Table 3.

F-Score based performance evaluation for proposed scenarios

Scenario A1: Training = 80% of dataset, Testing = 20% of dataset, Preliminary Selection: Filtered data with TS_window size 40							Scenario A2: Training = 100% dataset of any one user, Testing = 100% dataset of all other users, Preliminary Selection: Filtered data with TS_window size 40
User	T	RP	CNN	Performance metrics			User’s for training	T	RP	CNN	Performance metrics
				Recall	Precision	F2					Recall	Precision	F2
ID-1	0.21	3	4	1	0.75	0.94	ID-1	0.18	33	47	0.76	0.53	0.70
ID-2	0.21	4	3	0.75	1	0.79	ID-2	0.18	37	39	0.70	0.67	0.69
ID-3	0.21	1	2	1	0.5	0.83	ID-3	0.18	38	47	0.76	0.62	0.73
ID-4	0.21	0	3	-	-		ID-4	0.18	41	43	0.78	0.74	0.77
ID-5	0.21	1	1	1	1	1.00	ID-5	0.18	38	46	0.74	0.61	0.71
ID-6	0.21	3	3	1	1	1.00	ID-6	0.18	35	47	0.77	0.57	0.72
ID-7	0.21	1	2	1	0.5	0.83	ID-7	0.18	39	49	0.80	0.65	0.76
ID-8	0.21	2	3	0.5	0.33	0.45	ID-8	0.18	34	41	0.76	0.63	0.73
ID-9	0.21	1	2	0	0	0.00	ID-9	0.18	37	45	0.73	0.60	0.70
ID-10	0.21	2	2	1	1	1.00	ID-10	0.18	37	41	0.65	0.59	0.64
Weighted Average	0.21	18	26	0.83	0.60	0.77	Weighted Average				0.75	0.62	0.72

The significance of bold is linked to the best result of the model