Recognizing Human Daily Activities From Accelerometer Signal
Jin Wang, Ronghua Chen, Xiangping Sun, Mary F.H. She, Yuchuan Wu · Procedia Engineering · 2011
Automated recognition of human daily activities from wearable sensor signals has attracted a great deal of interests in many applications including health care, sports and aged care. In this paper, we presented a Hidden Markov Model (HMM)-based recognition method to recognize six human daily activities from sensor signals collected from a single waist-worn tri-axial accelerometer. All training signals from the same activity class are modeled as generated by a HMM, while a Gaussian Mixture Model (GMM) is used to model the continuous observation for each hidden state. A new test signal is classified to the activity class corresponding to the HMM that can produce the highest likelihood. In order to validate the performance of the proposed method, we collected 420 samples from seven subjects and 72 samples from six new subjects, while performing six daily activities including walking, standing, running, jumping, sitting-down, and falling-down. Two batches of experiments were conducted. The experimental results on the first dataset show that the proposed HMM classifier can learn acceleration signals well with low computational complexity, achieving a very high activity recognition accuracy of 94.8%. In the second experiment, the HMM classifier was evaluated by using new data contained in the second dataset and only two samples were misclassified, which demonstrates that the HMM-based recognition system has a good generalization capability. It can be concluded that the proposed method holds a potential in long-term in-situ assessment of human daily activities under ambulatory environment due to its robustness and computational simplicity.