Automated Detection of Puffing and Smoking with Wrist Accelerometers

Sitting is the new smoking: online complex human activity recognition with smartphones and wearables

“…This problem can be resolved in the training phase where such instances are labeled as a compound activity, such as smoking-eating or smoking-drinking. However, based on our observations and literature study [141], smokers usually use a different hand for drinking or eating while smoking. It does not affect the smoking gesture, but it increases the inter-puff time which is not a concern for our correction algorithm.…”

“…It is difficult to accurately recognize such high-level activities because they can be performed in various postures or in combination with other activities, they are non-periodic, and they may be confused with each other due to similar hand gestures. The authors in [141,3] argue that detecting various physical triggers (physical activities) can enable applications for effective interventions in achieving behavior change. The smoking, eating and drinking gestures can define such physical triggers.…”

“…Smoking is one of the physical triggers as discussed in [141,3] and its reliable detection can enable applications for an insight into one's smoking behavior as well as for automated self-reporting in smoking cessation programs [141,3]. In such programs, self-reporting is an important aspect where smokers keep track of their cigarette consumption which can be used for evaluating the progress of the relevant behavior change.…”

“…These reasons make it difficult to detect. Its detection has been studied in various ways such as from computer vision to smart lighter, but the ubiquitous use of smartphones and popularity of wearables such as smartwatches may offer a better alternative for smoking recognition [141]. In recent years, a few researchers have shown the potential of using wearable sensors for smoking detection [138,141,134,129].…”

“…However, these studies have mainly focused on the puff (hand-to-mouth gesture) recognition within the smoking activity. Some of them have additionally used a second layer of decision making such as threshold-based rules [141] or condition random fields [129] to identify smoking sessions or episodes. Instead of a fine grain analysis of puffing or handto-mouth gestures, we consider smoking as a non-periodic activity recognition problem.…”