IoT4Care Group: Paper accepted at IEEE International Conference on Omni-layer Intelligent Systems

Our newest work, a collaboration between the University of Limerick, Ireland, and the University of Stavanger, Norway 
"CNN-based Human Activity Recognition on Edge Computing Devices" has been accepted at IEEE International Conference on Omni-layer Intelligent Systems #IEEECOINS23! 🎉

A big thank goes to Amandeep Singh, and Prof. Tiziana Margaria for their effort and collaborative spirit.

More information below 🧵.
Human Activity Recognition (HAR) is a widely known research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to several issues, such as latency, bandwidth requirements, energy consumption, privacy risks, and limited offline capabilities, limiting its applicability to various fields that require a quasi-real-time response. On the other hand, performing HAR on edge devices close to the data source can offer several advantages, such as reduced latency, low bandwidth usage, energy efficiency, privacy, and offline capabilities. Therefore, transitioning towards Edge HAR can be a more effective and versatile solution that can overcome the challenges associated with traditional HAR techniques.
This paper presents a novel approach to HAR computation on edge devices, utilizing a Convolutional Neural Network (CNN) Deep Learning approach, and compares its performance with cloud-based HAR computation. The paper is accompanied by a self-collected dataset including nine different daily activities from 12 users and new algorithms specifically designed for edge computing. The experiments on the self-collected dataset demonstrate that the proposed edge computing model achieves high accuracy of over 92%, high Precision, Recall, and F1-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory.

#IEEECOINS23 #edgecomputing #health #collaboration #university #research