  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.
