Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier

Paulo Lopez-Meyer; Stephen Tiffany; Edward Sazonov

doi:10.1109/EMBC.2012.6346856

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier

Paulo Lopez-Meyer
, Stephen Tiffany
, Edward Sazonov

Psychology

University at Buffalo

University of Alabama

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Scopus citations

Abstract

This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.

Original language	English
Title of host publication	2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012
Pages	4050-4053
Number of pages	4
DOIs	https://doi.org/10.1109/EMBC.2012.6346856
State	Published - 2012
Event	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 - San Diego, CA, United States Duration: Aug 28 2012 → Sep 1 2012

Publication series

Name	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Conference

Conference	34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012
Country/Territory	United States
City	San Diego, CA
Period	08/28/12 → 09/1/12

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10.1109/EMBC.2012.6346856

Cite this

Lopez-Meyer, P., Tiffany, S., & Sazonov, E. (2012). Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012 (pp. 4050-4053). Article 6346856 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). https://doi.org/10.1109/EMBC.2012.6346856

Lopez-Meyer, Paulo ; Tiffany, Stephen ; Sazonov, Edward. / Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. 2012. pp. 4050-4053 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

@inproceedings{f0e0bbc41e1c45cc888fe9b68da03d4d,

title = "Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier",

abstract = "This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87\%, and a resulting recall >80\%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.",

author = "Paulo Lopez-Meyer and Stephen Tiffany and Edward Sazonov",

year = "2012",

doi = "10.1109/EMBC.2012.6346856",

language = "English",

isbn = "9781424441198",

series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",

pages = "4050--4053",

booktitle = "2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012",

note = "34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012 ; Conference date: 28-08-2012 Through 01-09-2012",

}

Lopez-Meyer, P, Tiffany, S & Sazonov, E 2012, Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier. in 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012., 6346856, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 4050-4053, 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012, San Diego, CA, United States, 08/28/12. https://doi.org/10.1109/EMBC.2012.6346856

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier. / Lopez-Meyer, Paulo; Tiffany, Stephen; Sazonov, Edward.
2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. 2012. p. 4050-4053 6346856 (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Lopez-Meyer, Paulo

AU - Tiffany, Stephen

AU - Sazonov, Edward

PY - 2012

Y1 - 2012

N2 - This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.

AB - This study presents a subject-independent model for detection of smoke inhalations from wearable sensors capturing characteristic hand-to-mouth gestures and changes in breathing patterns during cigarette smoking. Wearable sensors were used to detect the proximity of the hand to the mouth and to acquire the respiratory patterns. The waveforms of sensor signals were used as features to build a Support Vector Machine classification model. Across a data set of 20 enrolled participants, precision of correct identification of smoke inhalations was found to be >87%, and a resulting recall >80%. These results suggest that it is possible to analyze smoking behavior by means of a wearable and non-invasive sensor system.

UR - https://www.scopus.com/pages/publications/84880956337

U2 - 10.1109/EMBC.2012.6346856

DO - 10.1109/EMBC.2012.6346856

M3 - Conference contribution

C2 - 23366817

SN - 9781424441198

T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

SP - 4050

EP - 4053

BT - 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012

T2 - 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2012

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ER -

Lopez-Meyer P, Tiffany S, Sazonov E. Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier. In 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2012. 2012. p. 4050-4053. 6346856. (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS). doi: 10.1109/EMBC.2012.6346856

Identification of cigarette smoke inhalations from wearable sensor data using a Support Vector Machine classifier

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