Table 6.

Device-free HAR models

	C1	C2	C3	C4	C5	C6	C7	C8
	# Features	Feature extraction	ML/DL model	Architecture	Metrics	Validation	Hyper-parameters/optimizer loss function	CIT*
R1	Frequency	Hand-crafted	ELM	AACA: using the difference between the activity and the stationary parts in the signal variance feature. For recognition use 3-layer ELM with an i/p layer with 200 neurons, an o/p (10) and hidden layer (40) neurons	Accuracy	Tested the performance on 1100 samples	No. of hidden layer neurons (after 400 becomes stable), different users, impact of total no. of samples	Yan et al. (2020)
R2	Frequency CSI	Hand-crafted	DTW: by comparing similarity b/w waveforms	CP decomposition: decompose the CSI signals with CP and each rank-one tensor after decomposition is regarded as the feature. With DTW, we can compare the similarity between 2 waveforms and identify action	Accuracy	Recognition of gaits using MARS	Impact of nearby people, test for system delay	Fei et al. (2020)
R3	CSI (time & amplitude)	Automatic	LSTM	CNN extracts spatial features from multiple antenna pairs, then CNN o/p is given to LSTM followed by FC	FPR, precision, recall, F1-score		DO, Layer size, recognition method	Wang et al. (2019d)

*CIT citations, DTW dynamic time wraping, FPR false positive rate, DO dropout, ELM extreme learning machine, CSI channel state information