Human Health Activity Recognition Algorithm in Wireless Sensor Networks Based on Metric Learning

Abstract

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.

Deep Custom Transfer Learning Models for Recognizing Human Activities via Video Surveillance

“…For classifying images, recognizing objects, and recognizing actions, deep learning models like CNN [5] and Deep Belief Networks (DBNs) [6] were introduced to reduce the dimensionality of the input. However, building a new deep learning model from the start involves a significant quantity of data, powerful computing power, and hours, or even days, of training [7], [8]. In practical applications, collecting and annotating a sizable volume of domain-specific data requires a lot of time and money.…”