Social Touch Gesture Recognition Using Convolutional Neural Network

Table 4

Comparison of features from other existing classification methods applied on same dataset.

No.	Reference	Features extracted	# Subject	# Touch	# Features	Classify method	Accuracy (%)	S.D. (%)
1	[8]	Yes	31	14	28	Bayesian classifier	53	11
						SVM	46	9
2	[9]	Yes	31	14	28	Bayesian classifier	54	12
						SVM	53	11
3	[10]	Yes	31	14	45	Neural network	54	15
4	[11]	Yes	31	14	42	Random forests (RF)	55.6	13
5	[12]	Yes	31	14	5 set	Random forests (RF)	59
						Boosting	58
6	[13]	Yes	31	14	7	Deep autoencoders	56
7	[15]	Yes	31	14	273	SVM	60.5
						Random forests (RF)	60.8
8	[17]	Yes	31	14	54	Bayesian classifier	57	11
						Decision tree algorithm	48	10
						SVM	60	11
						Neural network	59	12
9	[24]	No	31	14	Raw data 8×8×45	CNN	42.34
					Raw data 8×8×45	CNN-RNN	52.86
					7	Deep autoencoders	33.52
10	Our proposed method	No	31	14	Input data (raw data) 8×8×85	Convolutional neural network	63.7	11.85