Copyright: © the author(s), publisher and licensee Technoscience Academy. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited International Journal of Scientific Research in Computer Science, Engineering and Information Technology ISSN : 2456-3307 (www.ijsrcseit.com) doi : https://doi.org/10.32628/IJSRCSEIT 173 Data Transmission in Wearable Sensor Network for Human Activity Monitoring using Embedded Classifier technique Lithin Kumble, Kiran Kumari Patil School of Computing and IT, REVA University, India Article Info Volume 8, Issue 2 Page Number : 173-182 Publication Issue : March-April-2022 Article History Accepted: 01 April 2022 Published: 09 April 2022 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. I. INTRODUCTION The majority of wireless wearable sensor networks are made up of many sensor nodes that are attached to the human body or incorporated in garments [1–3]. The sensor nodes can keep track of both body and environment characteristics. Wireless data links are used to communicate between the wearable sensor nodes. There have been several applications of wearable sensor networks considered in the literature so far. Healthcare, location, activity tracking, sport, fitness, augmented reality, safety, rescue, and emergency management are just a few of the potential uses.