CigTrak: Smartwatch-Based Accurate Online Smoking Puff & Episode Detection with Gesture-Focused Windowing for CNN.

Abstract

Wearable-based motion sensing solutions are capable of automatically detecting and tracking individual smoking puffs and/or episodes to aid the users in their journey of smoking cessation. But they are either obtrusive to use, perform with a low accuracy, or have questionable ability of running fully on a low-power device like a smartwatch, all affecting their widespread adoption. We propose 'CigTrak', a novel pipeline for an accurate smoking puff and episode detection using 6-DoF motion sensor on a smartwatch. A multi-stage method for puff detection is devised, comprising a novel kinematic analysis of puffing motion enabling temporal localization of puff. A Convolutional Neural Network (CNN)-backed model uses this candidate puff as an input instance by re-sampling it to required input size for the final decision. Clusters of detected puffs are further used to detect episodes. Data from 13 subjects was used for evaluating puff detection, and 9 subjects for evaluating episode detection. CigTrak achieved a high subject-independent performance for puff detection (F1-score 0.94) and free-living episode detection (F1-score 0.89), surpassing state of the art performance. CigTrak was also implemented fully online on two different smartwatches for testing a real-time puff detection.Clinical Relevance- Cigarette smoking affects physical & mental well-being of a person, and is the leading cause of preventable diseases, adversely affecting cardiac and respiratory systems. With many adults wanting to quit smoking [1], a reliable way of auto-journaling of smoking activities can greatly aid in cessation efforts through self-help, and reduce burden on healthcare industry. CigTrak, with its high accuracy in detecting smoking puffs and episodes, and capability of running fully on a smartwatch, can be readily used for this purpose.