Abstract
Recognizing human complex activities has become an essential topic in pervasive computing research area. With the growing popularity of mobile phones, more and more studies have been dedicated to identifying human complex activities using mobile phones in recent years. However, previous works often restrain the position and orientation of cell phones which limit the applicability of their methods. To overcome this limitation, we propose a novel position-irrelevant activities identification method named PSHCAR, which efficiently utilize information from multiple sensors on smartphones. Moreover, besides commonly-used features such as accelerometer and gyroscope, PSHCAR also employ the knowledge about scenes of activities, which is helpful but ignored by previous works, to identify complex activities of mobile phone users. Comparative experiments show that our method performs better than several strong baselines on the task of human complex activities recognition. In conclusion, our method achieves state-of-the-art performance without any limitation on position or orientation of mobile phones.
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Acknowledgements
This work is supported by Basic Research Funds for Higher Education Institution in Heilongjiang Province (Fundamental Research Project, Grant No.KJCXYB201702).
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Jia, B., Li, J., Xu, H. (2018). PSHCAR: A Position-Irrelevant Scene-Aware Human Complex Activities Recognizing Algorithm on Mobile Phones. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_15
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DOI: https://doi.org/10.1007/978-981-13-2203-7_15
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