Recognition of Smoking Gesture Using Smart Watch Technology

TL;DR

This study demonstrates that accelerometer data from smart watches, analyzed with neural networks, can accurately identify smoking gestures, enabling potential health interventions and continuous activity monitoring.

Contribution

The paper introduces a method using accelerometer sensors and neural networks to reliably detect smoking gestures with high accuracy from smartwatch data.

Findings

Achieved 85%-95% success rate in smoking gesture recognition.

X-axis accelerometer data is most effective for identifying smoking gestures.

High accuracy (>90%) achieved across different devices and participants.

Abstract

Diseases resulting from prolonged smoking are the most common preventable causes of death in the world today. In this report we investigate the success of utilizing accelerometer sensors in smart watches to identify smoking gestures. Early identification of smoking gestures can help to initiate the appropriate intervention method and prevent relapses in smoking. Our experiments indicate 85%-95% success rates in identification of smoking gesture among other similar gestures using Artificial Neural Networks (ANNs). Our investigations concluded that information obtained from the x-dimension of accelerometers is the best means of identifying the smoking gesture, while y and z dimensions are helpful in eliminating other gestures such as: eating, drinking, and scratch of nose. We utilized sensor data from the Apple Watch during the training of the ANN. Using sensor data from another participant collected on Pebble Steel, we obtained a smoking identification accuracy of greater than 90% when using an ANN trained on data previously collected from the Apple Watch. Finally, we have demonstrated the possibility of using smart watches to perform continuous monitoring of daily activities.