Analytical study of flexible stimulation waveforms in muscle fatigue reduction

Abstract

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.

Design of an FPGA-Based Fuzzy Feedback Controller for Closed-Loop FES in Knee Joint Model

“…The function of the Fuzzy Error Conversion sub-module is to convert the cris data (from the ADC) into a fuzzification data format (Error (E) and Change in Erro The sampled ADC data ("act_ADC [7:0]") in the range of 0-255 are first convert actual angle ("act_deg [7:0]") in degrees (0°-90°). The mathematical operation for version process is shown in Equation (13).…”

“…The sampled ADC data ("act_ADC [7:0]") in the range of 0-255 are first converted to an actual angle ("act_deg [7:0]") in degrees (0 • -90 • ). The mathematical operation for the conversion process is shown in Equation (13). act_deg = (act_ADC × 90 • )/255 (13) Figure 10 illustrates the FSM state diagram for the sampling and conversion process.…”

“…The mathematical operation for the conversion process is shown in Equation (13). act_deg = (act_ADC × 90 • )/255 (13) Figure 10 illustrates the FSM state diagram for the sampling and conversion process. Once the "sample_data_ADC" is activated, the FSM moves from state Idle to state Sample.…”

“…Besides that, the FES-assisted exercise has also gained interest among clinicians, engineers and researchers. The development of such systems is still on-going, with new innovations and techniques being invented [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ].…”

“…Therefore, the real-world FES device is expected to have the capability to regulate the pulse-to-pulse electrical stimulation in real-time to compensate for fatigue, muscle spasm, and retraining effects [ 1 , 22 , 24 , 25 ]. Several types of feedback controllers have been commonly used in the closed-loop FES system, which include Proportional-Integral-Derivative (PID) controller [ 1 , 27 , 28 , 29 ], Fuzzy Logic [ 7 , 8 , 18 , 19 , 28 , 30 , 31 , 32 , 33 , 34 ], Neuro Fuzzy [ 35 ], Sliding Mode Controller (SMC) [ 1 , 20 , 25 , 27 , 36 , 37 ], Gain Scheduling Controller (GSC) [ 38 ] and Artificial Neural Network (ANN) controller [ 36 , 39 ] and Adaptive Neuro Fuzzy Inference System (ANFIS) [ 13 ].…”