A Lightweight Deep Learning Model for Human Activity Recognition on Edge Devices

TL;DR

This paper introduces a lightweight deep learning model tailored for human activity recognition on resource-constrained edge devices, achieving high performance with reduced computational requirements.

Contribution

The paper presents a novel, computationally efficient deep learning model specifically designed for HAR on edge devices, addressing limitations of existing models.

Findings

Model outperforms existing machine learning techniques

Requires less computational power

Effective on six daily activities data

Abstract

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.