School of Mathematics and Information Science and Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China.
School of Information Science and Engineering (School of Software), Yanshan University, Qinhuangdao 066000, China.
Comput Intell Neurosci. 2022 Apr 18;2022:4204644. doi: 10.1155/2022/4204644. eCollection 2022.
Wireless sensor network is an ad hoc network with sensing capability. Usually, a large number of sensor nodes are randomly deployed in an unreachable environment or complex area for data collection and transmission, which can realize the perception and monitoring of the target area or specific objects and transmit the obtained data to the remote end of the system. Human health activity recognition algorithm is a hot topic in the field of computer. Based on the small sample problem and the linear indivisibility of real samples encountered in metric learning, this paper proposes a human activity recognition algorithm for wireless sensor networks. Human activity recognition algorithm for wireless sensor networks uses human activity recognition algorithm to solve the singularity of intraclass divergence matrix, so as to reduce the impact of small sample problem. The algorithm maps two different feature spaces to the high-dimensional linearly separable kernel space through the corresponding kernel function, calculates the distance between samples in the two projected feature subspaces to obtain two distance measurement functions, and finally linearly combines them with weights to obtain the final distance measurement function.
无线传感器网络是一种具有感知能力的自组织网络。通常，大量的传感器节点被随机部署在无法到达的环境或复杂区域，用于数据收集和传输，从而实现对目标区域或特定对象的感知和监测，并将获得的数据传输到系统的远程端。人体活动识别算法是计算机领域的一个热门话题。基于度量学习中遇到的小样本问题和真实样本的线性不可分性，本文提出了一种用于无线传感器网络的人体活动识别算法。无线传感器网络的人体活动识别算法使用人体活动识别算法来解决类内离散矩阵的奇异性问题，从而减少小样本问题的影响。该算法通过相应的核函数将两个不同的特征空间映射到高维线性可分核空间中，计算两个投影特征子空间中样本之间的距离，得到两个距离度量函数，最后通过权重线性组合得到最终的距离度量函数。
