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
• ISSN: 2093-9868; 2093-985X; 2093-985X
• DOI: 10.1007/s13534-020-00147-8

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 for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.