The wavelet transform seems particularly suited to analyse the electromyographic signal (EMG) during gait of asymptomatic and pathological subjects. Firstly, because physiologically the electrical activity generated by the muscles derives from a weighted sum of individual physiological components having limited support in time and in frequency. Secondly, because it is important to analyze muscle activity during specific phases of the cycle, and finally, because specific ranges of frequency are important pathological discriminators. In this paper we report the preliminary results of a project aimed at classifying asymptomatic and pathological subjects by analysing the complex wavelet transform of the EMG signal derived from two muscles (Tibialis Anterior and Lateral Gastrocnemius) during gait. An asymptomatic adult, an asymptomatic child and two pathological (cerebral palsy) children were examined using telemetric EMG devices and pressure footswitches. The results showed that the indices derived from the coefficient amplitudes (Gastrocnemius) and from frequency distribution (Tibialis) are capable of classifying the subjects into three groups. Despite the small number of cases analyzed, we believe that the relevance of the results deserves particular attention because of the novelty of the use of the wavelet transform for this application and of the potential application to monitor patients during interventions aimed at improving muscle behavior, particularly antispasticity treatment such as Botulinum Toxin injections
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