An Efficient ResNetSE Architecture for Smoking Activity Recognition from Smartwatch

Abstract

Smoking is a major cause of cancer, heart disease and other afflictions that lead to early mortality. An effective smoking classification mechanism that provides insights into individual smoking habits would assist in implementing addiction treatment initiatives. Smoking activities often accompany other activities such as drinking or eating. Consequently, smoking activity recognition can be a challenging topic in human activity recognition (HAR). A deep learning framework for smoking activity recognition (SAR) employing smartwatch sensors was proposed together with a deep residual network combined with squeeze-and-excitation modules (ResNetSE) to increase the effectiveness of the SAR framework. The proposed model was tested against basic convolutional neural networks (CNNs) and recurrent neural networks (LSTM, BiLSTM, GRU and BiGRU) to recognize smoking and other similar activities such as drinking, eating and walking using the UT-Smoke dataset. Three different scenarios were investigated for their recognition performances using standard HAR metrics (accuracy, F1-score and the area under the ROC curve). Our proposed ResNetSE outperformed the other basic deep learning networks, with maximum accuracy of 98.63%.