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Related Experiment Video

Updated: Aug 21, 2025

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

Electromyogram in Cigarette Smoking Activity Recognition.

Volkan Senyurek¹, Masudul Imtiaz², Prajakta Belsare³

¹Geosystems Research Institute, Mississippi State University, Starkville, MS 39759, USA.

|November 16, 2022

Summary

This summary is machine-generated.

Surface electromyogram (sEMG) signals can detect smoking gestures. Combining sEMG with inertial measurement unit (IMU) sensors improves accuracy to 84% for recognizing smoking activity.

Keywords:

CNN LSTM Myo cigarette smoking wearable sensors

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Area of Science:

Biomedical Engineering
Wearable Technology
Human-Computer Interaction

Background:

Cigarette smoking recognition is crucial for health monitoring and behavior analysis.
Wearable sensors offer a non-invasive method for detecting daily activities, including smoking.

Purpose of the Study:

To evaluate the effectiveness of surface electromyogram (sEMG) signals for recognizing cigarette smoking gestures.
To assess the performance improvement of sEMG when combined with inertial measurement unit (IMU) sensors.

Main Methods:

sEMG signals from the lower arm were collected from 16 subjects performing daily activities.
Convolutional and recurrent neural networks were used to analyze sEMG and IMU data.
Leave-one-subject-out cross-validation was employed for person-independent evaluation.

Main Results:

sEMG alone achieved a 75% F1-score for smoking detection.
IMU alone achieved an 81% F1-score.
The combination of sEMG and IMU sensors reached an 84% F1-score.

Conclusions:

sEMG signals alone do not outperform IMU signals for smoking detection.
Combining sEMG with IMU sensors enhances the accuracy of cigarette smoking recognition.
This multimodal approach improves smoking detection without requiring additional hardware.