Table 9.

Average results and standard deviation for the Rule-based (RB) and Neural Network (NN) methods for each exercise (E1, E2, and E3) with LOSO and LOES cross-validation

	Precision	Recall	$F_{1}score$	Hamming loss
Rule-based (RB) Approach
Leave-One-Subject-Out (LOSO) cross-validation
$\text{E1}$	0.75 ± 0.14	0.78 ± 0.12	0.76 ± 0.12	0.11 ± 0.07
$\text{E2}$	$0.54\pm 0.17$	$0.65\pm 0.17$	$0.59\pm 0.16$	$0.20\pm 0.08$
$\text{E3}$	$0.69\pm 0.27$	$0.71\pm 0.26$	$0.70\pm 0.26$	0.13 ± 0.11
Neural Network (NN) based Approach
Leave-One-Subject-Out (LOSO) cross-validation
$\text{E1}$	$0.71\pm 0.23$	$0.70\pm 0.25$	$0.70\pm 0.24$	$0.18\pm 0.15$
$\text{E2}$	0.73 ± 0.21	0.73 ± 0.19	0.73 ± 0.19	0.15 ± 0.11
$\text{E3}$	0.80 ± 0.22	0.80 ± 0.21	0.80 ± 0.22	$0.14\pm 0.14$
Leave-One-Exercise-Out (LOEO) cross-validation with E1 and E2
	$0.78\pm 0.05$	$0.81\pm 0.01$	$0.80\pm 0.02$	$0.12\pm 0.01$

The results in bold correspond to the best classifiers' performance for the different metrics for each exercise

F₁ score is a measure of accuracy