TY - GEN
T1 - Recognizing cigarette smoke inhalations using hidden Markov models
AU - Ramos-Garcia, Raul I.
AU - Sazonov, Edward
AU - Tiffany, Stephen
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/13
Y1 - 2017/9/13
N2 - Previous studies with the Personal Automatic Cigarette Tracker (PACT) wearable system have found that smoking presents a distinct temporal breathing pattern, which might be well-suited for recognition by hidden Markov models (HMMs). In this work, we explored the feasibility of using HMMs to characterize the temporal information of smoking inhalations contained in the respiratory signals such as tidal volume, airflow, and the signal from the hand-to-mouth proximity sensor. Left-to-right HMMs were built to classify smoking and non-smoking inhalations using either only the respiratory signals, or both respiratory and hand proximity signals. Using a data set of 20 subjects, a leave-one-out cross-validation was performed on each HMM. In the recognition of smoke inhalations, the highest average recall, precision and F-score perceived by the HMMs was 42.39%, 88.19% and 56.38%, respectively, providing a 7.3% improvement in recall against a previously reported Support Vector Machines.
AB - Previous studies with the Personal Automatic Cigarette Tracker (PACT) wearable system have found that smoking presents a distinct temporal breathing pattern, which might be well-suited for recognition by hidden Markov models (HMMs). In this work, we explored the feasibility of using HMMs to characterize the temporal information of smoking inhalations contained in the respiratory signals such as tidal volume, airflow, and the signal from the hand-to-mouth proximity sensor. Left-to-right HMMs were built to classify smoking and non-smoking inhalations using either only the respiratory signals, or both respiratory and hand proximity signals. Using a data set of 20 subjects, a leave-one-out cross-validation was performed on each HMM. In the recognition of smoke inhalations, the highest average recall, precision and F-score perceived by the HMMs was 42.39%, 88.19% and 56.38%, respectively, providing a 7.3% improvement in recall against a previously reported Support Vector Machines.
KW - Cigarette smoking
KW - Hidden Markov models
KW - Wearable sensors
UR - https://www.scopus.com/pages/publications/85032221861
U2 - 10.1109/EMBC.2017.8037056
DO - 10.1109/EMBC.2017.8037056
M3 - Conference contribution
C2 - 29060101
AN - SCOPUS:85032221861
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1242
EP - 1245
BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Y2 - 11 July 2017 through 15 July 2017
ER -