On Influencing Factors in Human Activity Recognition Using Wireless Networks
On Influencing Factors in Human Activity Recognition Using Wireless Networks
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DOI：
10.1109/globecom38437.2019.9014016
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发表时间：
2019-12
期刊：
影响因子：
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通讯作者：
Haochen Hu;Zhi Sun;Lu Su
中科院分区：
文献类型：
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作者：
Haochen Hu;Zhi Sun;Lu Su
在机器学习和无线技术发展的推动下，人们在人类活动识别（HAR）方面投入了大量的研究工作。尽管各种深度学习算法可以实现识别人类活动的高精度，但现有的工作缺乏理论性能上限，即最佳精度，无论任何 HAR 算法，仅受无线网络中的影响因素（例如室内物理环境和无线传感设备的设置）的限制。在不了解性能上限的情况下，无论使用什么深度学习算法，错误配置影响因素都会大大降低 HAR 精度。在本文中，我们提出了 HAR 性能上限，它是不依赖于任何 HAR 算法的最小分类错误概率，并且可以被视为无线传感网络中基于 CSI 的人体活动识别的影响因素的函数。由于性能上限可以捕捉影响因素对 HAR 精度的影响，因此我们通过 MATLAB 仿真进一步分析了不同情况下这些因素的影响，例如穿墙 HAR 和不同的人类活动。
Driven by the development of machine learning and the development of wireless techniques, lots of research efforts have been spent on the human activity recognition (HAR). Although various deep learning algorithms can achieve high accuracy for recognizing human activities, existing works lack of a theoretical performance upper bound which is the best accuracy that is only limited by the influencing factors in wireless networks such as indoor physical environments and settings of wireless sensing devices regardless of any HAR algorithm. Without the understanding of performance upper bound, mistakenly configuring the influencing factors can reduce the HAR accuracy drastically no matter what deep learning algorithms are utilized. In this paper, we propose the HAR performance upper bound which is the minimum classification error probability that doesn't depend on any HAR algorithms and can be considered as a function of influencing factors in wireless sensing networks for CSI based human activity recognition. Since the performance upper bound can capture the impacts of influencing factors on HAR accuracy, we further analyze the influences of those factors with varying situations such as through the wall HAR and different human activities by MATLAB simulations.