# Electromyogram in Cigarette Smoking Activity Recognition
## 1 Introduction  
According to the World Health Organization (WHO), smoking is a widespread public health problem that has resulted in millions of deaths per year [1]. Smoking causes ten percent of all annual deaths in the world and causes productivity loss, health care expenditures, and other related costs, which correspond to 1.8% of the world's annual gross domestic product [2; 3]. The statistics show the importance of quitting smoking and smoking cessation programs. Understanding the patient's smoking pattern over time is the primary step of cessation programs. An accurate estimation of smoking activity in an unobstructed way that not only will enhance the efficacy of smoking cessation programs and contribute useful information about smoking behavior, but also enable just-in-time interventions aimed at the control of smoking and reducing smoking-related diseases and deaths [4]. Currently, wearable sensors/devices have attracted attention as a tool for monitoring people's daily behavioral and physiological activities to observe and understand human behavior and health conditions [5; 6]. One of the application areas has been the monitoring of cigarette smoking. Accurate tracking of smoking patterns will not only contribute to our understanding mechanisms underlying smoking, but should also increase the efficacy of our smoking interventions [7; 8]. Several studies have evaluated different sensor modalities to automatically detect cigarette smoking in the daily life of smokers [9; 10; 11; 12; 13]. One approach has been to measure the respiration pattern. A Respiratory Inductance Plethysmography (RIP) band is a sensor thatmeasures characteristic breathing patterns associated with smoking. The metrics related to smoke inhalation (puff duration, puff volume, and inhale and exhale duration) can be obtained through the analysis of breathing patterns [14]. Measurement of the pattern of breathing, however, is highly sensitive to the motion of hand and body, and challenging for long-term data collection under free-living conditions [15,16]. Another significant approach for the detection of smoking employs motion sensors to detect the signature arm/hand movement associated with a smoking activity. Many researchers have used single or multiple inertial measurement units (IMU) [17, 18, 19, 20] worn on the body, such as on the wrist, upper arm, or ankle to detect cigarette smoking activities. A summary of prior studies related to smoking detection via wearable sensors is provided in Table 1.  
In [19], arm gestures related to cigarette smoking were detected from two 9-D IMUs (such as the combination of accelerometer, magnetometer, and gyroscope) placed at the wrist and elbow. This study was performed on 15 subjects to collect data on different activities including smoking, eating, drinking, etc. As a predictor of smoking, this study \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study** & **Sensor Type** & **F1-Score** & **Validation** & **Dataset** & **Classifier** & **Sensor Location (Red)** \\  & & & & & & **Measurement Area (Blue)** \\ \hline
[19] & 9D-IMU & 0.85 & 10-fold & 15 subjects & RF & \\  & & & & & \\
[21] & 3D-IMU & 0.79 & 5-fold & 6 subjects (11.8 h) & RF & \\  & & & & & \\
[22] & 6D-IMU & 0.83–0.94 & LOSO & 11 subjects (45 h) & Hierarchical & \\  & & & & & \\
[23] & 6D-IMU & 0.08–0.86 & - & 6 subjects (21 h) & SVM, Edge det. & \\  & & & & & \\
[24] & RIP & 0.91 & 10-fold & 6 subjects (40 h) & SVM \\  & & & & & \\
[25] & 6D-IMU & 0.85 & LOSO & 35 subjects (816 h) & SVM \\  & & & & & \\ this study & sEMG & 0.84 & LOSO & 16 subjects (25 h) & CNN-LSTM & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the studies employing wearable sensors for smoking monitoring. computed the relative position of the elbow to the wrist. A random forest classification model was used to predict motions. For the 10-fold cross-validation, the model accomplished an F1-score of 0.85.