Visual Feedback Cycling System for Stroke Patient and Real-Time Electromyography Processing Algorithm during Electrical Stimulation

• Main Authors: YIN, CHIEH, 殷傑
• Other Authors: HSUEH, YA-HSIN
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/53ugn2

博士 === 國立雲林科技大學 === 電子工程系 === 105 === Strokes many result in motor function impairment, which is observed frequently, among hemiplegia patients. These stroke patients have difficulty in moving their upper limbs, controlling hand movement, cause imbalance and lose the walking abilities in lower limbs due to brain injury. Functional electrical stimulation (FES) has proven to be a good tool to improve strength of the muscle and cycling benefits symmetry and walking abilities, the use of a foot plate can evaluate standing symmetry ability. However, during cycle training, the unaffected limb tends to compensate for the affected one, which resulted in suboptimal rehabilitation. To address this issue, we developed a Virtual Reality-Cycling Training System (VRCTS) which senses the cycling force and speed in real-time, analyzes the acquired data to produce feedback to the patient to induce particular activities using a controllable VR car within a VR rehabilitation program to specifically trains the affected side. The aim of the study was to verify the functionality of the VRCTS from the ten stroke patients, were compared to the results of the Asymmetry Ratio Index (ARI) between the experimental group and the control group.We also use the bilateral pedal force and force plate to determine any training effect. Our results showed that, after the VRCTS training, bilateral pedal force was improved by 0.22 (?? = 0.046) and, on the force plate, standing balance had improved by 0.29 (?? = 0.031) showing a significant difference. During Electrical Stimulation (ES) the muscle contraction will be induced, the EMG will become a hybrid EMG signal, which increases the difficulty to analyze. The Hybrid EMG Baseline Remove (HEBR) method has been developed to improve the analysis in real-time EMG. The volitional EMG is separated from hybrid EMG signal and, also, removes the SA (Stimulus Artifact). In the open palm ES feedback control training, the vEMG is able to be used as a control signal, the result from the open palm experiment in the pretest and during ES are compared by the pair-t static test, which showed no significant difference (p=0.047). In the Correlation Coefficient method, between the HEBR volitional EMG and the pretest, the open palm movement EMG is 0.9 and the Mean Squared Error (MSE) is 22 shows that the HEBR volitional EMG is very similar to the pretest open palm movement EMG.