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Wearable sensor-based human activity recognition from environmental background sounds

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Abstract

Understanding individual’s activities, social interaction, and group dynamics of a certain society is one of fundamental problems that the social and community intelligence (SCI) research faces. Environmental background sound is a rich information source for identifying individual and social behaviors. Therefore, many power-aware wearable devices with sound recognition function are widely used to trace and understand human activities. The design of these sound recognition algorithms has two major challenges: limited computation resources and a strict power consumption requirement. In this paper, a new method for recognizing environmental background sounds with a power-aware wearable sensor is presented. By employing a novel low calculation one-dimensional (1-D) Haar-like sound feature with hidden Markov model (HMM) classification, this method can achieve high recognition accuracy while still meeting the wearable sensor’s power requirement. Our experimental results indicate an average recognition accuracy of 96.9 % has been achieved when testing with 22 typical environmental sounds related to personal and social activities. It outperforms other commonly used sound recognition algorithms in terms of both accuracy and power consumption. This is very helpful and promising for future integration with other sensor(s) to provide more trustworthy activity recognition results for the SCI system.

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Acknowledgments

The authors want to sincerely thank Dr. Yano K., Senior Chief Researcher of Central Research Laboratory at Hitachi Ltd. for providing us an opportunity to take part in this research. We want to express our sincere acknowledges to Mr. Ohkubo N. and Mr. Wakisaka Y. for developing the wearable sensor node well used in our experiments. We would also thank Dr. Daribo Ismael, Mr. Jun Nishimura, and Mr. Hao Zhang for their helpful discussion and comments during this research. Finally, we gratefully acknowledge the anonymous reviewers. Their valuable comments and suggestions are very helpful to improve the presentation of this paper and our future work.

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  1. Department of Electronics and Electrical Engineering, Keio University, Hiyoshi 3-14-1, Kouhoku-ku, Yokohama-shi, Kanagawa-ken, 223-8522, Japan

    Yi Zhan & Tadahiro Kuroda

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  1. Yi Zhan
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  2. Tadahiro Kuroda
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Correspondence to Yi Zhan.

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Zhan, Y., Kuroda, T. Wearable sensor-based human activity recognition from environmental background sounds. J Ambient Intell Human Comput 5, 77–89 (2014). https://doi.org/10.1007/s12652-012-0122-2

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