Patients with spinal cord injury (SCI) benefit from muscle training with functional electrical stimulation (FES). For safety reasons and to optimize training outcome, the fatigue state of the target muscle must be monitored. Detection of muscle fatigue from mel frequency cepstral coefficient (MFCC) feature of mechanomyographic (MMG) signal using support vector machine (SVM) classifier is a promising new approach. Five individuals with SCI
The idea of an FES is to stimulate the contraction of paralyzed muscles by inducing electrical pulses. Recent studies have demonstrated that the intervention of Functional Electrical Stimulation (FES) have improved patients with paralyzed muscle injuries. Unfortunately, due to the high cost of an FES device, rehabilitation centers in local hospitals are not equipped with FES devices. Generally, an FES device consists of electrodes and a stimulator. The stimulator of an FES device will act as the main controller that will provide with the activation or stimulation functions. This paper investigates the parameters that need to be addressed in designing an FES stimulator. While there are many different types of electrodes to be used in FES system, for this early work, we only look at FES systems application using only skin surface electrodes (non-invasive).
Functional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.
While electrical stimulation has proven to produce positive outcome among patients, electrical stimulation in post stroke rehabilitation faced one main limitation – muscle fatigue. Muscle fatigue limits the training time, hence, affecting the recovery process. This work analyzes the occurrence of muscle fatigue with respect to the different stimulator parameters; amplitude, pulse shape and frequency. The detection of muscle fatigue will be monitored as force where the force sensitive resistor (FSR) sensors are used to detect the variations of the force exerted by the muscle. In this work, it is assumed that the fatigue in muscle will happen when the force recorded reduces to 60% from its initial force over the stimulation period. The experiment results proof that the stimulator parameters, amplitude, pulse shape and frequency, have different effect to the upper limb muscle with respect to muscle fatigue. The results also show that the exponential waveform can be considered in future rehabilitation program since the onset of muscle fatigue is delayed later in the experiment when compared to the other signals. This allows extended training duration among stroke patients to benefit from the recovery window time frame especially for patients who are still in their early recovering stage.
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