A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors

• Published in: Biomedical engineering letters Vol. 10; no. 2; pp. 195 - 203
• Main Authors: Senyurek, Volkan Y., Imtiaz, Masudul H., Belsare, Prajakta, Tiffany, Stephen, Sazonov, Edward
• Format: Journal Article
• Language: English
• Published: Korea The Korean Society of Medical and Biological Engineering 01.05.2020; Springer Nature B.V; 대한의용생체공학회
• Subjects: Algorithms; Artificial neural networks; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Cigarette smoking; Computer architecture; Drug addiction; Engineering; Inductance; Inertial platforms; Inertial sensing devices; Long short-term memory; Medical and Radiation Physics; Neural networks; Original; Original Article; Plethysmography; Recurrent neural networks; Sensors; Signal classification; Smoking; Streams; Wearable technology; 의공학; Deep learning; IMU; CNN; Puff; LSTM; PACT; Cigarette smoking; Respiration

A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm fo...