Table 5.

RFID-based 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	84 features for F-statistics, Relief-F, Fisher	Hand-crafted	Canonical Correlation Analysis (CCA)	Divide RSSI stream into segments, CCA for extracting features by computing canonical correlation for each feature pair, activity specific dictionary is formed: Sparse coding and dictionary is updated sequentially using K-SVD	F1-score	One sub out validation strategy	_	Yao et al. (2018)
R2	Frequency	Automatic	LSTM	I/p layer, two hidden layers and o/p layer	Precision, accuracy	_	Timestep, neuron in hidden layers/Adam/Cross entropy	Du et al. (2019)
R3	Frequency	Hand-crafted	Multi variate Gaussian Approach	ADL activity data gathering, score each activity with gaussian pdf, Human Activity Recognition Based on Maximum likelihood Estimation, Activity classification	Accuracy, precision, recall, F1-score, root mean square error (RMSE)	_	_	Oguntala et al. (2019)

*CIT citations, DTW dynamic time wraping, DO dropout, LR learning rate, SVM support vector machine, LSTM long short-term memory