (SCI) patients. Many research studies employ a closed-loop FES system to monitor the stimulated muscle response and provide a precise amount of charge to the muscle. However, most closed-loop FES devices perform poorly and sometimes fail when muscle nonlinearity effects such as fatigue, time delay response, stiffness, spasticity, and subject change happen. The poor performance of the closed-loop FES device is mainly due to discrepancies in the feedback control algorithms. Most of the existing feedback control algorithms were not designed to adapt to new changes in patients with different nonlinearity effects, resulting in early muscle fatigue. Therefore, this research proposes an adaptive sliding mode (SM) feedback control algorithm that could adapt and fine-tune internal control settings in real-time according to the current subject’s nonlinear and time-varying muscle response during the rehabilitation (knee extension) exercise. The adaptive SM feedback controller consists mainly of system identification, direct torque control, and tunable feedback control settings. Employing the system identification unit in the feedback control algorithm enables real-time self-tuning and adjusting of the SM internal control settings according to the current patient’s condition. Initially, the patient’s knee trajectory response was identified by extracting meaningful information, which included time delay, rise time, overshoot, and steady-state error. The extracted information was used to fine-tune and update the internal SM control settings. Finally, the performance of the proposed adaptive SM feedback control algorithm in terms of system response time, stability, and rehabilitation time was analysed using a nonlinear knee model. The findings from the simulation results indicate that the adaptive SM feedback controller demonstrated the best control performance in accurately tracking the desired knee angle movement by having faster response times, smaller overshoots, and lower steady-state errors when compared with the conventional SM across four reference angle settings (20°, 30°, 40°, and 76°). The adaptive feedback SM controller was also observed to compensate for muscle nonlinearities, including fatigue, stiffness, spasticity, time delay, and other disturbances.

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The lower extremities of a humanoid model having the parameters of stroke patient is designed to promote an understanding of its paralysis behaviour. The assemblies of the model are designed using SolidWork, a three dimensional computer-aided design (CAD) software and converted into SimMechanics models for further simulations. The humanoid lower extremities model is designed based on anthropometric data of a healthy human and modified accordingly to changes of body composition and weight of stroke patients. Difference of 0.56 kg between paretic and non-paretic leg might gives significant results in control simulations. The developed model can offer prediction and much insight into the nature of movement generation and behaviour of the real stroke system.

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This paper presents the model development of Functional Electrical Stimulation (FES)-assisted wheel-chaired elliptical stepping exercise. This exercise specially designed for persons with hemiplegic or spinal cord injury (SCI). The design involves the development of humanoid, wheelchair and elliptical stepping machine models. A feedback control system is used to implement the control and coordinate the exercise movement. Knee extension of the subject is initiate to lead the elliptical stepping movement. The torque production and cadence speed of the exercise is observed at different setting of the control gain. The result obtained shows that the implementation of the exercise required an adequate amount of torque applied to produce the well coordinate knee movement for left and right knee. Integration with muscle model and implementation of more advance control system is required for further improvement to the exercise model.