Human Motion Recognition Based on Triaxial Accelerometer

In this paper, a new method is presented to realize motion detection on a mobile device. The scheme can recognize the people’s motions state according to the acceleration data as long as they simply carry a mobile device with a build-in triaxial accelerometer. The features of the motion signal are extracted in frequency domain and time domain using the method of comprehensive analysis. To enhance the adaptability of the method, the algorithm of independent direction of mobile device algorithm has been applied. The 11 major components, which have greatest contribution to the motion detection, are selected from the 21 motion’s features by principal component analysis, so the input dimension is reduced and the computational complexity of time and space of the algorithm is decreased. Based on the analysis and synthetic comparison of various classification algorithm, the J48 decision tree is accepted. According to the characteristics of the people nature motion, the hidden Markov model is introduced to improve the detection accuracy. Experiments, with different person and different motion, show that the synthesis algorithm has good accuracy and adaptability, and the highest recognition rate achieves 96.13%.