Abstract
The recognition of human activity by wearable sensors has garnered significant interest owing to its extensive applications in health, sports, and surveillance systems. This paper presents a novel hybrid deep learning model, termed CNNd-TAm, for the recognition of both basic and complicated activities. The suggested approach enhances spatial feature extraction and long-term temporal dependency modeling by integrating Dilated convolutional networks with a modified temporal attention mechanism. Data from accelerometer and gyroscope sensors in the UTwente dataset, encompassing 13 activities and 10 people, underwent preparation that included filtering, normalization, and the selection of diverse time periods according to the activity type. Experimental findings demonstrate an accuracy of 99.4% in identifying intricate behaviors, such as conversing and consuming coffee, surpassing earlier hybrid deep learning models. This model represents a significant advancement in the development of efficient Human Activity Recognition systems by solving deficiencies in the recognition of intricate activities.