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A convolutional neural network for smoking activity recognition

A convolutional neural network for smoking activity recognition
A convolutional neural network for smoking activity recognition
Smoking is linked to more than two million preventable deaths yearly. The widespread use of sensors embedded in everyday devices provides novel means for research on smoking. Smartphones and smartwatches can monitor smoking behavior, which could lead to the development of new methods for smoking reduction or cessation. However, smoking often co-occurs with other activities, such as drinking and eating, which makes the recognition of concurrent and overlapping smoking activities from wearable sensors challenging. In this paper, we proposed for the first time to use deep learning for the automatic detection of smoking activities. A Convolutional Neural Network (CNN) architecture was proposed, and this improved on previously reported performance results. We investigated the impact of various data preprocessing approaches that influence the CNN classification results with statistical features and raw sensor data. We also considered the individual performance of the smartwatch vs. the smartphone and the gyroscope vs. accelerometer sensors for smoking activity recognition. Considering a dataset of concurrent activities such as drinking, eating, smoking while sitting, standing, walking, and partaking in a group conversation, our CNN approach obtained an F1-score of 92-96% in person-independent evaluation.
IEEE
Alharbi, Fayez
871a8d43-07b1-4cf4-9ef4-0b4395d9ab08
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Alharbi, Fayez
871a8d43-07b1-4cf4-9ef4-0b4395d9ab08
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb

Alharbi, Fayez and Farrahi, Katayoun (2018) A convolutional neural network for smoking activity recognition. In 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom). IEEE. 6 pp . (doi:10.1109/HealthCom.2018.8531148).

Record type: Conference or Workshop Item (Paper)

Abstract

Smoking is linked to more than two million preventable deaths yearly. The widespread use of sensors embedded in everyday devices provides novel means for research on smoking. Smartphones and smartwatches can monitor smoking behavior, which could lead to the development of new methods for smoking reduction or cessation. However, smoking often co-occurs with other activities, such as drinking and eating, which makes the recognition of concurrent and overlapping smoking activities from wearable sensors challenging. In this paper, we proposed for the first time to use deep learning for the automatic detection of smoking activities. A Convolutional Neural Network (CNN) architecture was proposed, and this improved on previously reported performance results. We investigated the impact of various data preprocessing approaches that influence the CNN classification results with statistical features and raw sensor data. We also considered the individual performance of the smartwatch vs. the smartphone and the gyroscope vs. accelerometer sensors for smoking activity recognition. Considering a dataset of concurrent activities such as drinking, eating, smoking while sitting, standing, walking, and partaking in a group conversation, our CNN approach obtained an F1-score of 92-96% in person-independent evaluation.

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More information

Accepted/In Press date: 20 June 2018
e-pub ahead of print date: 17 September 2018
Published date: 12 November 2018
Venue - Dates: IEEE International Conference on E-health Networking, Application & Services, Dolní Vítkovice area, Czech Republic, 2018-09-16 - 2018-09-19

Identifiers

Local EPrints ID: 425452
URI: http://eprints.soton.ac.uk/id/eprint/425452
PURE UUID: cdcb0257-a641-4967-af3a-2147775ee8c7

Catalogue record

Date deposited: 19 Oct 2018 16:30
Last modified: 06 Oct 2020 22:29

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