A Feasibility Study of Wrist-Worn Accelerometer Based Detection of Smoking Habits

Abstract

Cigarette smoking is one of the major causes of lung cancer, and has been linked to a large amount of other cancer types and diseases. Smoking cessation, the only mean to avoid these serious risks, is hindered by the ease to ignore these risks in day-to-day life. In this paper we present a feasibility study with smokers wearing an accelerometer device on their wrist over the course of a week to detect their smoking habits based on detecting typical gestures carried out while smoking a cigarette. We provide a basic detection method that identifies when the user is smoking, with the goal of building a system that provides an individualized risk estimation to increase awareness and motivate smoke cessation. Our basic method detects typical smoking gestures with a precision of 51.2% and shows a userspecific recall of over 70% -creating evidence that an unobtrusive wrist-watch-like sensor can detect smoking.

The Usage of Statistical Learning Methods on Wearable Devices and a Case Study: Activity Recognition on Smartwatches

“…Scholl and van Laerhoven [9] presented a feasibility study with smokers wearing an accelerometer device on their wrist over the course of a week to detect their smoking habits based on detecting typical gestures carried out while smoking a cigarette. The Gaussian method was used as a classifier.…”

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

“…Additionally, they investigate the smartwatch's camera to take images of the food being consumed to analyze what a user is eating. A feasibility study on smoking detection using a wrist-worn accelerometer is done in [134], where the authors reported a user-specific accuracy of 70% for this activity. This study used only an accelerometer at the wrist position.…”

“…For example, a user should not be interrupted for a feedback message while typing, writing or giving a talk, but can be interrupted while having a cup of coffee or smoking. Earlier studies [126,132,133,128,130,134] have shown that such activities can be recognized using on-body motion sensors, which we discuss further in the Related Work Section 4.2. However, these studies have used the wrist and pocket positions in isolation.…”

“…For example, in [128], the authors focus on the eating activity only. Similarly, in [134], only the smoking activity is considered. These studies did not explore the effect of increasing segmentation window size on these activities.…”

“…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.…”