Table 6.

Summary of the 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 CBS 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	84.8	0.796	80.2	0.735	81.2	0.749	5
	Gyroscope	82.8	0.770	78.9	0.716	77.6	0.700	5
	Accelerometer and Gyroscope	85.2	0.801	85.3	0.803	87.8	0.836	5
5 s	Accelerometer	83.2	0.774	78.9	0.717	83.8	0.782	4
	Gyroscope	82.7	0.767	78.6	0.711	81.4	0.750	4
	Accelerometer and Gyroscope	87.4	0.830	86.2	0.814	86.2	0.815	4
11 s	Accelerometer	72.7	0.631	76.9	0.689	74.4	0.656	4
	Gyroscope	66.3	0.542	63.9	0.510	64.8	0.524	4
	Accelerometer and Gyroscope	78.0	0.702	67.8	0.565	71.5	0.616	4