Abstract
In the field of human action recognition, some existing works are mainly focused on macro actions, e.g., the requirements for action recognition is walking or jumping, while others are concentrated on micro actions, e.g., hand waving or leg raising. However, existing works rarely consider the recognition effect of different sensor wearing schemes with various requirements. In this work, the influences of the wearing scheme on action recognition effect are taken into account, a universal action recognition method to adapt different recognition requirements is developed. First, we present an action layered verification model which includes static action layer, dynamic action layer and joint presentation layer, which is used to provide an optional wearing scheme for each layer and to prevent wrong classification problems. Second, we verify the recognition effect of various wearing schemes under different layers. Finally, an action recognition method based on decision tree is introduced to adapt different requirements. The experiments show that the proposed method achieves a desirable recognition effect in comparison to existing ones.
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Acknowledgement
This work is supported by the Natural Science Foundation of China (No. 61502118), the Natural Science Foundation of Heilongjiang Province in China (No. F2016009), the Fundamental Research Fund for the Central Universities in China (No. HEUCF180602 and HEUCFM180604) and the National Science and Technology Major Project (No. 2016ZX03001023-005).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ma, F. et al. (2019). An Action Recognition Method Based on Wearable Sensors. In: Zheng, J., Xiang, W., Lorenz, P., Mao, S., Yan, F. (eds) Ad Hoc Networks. ADHOCNETS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 258. Springer, Cham. https://doi.org/10.1007/978-3-030-05888-3_19
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DOI: https://doi.org/10.1007/978-3-030-05888-3_19
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