Table 2:

Comparison of our $\text{AQSM-}S\mathbb{W}1PerS$ model trained on accelerometer data with additional period estimation features $(\text{RF-}P{S}_{10}+Period(\text{Accel}))$ and pose skeleton $(\text{RF-}P{S}_{10}(\text{Pose}))$ using a Random Forest classifier against prior work. Performance is evaluated using Accuracy (A) and weighted $F_1$ score, the latter being more informative under class imbalance.

Study	Method	S1		S2		S3		S4		S5		S6		Mean
		$F_1$	A	$F_1$	A	$F_1$	A	$F_1$	A	$F_1$	A	$F_1$	A	$F_1$	A
Study 1	SVM [11]	0.73	0.86	0.36	0.85	0.50	0.94	0.73	0.67	0.44	0.75	0.46	0.87	0.54	0.82
	RF-RQA [12]	—	0.71	—	0.73	—	0.70	—	0.92	—	0.68	—	0.94	—	0.78
	CNN-Rad [30]	0.70	0.71	0.74	0.70	0.69	0.68	0.92	0.78	0.68	0.78	0.93	0.78	0.78	0.74
	Frequency-domain CNN [33]	0.97	0.99	0.78	0.89	0.94	0.99	0.96	0.97	0.96	0.98	0.99	0.98	0.93	0.97
	$\text{RF-}P{S}_{10}+Period(\text{Accel})$	0.90	0.90	0.94	0.94	0.94	0.95	0.95	0.95	0.90	0.91	0.92	0.92	0.93	0.93
	$\text{RF-}P{S}_{10}(\text{Pose})$	0.78	0.81	0.68	0.75	0.68	0.76	0.77	0.78	0.70	0.77	0.88	0.87	0.75	0.79
Study 2	SVM	0.43	0.71	0.26	0.79	0.03	0.99	0.86	0.90	0.72	0.73	—	—	0.46	0.82
	RF-RQA	—	0.80	—	0.69	—	0.99	—	0.95	—	0.85	—	—	—	0.856
	CNN-Rad	0.67	0.68	0.22	0.02	0.02	0.77	0.75	0.75	0.68	0.75	0.80	0.49	0.41	0.58
	Frequency-domain CNN	0.96	0.97	0.95	0.98	0.85	1.00	0.98	0.99	0.94	0.97	—	—	0.94	0.98
	$\text{RF-}P{S}_{10}+Period(\text{Accel})$	0.85	0.85	0.60	0.58	—	—	0.96	0.96	0.70	0.70	0.98	0.98	0.82	0.81
	$\text{RF-}P{S}_{10}(\text{Pose})$	0.83	0.84	0.71	0.75	—	—	0.96	0.96	0.86	0.86	0.98	0.98	0.87	0.88