A Novel IoT-Perceptive Human Activity Recognition (HAR) Approach Using Multihead Convolutional Attention

TLDR

The rapid growth of IoT has enabled smart healthcare systems equipped with numerous wearable sensors that collect data and track daily activities, yet most existing HAR methods rely on shallow feature learning and perform poorly in real‑world settings. This study proposes a novel HAR approach that combines convolutional neural networks with an attention mechanism. The method enhances feature extraction and selection by integrating attention into multi‑head CNNs and is evaluated on the publicly available WISDM wireless sensor dataset. The proposed approach achieves higher accuracy than current state‑of‑the‑art HAR methods.

Abstract

Together with the fast advancement of the Internet of Things (IoT), smart healthcare applications and systems are equipped with increasingly more wearable sensors and mobile devices. These sensors are used not only to collect data but also, and more importantly, to assist in daily activity tracking and analyzing of their users. Various human activity recognition (HAR) approaches are used to enhance such tracking. Most of the existing HAR methods depend on exploratory case-based shallow feature learning architectures, which struggle with correct activity recognition when put into real-life practice. To tackle this problem, we propose a novel approach that utilizes the convolutional neural networks (CNNs) and the attention mechanism for HAR. In the presented method, the activity recognition accuracy is improved by incorporating attention into multihead CNNs for better feature extraction and selection. Proof of concept experiments are conducted on a publicly available data set from wireless sensor data mining (WISDM) lab. The results demonstrate a higher accuracy of our proposed approach in comparison with the current methods.