Abstract
Human Action Recognition is becoming more and more important in many fields, especially in sports. However, conventional algorithm are almost camera-based methods, which make it cumbersome and expensive. As the wearable inertial sensor has developed a lot, in this paper, we present a novel human action classification algorithm using in basketball, based on a single inertial sensor, which is a application of multi-label classification. We performed experiment on real world datasets. The AUPRC, AUROC and confusion matrix of our results demonstrated that our novel basketball motion recognizer have a great performance.
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Acknowledgments
This work is supported by Student’s Platform for Innovation and Entrepreneurship Training Program, Xiamen University (2016Y1123), and 2016 Google Student Innovation Project (64008066).
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Meng, X. et al. (2018). Human Action Classification in Basketball: A Single Inertial Sensor Based Framework. In: Hung, J., Yen, N., Hui, L. (eds) Frontier Computing. FC 2017. Lecture Notes in Electrical Engineering, vol 464. Springer, Singapore. https://doi.org/10.1007/978-981-10-7398-4_16
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DOI: https://doi.org/10.1007/978-981-10-7398-4_16
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