puffMarker: A Multi-Sensor Approach for Pinpointing the Timing of First Lapse in Smoking Cessation
Year: 2015
Type: article
Abstract: Recent researches have demonstrated the feasibility of detecting smoking from wearable sensors, but their performance on real-life smoking lapse detection is unknown. In this paper, we propose a new model and evaluate its performance on 61 newly abstinent smokers for detecting a first lapse. We use two wearable sensors — breathing pattern from respiration and arm movements from 6-axis inertial sensors worn on wrists. In 10-fold cross-validation on 40 hours of training data from 6 daily smokers, our model achieves a recall rate of 96.9%, for a false positive rate of 1.1%. When our model is applied to 3 days of post-quit data from 32 lapsers, it correctly pinpoints the timing of first lapse in 28 participants. Only 2 false episodes are detected on 20 abstinent days of these participants. When tested on 84 abstinent days from 28 abstainers, the false episode per day is limited to 1/6.
Source: Europe PMC (PubMed Central)
Authors: Saleheen, Nazir, Ali, Amin Ahsan, Hossain, Syed Monowar, Sarker, Hillol, Chatterjee, Soujanya, Marlin, Benjamin, Ertin, Emre, al’Absi, Mustafa, Kumar, Santosh
Language: English
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7073865285