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.
- 4.Gao, L., Bourke, A. K., Nelson, J., Activity recognition using dynamic multiple sensor fusion in body sensor networks. In: 2012 Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 49:1077–1080, 2012.Google Scholar
- 9.Doherty, A. R., Ó Conaire, C., Blighe, M., et al., Combining image descriptors to effectively retrieve events from visual lifelogs. In: Proc. 1st ACM Int. Conf. Multimed. Inf. Retr. 10–17, 2008.Google Scholar
- 10.Chatzichristofis, S. A., Boutalis, Y. S., CEDD: color and edge directivity descriptor . A compact descriptor for image indexing and retrieval. In: Proc. 6th Int. Conf. Comput. Vis. Syst. 312–322, 2008.Google Scholar
- 11.Lowe, D. G., Object recognition from local scale-invariant features. In: Proc. Seventh IEEE Int. Conf. Comput. Vis. 2:1150–1157, 1999.Google Scholar
- 13.Bebars, A. A., Hemayed, E. E., Comparative study for feature detectors in human activity recognition. In: 2013 9th Int. Comput. Eng. Conf. 19–24, 2013.Google Scholar
- 14.Hassan, S. M., Al-Sadek, A. F., Hemayed, E. E., Rule-based approach for enhancing the motion trajectories in human activity recognition. In: 2010 10th Int. Conf. Intell. Syst. Des. Appl. 829–834, 2010.Google Scholar
- 15.Boutell, M., and Brown, C., Pictures are not taken in a vacuum - an overview of exploiting context for semantic scene content understanding. IEEE Signal Process. Mag. 23:101–114, 2006.Google Scholar
- 21.Zhang, W., Jia, W., Sun, M., Segmentation for efficient browsing of chronical video recorded by a wearable device. In: Proc 2010 I.E. 36th Annu. Northeast. Bioeng. Conf. 1–2, 2010.Google Scholar
- 23.Csurka, G., Dance, C., Fan, L., et al., Visual categorization with bags of keypoints. In: Work. Stat. Learn. Comput. Vision ECCV. 1–2, 2004.Google Scholar
- 25.Powers, D. M., Evaluation: from precision, recall and F-measure to ROC, informedness, markedness & correlation. J. Mach. Learn. Technol. 2:37–63, 2011.Google Scholar