Data Transmission in Wearable Sensor Network for Human Activity Monitoring Using Embedded Classifier Technique

Abstract

The recent development of wireless wearable sensor networks has opened up a slew of new possibilities in industries as diverse as healthcare, medicine, activity monitoring, sports, safety, human-machine interface, and more. The battery-powered sensor nodes' longevity is critical to the technology's success. This research proposes a new strategy for increasing the lifetime of wearable sensor networks by eliminating redundant data transmissions. The proposed solution is based on embedded classifiers that allow sensor nodes to determine whether current sensor readings should be sent to the cluster head. A strategy was developed to train the classifiers, which takes into account the impact of data selection on the accuracy of a recognition system. This method was used to create a wearable sensor network prototype for human monitoring of activity Experiments were carried out in the real world to assess the novel method in terms of network lifetime, energy usage, and human activity recognition accuracy. The proposed strategy allows for a large increase in network lifetime while maintaining excellent activity detection accuracy, according to the results of the experimental evaluation. Experiments have also demonstrated that the technology has advantages over state-of-the-art data transmission reduction strategies. Keywords: wireless sensor network; wearable sensors; activity recognition; lifetime; energy con- sumption; transmission suppression; embedded machine learning