A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors
A CNN-LSTM neural network for recognition of puffing in smoking episodes using wearable sensors
复制标题
使用可穿戴传感器识别吸烟事件中的抽吸现象的 CNN-LSTM 神经网络
DOI：
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发表时间：
2020
影响因子：
4.6
通讯作者：
E. Sazonov
中科院分区：
文献类型：
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作者：
V. Senyurek;Masudul H. Imtiaz;Prajakta Belsare;Stephen T. Tiffany;E. Sazonov
自由生活条件下吸烟行为的详细评估是健康行为研究的一个关键挑战。已经开发了许多使用可穿戴传感器和抽吸形貌装置的方法用于吸烟和个体抽吸检测。在本文中，我们提出了一种新的算法，通过使用呼吸电感体积描记术和惯性测量单元传感器的组合，自动检测吸烟发作的抽吸。通过使用包含卷积和递归神经网络的深度网络来执行抽吸的检测。卷积神经网络（CNN）被用来从原始传感器流自动化特征学习。利用长短期记忆（LSTM）网络层获得传感器信号的时间动态，并对时间分段的传感器流序列进行分类。通过使用一个大型的、具有挑战性的数据集进行评估，该数据集包含来自40名参与者在自由生活条件下的467起吸烟事件。所提出的方法实现了78%的F1分数在留一个主题的交叉验证。结果表明，基于CNN-LSTM的神经网络架构足以检测自由生活条件下的抽吸事件。该模型可作为戒烟计划和科学研究的检测工具。
A detailed assessment of smoking behavior under free-living conditions is a key challenge for health behavior research. A number of methods using wearable sensors and puff topography devices have been developed for smoking and individual puff detection. In this paper, we propose a novel algorithm for automatic detection of puffs in smoking episodes by using a combination of Respiratory Inductance Plethysmography and Inertial Measurement Unit sensors. The detection of puffs was performed by using a deep network containing convolutional and recurrent neural networks. Convolutional neural networks (CNN) were utilized to automate feature learning from raw sensor streams. Long Short Term Memory (LSTM) network layers were utilized to obtain the temporal dynamics of sensor signals and classify sequence of time segmented sensor streams. An evaluation was performed by using a large, challenging dataset containing 467 smoking events from 40 participants under free-living conditions. The proposed approach achieved an F1-score of 78% in leave-one-subject-out cross-validation. The results suggest that CNN-LSTM based neural network architecture sufficiently detect puffing episodes in free-living condition. The proposed model be used as a detection tool for smoking cessation programs and scientific research.
DOI：
10.1016/s2213-2600(15)00521-4
发表时间：
2016-02
期刊：
The Lancet. Respiratory medicine
影响因子：
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作者：
Kalkhoran S;Glantz SA
通讯作者：
Glantz SA