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A Study of Computing Zero Crossing Methods and an Improved Proposal for EMG Signals

Zero crossings are a practical and efficient feature to approximate the frequency of a sampled series of data. Some research describes in different ways how to compute the zero crossings feature starting from its definition, and in some of them, a threshold is included as part of it. This research compiles a comprehensive list of description methods for zero crossings, both with or without threshold. In addition, an improvement of one method is proposed, mainly to save time resources. Moreover, it increases the precision when the objective is to perform some classification. This feature is often used as a vector of a matrix of features in signal classification. To test the different variations of the zero crossings methods, a classification of electromyographic signals was performed using support vector machines. The results obtained by the proposed method threw near to a 40% improvement in the classification compared to those approaches that do not consider a threshold and more than 7% compared to those with a threshold. The processing time of this work is shortened compared to others that also take into account a threshold.