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Develop a home-used EMG sensor system to identify pathological gait with less data via frequency analysis.

In order to develop a low-cost wearable electromyography (EMG) sensor system that can be used at home, compacting the data size is studied first to extract potential features via frequency analysis. A low-cost wearable home-use EMG sensor is then developed. Results show the frequency band at 40-60 Hz of tibialis anterior offers significant differences to identify walking problems (p-value < 0.05), which can be used as a detection standard with a smaller data size. More significantly, data sizes are dramatically reduced by 95.06% compared to the original data size. This finding suggests a potential examining method for identifying pathological gait with the compacted data in satisfactory processing time that can be used at home.