EMG ONSET DETECTION - A HIDDEN FACTOR
AbstractElectromyographic (EMG) examination has gained popularity and was deployed in both practical and research fields. This technology enables one to observe the voltage generated by a neuromuscular system and to interpret body movements. Over time, it has been proved highly difficult to precisely determine the beginning of a muscle contraction. A relatively low signal to noise ratio, which is even smaller in case of a surface electromyography (sEMG), complicates this task, and more advanced onset detection algorithms were proposed. Despite the existence of statistically advanced algorithms, proposed in recent years, in many cases a manual onset detection performed by an EMG specialist remains in use, mainly due to lack of reliable solutions. This paper presents a hidden factor, not found in the literature until now, which directly relates subsequent voltage values with muscle activity. Observation of this parameter helps detect muscle onset precisely. Combined with other statistical factors, it can shed light on a totally new, enhanced branch of muscle onset estimators. This study conducts a numerical comparison to follow the hypothesis that the examined factor is correlated with a muscle contraction state.
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