# Electromyogram in Cigarette Smoking Activity Recognition
## 1 Introduction
The model also accomplished an F1-score of 0.83 when applied to four users wearing IMUs at free living for three days at a rate of 4 h/day. In [21], four 3-axis accelerometers were placed at the dominant and non-dominant wrist, dominant upper arm, and ankle and detection of both smoking events and puff were reported. This study was performed on six subjects to collect data ( 11.8 h) on different daily activities including eating, reading, working on a computer and using a phone, etc. For 5-fold cross-validation, the model based on the random forest classifier reported as an F1-score of 0.79 for cigarette smoking detection. In [22], the authors use a smartwatch on the wrist for detecting smoking activities. They collected 45 h of data from 11 subjects who completed seven daily activities. For a leave-one-subject-out validation, the model reached the F1-score ranging from 0.83 to 0.94. In [23], four 6-axis IMUs were placed at the dominant wrist and shoulder, and elbows on six participants, and 3.5 h of data was collected from each participant in a controlled laboratory setting. For different participants, the F1-scores between 0.08 and 0.86 were reported. In [24], a model was proposed employing a RIP sensor to detect inhale-exhale pattern and two IMUs to detect hand gestures. The study was performed on six smokers, which delivered a total of 291 puffs. For 10-fold cross-validation. The support vector-based classifier reached an F1-score of 0.91 for smoking detection. In our previous study [25], an algorithm was proposed for detection of hand-to-mouth gestures by using single IMU on the wrist and an instrumented lighter. This approach was evaluated from 35 subjects generating a dataset including 55 h from a controlled setting and 816 h from free-living conditions. This support vector machine-based approached achieved an F1-score of 0.86 for puff detection in the controlled environment and 0.85 for the detection of free-living smoking events. In summary, all previous studies mainly focused on the motion of the forearm to identify hand-to-mouth gestures associated with smoking. However, the motion of the forearm is not sufficient for high accuracy detection of smoking in free-living. Besides, smoking-related gestures are not limited to forearm motions. Many other motions that include finger and hand gestures are also actively engaged during cigarette smoking. Table 2 summarizes possible motions and involved muscles during cigarette smoking [26,27]. Some researchers used multiple IMUs on a different part of the body to obtain higher detection accuracy, such as non-dominant arm, upper-arm, wrist, and chest. Nevertheless, these approaches increase the of devices on users' body and make the data acquisition more obstructive for long-term usage. This study overcomes this problem by utilizing sEMG signals. By using a single wearable device, both the motion of forearm, via an IMU, and the motion of hand/finger, via sEMG signals, are followed. To the best of our knowledge, usage of sEMG signals have not been investigated for detection of cigarette smoking. \begin{table}
\begin{tabular}{l l} \hline \hline
**Motion** & **Activated Muscles** \\ \hline Elbow flexion (EF) & Biceps brachii, Brachioradialis, Brachialis \\ Elbow extension (EE) & Anconeus, Triceps brachii \\ Forearm supination (FS) & Supinator, Biceps brachii (long head) \\ Forearm pronation (FP) & Pronator quadratus, Pronator trees \\ Wrist flexion (WF) & Flexor carpi radialis, Flexor carpi ulnaris, Palmaris longus \\ Wrist extension (WE) & Extensor carpi radialis longus, Extensor carpi radialis brevis, Extensor \\  & carpi ulnaris \\ Tip pinch & Extensor Digitorium, Abductor Pollicis Longus, Flexor Digitorum Profundus \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Possible motions and activated muscles for cigarette smoking. Different sensor approaches were proposed to detect finger and hand movements across a wide range of applications such as sign language translation, human-computer interaction, health care, and virtual reality.