A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices
This addresses the need for efficient activity recognition on edge devices in fields like healthcare and surveillance, but it is incremental as it builds on existing deep learning approaches.
The paper tackles the problem of deploying deep learning for Human Activity Recognition on resource-constrained edge devices by proposing a lightweight model, which outperforms existing techniques on a dataset of six daily activities.
Human Activity Recognition (HAR) using wearable and mobile sensors has gained momentum in last few years, in various fields, such as, healthcare, surveillance, education, entertainment. Nowadays, Edge Computing has emerged to reduce communication latency and network traffic.Edge devices are resource constrained devices and cannot support high computation. In literature, various models have been developed for HAR. In recent years, deep learning algorithms have shown high performance in HAR, but these algorithms require lot of computation making them inefficient to be deployed on edge devices. This paper, proposes a Lightweight Deep Learning Model for HAR requiring less computational power, making it suitable to be deployed on edge devices. The performance of proposed model is tested on the participants six daily activities data. Results show that the proposed model outperforms many of the existing machine learning and deep learning techniques.