EdgeHARNet: An Edge-Friendly Shallow Convolutional Neural Network for Recognizing Human Activities Using Embedded Inertial Sensors of Smart-Wearables

Human Activity Recognition (HAR) is a well-known area of study in the Internet of Medical and Health Things. The goal of HAR is to monitor complex, subtle, and postural human behaviours in the domains of Ambient Assisted Living (AAL), injury prevention, well-being management, medical diagnostics, and, in particular, geriatric care. The use of inertial sensors in smart devices for HAR is becoming more common, as it eliminates all of the constraints of traditional computer vision techniques. The usage of artificial neural networks improves classification, but their larger complexity makes them harder to deploy near the edge, where latency is reduced. This article presents a deep learning model that is lite in terms of trainable parameters and hence enables Edge-AI. The model is named EdgeHARNet. It assessed WISDM Activity Prediction and WISDM Actitracker datasets. The presented model has only 2031 trainable parameters. It has achieved 94.036% average accuracy for the WISDM Activity Prediction dataset and 74.06% average accuracy for the WISDM Actitracker dataset. Fl-score have also been reported for all individual classes. The performance comparison is carried out with existing models. We have done inference on Raspberry Pi 4 which is a single board computer as well.