Currently, smartwatches are mainly used as an extension of smartphones. However, equipped with various motion sensors, they are also effective devices for human activity recognition, particularly for those involving hand and arm movements. In this paper, we investigate the smoking recognition problem with motion sensors on smartwatches using supervised learning algorithms. For this purpose, we collected a dataset from 11 participants including ten different activities. The dataset includes different smoking variations in four different postures, such as smoking while standing, as well as similar activities, such as eating, and other activities, such as walking. Instead of approaching the problem as a binary classification problem, such as smoking and other, we are interested in differentiating smoking in different postures. Our aim is to explore the parameter space that may affect the recognition process on a large and complex dataset, considering 4 different window sizes and overlaps, 63 different features extracted from each sensor, 4 different sensors, 2 different sensor combinations, 3 classifiers and 10 different activities. Additionally, we analyze the impact of participants’ height on the recognition performance. The results show that, simple time-domain features and the combination of accelerometer and gyroscope sensors perform the best. When we consider the impact of height on the recognition performance, the results show that it does not have a significant effect when all activities are considered, however, it does have an effect on smoking while standing, particularly for participants with a significant height difference than others.

中文翻译：

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使用智能手表传感器以不同姿势和对用户身高的影响进行吸烟识别

当前，智能手表主要用作智能手机的扩展。但是，由于配备了各种运动传感器，它们还是用于人类活动识别的有效设备，尤其是对于那些涉及手和手臂运动的传感器。在本文中，我们使用监督学习算法，通过智能手表上的运动传感器来研究吸烟识别问题。为此，我们收集了11位参与者的数据集，其中包括10种不同的活动。数据集包括四种不同姿势的不同吸烟变化，例如站立时吸烟，以及类似的活动（例如进餐）和其他活动（例如步行）。与其将问题作为吸烟和其他问题的二元分类问题来解决，我们对区分不同姿势的吸烟感兴趣。我们的目标是探索可能影响大型复杂数据集识别过程的参数空间，考虑4种不同的窗口大小和重叠，从每个传感器提取的63个不同特征，4个不同的传感器，2个不同的传感器组合，3个分类器和10个不同的活动。此外，我们分析了参与者身高对识别性能的影响。结果表明，简单的时域特征以及加速度计和陀螺仪传感器的组合表现最佳。当我们考虑身高对识别性能的影响时，结果表明，在考虑所有活动后，身高并没有明显的影响，但是，它确实对站立时的吸烟有影响，特别是对于身高比其他。