Recurrent Transformation of Prior Knowledge Based Model for Human Motion Recognition

Table 3

Comparisons with methods in other literatures.

Method	Candidate motions	Sensors type	Sensors location	Accuracy
Decision tree [8]	25 actions, Stand-Sit, Sit-Lie, etc.	Accelerometer, gyroscope	9, wrist, arm, ankle, etc.	93.3%
K-NN [10]	25 actions, Stand-Sit, Sit-Lie, etc.	Accelerometer, gyroscope	8, waist, left-forearm, etc.	92.2%
Neural Networks [11]	12 actions, Standing, Lying, etc.	Accelerometer	5, left forearm, trunk, etc.	89.2%
SVM [12]	8 actions, running, upstairs, etc.	Accelerometer, gyroscope, Magnetometer, barometer sensor	1, hand	88.6%
Bayesian Network [7]	7 actions, running, walking, etc.	Accelerometer, gyroscope, Magnetometer	1, belt	90%
proposed RT-PKDT	8 actions, listed in Activity	Accelerometer, gyroscope, barometer sensor	5 body-positions, listed in Location	96.68