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"cells" : [
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{
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"cell_type" : " code" ,
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- "execution_count" : 25 ,
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+ "execution_count" : null ,
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"metadata" : {},
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"outputs" : [
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{
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" Root Mean Square (RMS), and Waveform Length (WL).\n " ,
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" \"\"\"\n " ,
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" features = {\n " ,
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- " 'mean_absolute_value': np.mean(np.abs(segment)), # MAV : signal strength \n " ,
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- " 'root_mean_square': np.sqrt(np.mean(segment**2)), # RMS: provides insight into signal energy \n " ,
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- " 'waveform_length': np.sum(np.abs(np.diff(segment))), # WL: measures the signal's variation \n " ,
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+ " 'mean_absolute_value': np.mean(np.abs(segment)), # reflecting muscle contraction intensity \n " ,
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+ " 'root_mean_square': np.sqrt(np.mean(segment**2)), # indicating muscle force. \n " ,
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+ " 'waveform_length': np.sum(np.abs(np.diff(segment))), # providing insights of muscle activity duration & intensity. \n " ,
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" }\n " ,
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" return features\n " ,
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" \n " ,
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" Calculates the Zero Crossing Rate, which is useful to measure signal complexity.\n " ,
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" \"\"\"\n " ,
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" return np.sum(np.diff(np.sign(segment)) != 0) / len(segment) # Measures the number of times the signal crosses zero\n " ,
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+ " # Which indicates signal smoothness and contraction consistency.\n " ,
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" \n " ,
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" def autocorrelation(segment):\n " ,
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" \"\"\"\n " ,
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