Table 1.

Comparison of accuracy, F-measure, number of Trainable Parameters (#TP), and Training Time (Time) for the three state-of-art DL architectures (1D-CNN, Hybrid, and Mimetic-Enhanced) when trained on different class-labels (i.e. related to App$\times$Act, App, and Act) for different classification tasks. #TP slightly varies with the classification task (with variations being smaller than reported precision). Results are in the format $avg.(\pm std.)$ obtained over 10-folds. Training time was computed by pre-training the individual modalities in parallel. The best result per metric (column) is highlighted in boldface. MM denotes a multi-modal architecture .

Classifier	MM	Training Strategy	Joint-TC		App-TC		Act-TC		#TP [k]	Time [min]
			Accuracy [%]	F-measure [%]	Accuracy [%]	F-measure [%]	Accuracy [%]	F-measure [%]
1D-CNN	$○$	App$\times$Act	$57.94(\pm 0.82)$	$52.84(\pm 0.86)$	$96.12(\pm 0.30)$	$96.66(\pm 0.25)$	$59.95(\pm 0.77)$	$56.12(\pm 0.80)$	4272	$47(\pm 5)$
		App	–	–	$97.48(\pm 0.21)$	$98.05(\pm 0.23)$	–	–	4256	$45(\pm 4)$
		Act	–	–	–	–	$60.18(\pm 1.04)$	$56.45(\pm 1.17)$	4250	$47(\pm 3)$
Hybrid	$○$	App$\times$Act	$61.62(\pm 0.93)$	$57.12(\pm 1.04)$	$94.36(\pm 0.41)$	$95.11(\pm 0.36)$	$64.77(\pm 0.96)$	$63.13(\pm 0.86)$	428	$\mathbf{12 (\pm 4)}$
		App	–	–	$94.85(\pm 0.51)$	$95.62(\pm 0.42)$	–	–	$\mathbf{426}$	$15(\pm 4)$
		Act	–	–	–	–	$63.99(\pm 0.90)$	$62.06(\pm 1.05)$	$\mathbf{426}$	$\mathbf{12 (\pm 4)}$
Mimetic-Enhanced	●	App$\times$Act	$\mathbf{67.12 (\pm 1.14)}$	$\mathbf{62.29 (\pm 1.21)}$	$98.54(\pm 0.21)$	$98.75(\pm 0.18)$	$\mathbf{67.94 (\pm 1.13)}$	$\mathbf{65.33 (\pm 1.15)}$	1235	$57(\pm 6)$
		App	–	–	$\mathbf{98.73 (\pm 0.18)}$	$\mathbf{98.95 (\pm 0.16)}$	–	–	1225	$42(\pm 3)$
		Act	–	–	–	–	$65.37(\pm 0.74)$	$62.60(\pm 1.05)$	1221	$49(\pm 4)$