An Inhaler Tracking System Based on Acoustic Analysis: Hardware and SoftwareCitation formats

Standard

An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software. / Xie, Wenyang; Gaydecki, Patrick; Caress, Ann-Louise.

In: IEEE Transactions on Instrumentation and Measurement , 2019.

Research output: Contribution to journalArticlepeer-review

Harvard

Xie, W, Gaydecki, P & Caress, A-L 2019, 'An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software', IEEE Transactions on Instrumentation and Measurement . https://doi.org/10.1109/tim.2018.2886978

APA

Xie, W., Gaydecki, P., & Caress, A-L. (2019). An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software. IEEE Transactions on Instrumentation and Measurement . https://doi.org/10.1109/tim.2018.2886978

Vancouver

Author

Xie, Wenyang ; Gaydecki, Patrick ; Caress, Ann-Louise. / An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software. In: IEEE Transactions on Instrumentation and Measurement . 2019.

Bibtex

@article{85ecbd7be2614801b73a620d345fbf20,
title = "An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software",
abstract = "In treating asthma and chronic obstructive pulmonary disorder (COPD), acquisition of authentic and effective feedback from patients on regimen adherence is difficult. Face-to-face and oral reporting methods do not satisfy current intelligent medication best practices. This paper presents a system to track and analyse daily inhaler usage. A portable electronic device that attaches to the inhaler uses an accelerometer and capacitive sensors to detect users{\textquoteright} motion and an embedded digital microphone to capture sounds while the inhaler is in use. In terms of analysis, sound features are extracted, and breath phases are identified by employing a hidden Markov model (HMM) with a Gaussian mixture model (GMM). A feature template is also constructed and used to search for and identify {\textquoteleft}canister pressed{\textquoteright} events. The system provides objective feedback, quantifying asthma and COPD patients{\textquoteright} adherence to medication regimens. Although interest in asthma adherence to medication regimens is growing, there is still a relative paucity of research and, indeed, compliance devices in this area; the tracking system can help doctors better understand the patient's condition and choose an appropriated treatment plan. At the same time, patients can also improve their self-management by system feedback.",
keywords = "Acoustic monitoring, breath phase identification, inhaler techniques, HMM-GMM, RF, SVM",
author = "Wenyang Xie and Patrick Gaydecki and Ann-Louise Caress",
year = "2019",
doi = "10.1109/tim.2018.2886978",
language = "English",
journal = "IEEE Transactions on Instrumentation and Measurement ",
issn = "0018-9456",
publisher = "IEEE",

}

RIS

TY - JOUR

T1 - An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software

AU - Xie, Wenyang

AU - Gaydecki, Patrick

AU - Caress, Ann-Louise

PY - 2019

Y1 - 2019

N2 - In treating asthma and chronic obstructive pulmonary disorder (COPD), acquisition of authentic and effective feedback from patients on regimen adherence is difficult. Face-to-face and oral reporting methods do not satisfy current intelligent medication best practices. This paper presents a system to track and analyse daily inhaler usage. A portable electronic device that attaches to the inhaler uses an accelerometer and capacitive sensors to detect users’ motion and an embedded digital microphone to capture sounds while the inhaler is in use. In terms of analysis, sound features are extracted, and breath phases are identified by employing a hidden Markov model (HMM) with a Gaussian mixture model (GMM). A feature template is also constructed and used to search for and identify ‘canister pressed’ events. The system provides objective feedback, quantifying asthma and COPD patients’ adherence to medication regimens. Although interest in asthma adherence to medication regimens is growing, there is still a relative paucity of research and, indeed, compliance devices in this area; the tracking system can help doctors better understand the patient's condition and choose an appropriated treatment plan. At the same time, patients can also improve their self-management by system feedback.

AB - In treating asthma and chronic obstructive pulmonary disorder (COPD), acquisition of authentic and effective feedback from patients on regimen adherence is difficult. Face-to-face and oral reporting methods do not satisfy current intelligent medication best practices. This paper presents a system to track and analyse daily inhaler usage. A portable electronic device that attaches to the inhaler uses an accelerometer and capacitive sensors to detect users’ motion and an embedded digital microphone to capture sounds while the inhaler is in use. In terms of analysis, sound features are extracted, and breath phases are identified by employing a hidden Markov model (HMM) with a Gaussian mixture model (GMM). A feature template is also constructed and used to search for and identify ‘canister pressed’ events. The system provides objective feedback, quantifying asthma and COPD patients’ adherence to medication regimens. Although interest in asthma adherence to medication regimens is growing, there is still a relative paucity of research and, indeed, compliance devices in this area; the tracking system can help doctors better understand the patient's condition and choose an appropriated treatment plan. At the same time, patients can also improve their self-management by system feedback.

KW - Acoustic monitoring

KW - breath phase identification

KW - inhaler techniques

KW - HMM-GMM

KW - RF

KW - SVM

U2 - 10.1109/tim.2018.2886978

DO - 10.1109/tim.2018.2886978

M3 - Article

JO - IEEE Transactions on Instrumentation and Measurement

JF - IEEE Transactions on Instrumentation and Measurement

SN - 0018-9456

ER -