Publication detail
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Human Activity Recognition Based on Wireless Electrocardiogram and Inertial SensorsIEEE Sensors Journal, 24(5), pp. 6490–6499, 2024
Abstract
Wearable devices enable remote, long-term, and unobtrusive monitoring of patients in their everyday living and working environments. Remote health monitoring often involves monitoring physical and cardiac activities (exertions) in order to establish correlations between the two. With recent advances in sensor technologies and machine learning, the efficiency with which these activities can be recognized has been steadily improving. In this paper, we apply Convolutional Neural Networks (CNN) to measurements taken with wireless electrocardiograms and inertial sensors for Human Activity Recognition (HAR). Experimental results confirm that our approach is able to recognize a wide range of everyday activities with a high degree of accuracy. Specifically, activities such as Jumping, Running, and Sitting could be recognized with accuracy exceeding 99
Topics: Internet of Things