An Adaptive Hidden Markov Model for Activity Recognition Based on a Wearable Multi-Sensor Device
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Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based on multi-sensor data is presented. In order to utilize these data efficiently and overcome the big data problem, an offline adaptive-Hidden Markov Model (HMM) is proposed. A sensor selection scheme is implemented based on an improved Viterbi algorithm. A new method is proposed that incorporates personal experience into the HMM model as a priori information. Experiments are conducted using a personal wearable computer eButton consisting of multiple sensors. Our comparative study with the standard HMM and other alternative methods in processing the eButton data have shown that our method is more robust and efficient, providing a useful tool to evaluate human activity and lifestyle.
KeywordsHidden Markov model Activity recognition Wearable device Big data Machine learning Personal health
This work was supported by National Institutes of Health Grants No. R01CA165255, R21CA172864, and P30 AG024827 of the United States, and the National Natural Science Foundation No. 61402428 of China.
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