Publications Repository - Gdańsk University of Technology

Together with 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 straggle 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 multi-head convolutional neural networks for better feature extraction and selection. Proof of concept experiments are conducted on a publicly available dataset from Wireless Sensor Data Mining (WISDM) laboratory. The results demonstrate higher accuracy of our proposed approach in comparison with the current methods.

Authors

• Haoxi Zhang,
• Zhiwen Xiao,
• Juan Wang,
• Fei Li,
• prof. dr hab. inż. Edward Szczerbicki link open in new tab