A New Approach to Noninvasive-Prolonged Fatigue Identification Based on Surface EMG Time-Frequency and Wavelet Features

Table 5

Classification results of prolonged fatigue based on the naïve Bayes method.

Parameter		Muscles
Features	Performance	BF	RF	VL	VM
Time features (ΔMAV, ΔRMS)	Accuracy (%)	70	78	64	56
	Specificity (%)	100	84	83	97
	Precision (%)	0	67	36	0
	CVErr	0.31	0.25	0.43	0.44
Frequency features (ΔFmed, ΔFmean)	Accuracy (%)	86	95	68	77
	Specificity (%)	88	94	89	83
	Precision (%)	79	96	36	69
	CVErr	0.15	0.04	0.39	0.23
Time and frequency features (ΔMAV, ΔRMS, ΔFmed, ΔFmean)	Accuracy (%)	94	98	95	97
	Specificity (%)	97	100	100	97
	Precision (%)	86	96	88	96
	CVErr	0.06	0.01	0.07	0.02
Wavelet index features (ΔWIRM1551, ΔWIRM1M51, ΔWIRM1522, ΔWIRE51, ΔWIRW51)	Accuracy (%)	82	91	80	66
	Specificity (%)	85	93	78	71
	Precision (%)	77	89	84	58
	CVErr	0.18	0.09	0.23	0.38
Time, frequency, and wavelet index features (ΔMAV, ΔRMS, ΔFmed, ΔFmean, ΔWIRM1551, ΔWIRM1M51, ΔWIRM1522, ΔWIRE51, ΔWIRW51)	Accuracy	87	88	77	90
	Specificity	86	91	79	83
	Precision	89	85	80	100
	CVErr	0.16	0.15	0.23	0.2