Table 5.

Summary of accuracy and kappa values for different ML predictions of walking, standing, grazing, lying and running at 3, 5 and 11 s time windows on the test dataset. Bold indicates the highest accuracy and Kappa values combination.

Time Window	Sensor	ELM		AdaBoost		Stacking		Number of Classified Behaviors
		Accuracy (%)	Kappa Value	Accuracy (%)	Kappa Value	Accuracy (%)	Kappa Value
3 s	Accelerometer	91.5	0.871	94.7	0.921	94.6	0.918	5
	Gyroscope	90.1	0.850	92.4	0.885	93.1	0.895	5
	Accelerometer and Gyroscope	92.7	0.890	97.1	0.956	97.2	0.959	5
5 s	Accelerometer	93.3	0.899	97.5	0.963	97.6	0.964	4
	Gyroscope	93.0	0.894	95.0	0.923	94.9	0.922	4
	Accelerometer and Gyroscope	94.7	0.920	98.9	0.983	98.9	0.983	4
11 s	Accelerometer	98.26	0.983	99.3	0.989	99.3	0.988	4
	Gyroscope	97.0	0.951	96.6	0.945	96.2	0.939	4
	Accelerometer and Gyroscope	98.5	0.976	99.7	0.995	99.7	0.995	4