Bhandari Babin, Rajasegarar Sutharshan, Karmakar Chandan
Annu Int Conf IEEE Eng Med Biol Soc. 2017 Jul;2017:845-848. doi: 10.1109/EMBC.2017.8036956.
Although smoking prevalence is declining in many countries, smoking related health problems still leads the preventable causes of death in the world. Several smoking intervention mechanisms have been introduced to help smoking cessation. However, these methods are inefficient since they lack in providing real time personalized intervention messages to the smoking addicted users. To address this challenge, the first step is to build an automated smoking behavior detection system. In this study, we propose an accelerometer sensor based non-invasive and automated framework for smoking behavior detection. We built a prototype device to collect data from several participants performing smoking and other five confounding activities. We used three different classifiers to compare activity detection performance using the extracted features from accelerometer data. Our evaluation demonstrates that the proposed approach is able to classify smoking activity among the confounding activities with high accuracy. The proposed system shows the potential for developing a real time automated smoking activity detection and intervention framework.
尽管许多国家的吸烟率正在下降，但吸烟相关的健康问题仍然是全球可预防死亡的首要原因。已经引入了几种吸烟干预机制来帮助戒烟。然而，这些方法效率低下，因为它们缺乏向吸烟成瘾者提供实时个性化干预信息的能力。为应对这一挑战，第一步是构建一个自动吸烟行为检测系统。在本研究中，我们提出了一种基于加速度计传感器的非侵入式自动吸烟行为检测框架。我们构建了一个原型设备，以收集多名参与者在进行吸烟及其他五种干扰活动时的数据。我们使用三种不同的分类器，利用从加速度计数据中提取的特征来比较活动检测性能。我们的评估表明，所提出的方法能够在干扰活动中高精度地对吸烟活动进行分类。所提出的系统显示了开发实时自动吸烟活动检测与干预框架的潜力。
