research-article

SpiroSmart: using a microphone to measure lung function on a mobile phone

Authors:

Eric C. Larson,

Gaetano Boriello,

Margaret Rosenfeld,

Shwetak N. PatelAuthors Info & Claims

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

Pages 280 - 289

https://doi.org/10.1145/2370216.2370261

Published: 05 September 2012 Publication History

Abstract

Home spirometry is gaining acceptance in the medical community because of its ability to detect pulmonary exacerbations and improve outcomes of chronic lung ailments. However, cost and usability are significant barriers to its widespread adoption. To this end, we present SpiroSmart, a low-cost mobile phone application that performs spirometry sensing using the built-in microphone. We evaluate SpiroSmart on 52 subjects, showing that the mean error when compared to a clinical spirometer is 5.1% for common measures of lung function. Finally, we show that pulmonologists can use SpiroSmart to diagnose varying degrees of obstructive lung ailments.

References

[1]

Allen, J. and Murray, A. Time-frequency analysis of Korotkoff sounds. IEE Seminar Digests 1997, 6 (1997).

[2]

Alshaer, H., Fernie, G. R., and Bradley, T. D. Phase tracking of the breathing cycle in sleeping subjects by frequency analysis of acoustic data. International Journal of Healthcare Technology and Management 11, 3 (2010).

[3]

Amft, O. and Lukowicz, P. Analysis of chewing sounds for dietary monitoring. UbiComp'05, (2005).

Digital Library

[4]

Bishara, W., Su, T.-W., Coskun, A. F., and Ozcan, A. Lensfree on-chip microscopy over a wide field-of-view using pixel super-resolution. Opt. Express 18, 11 (2010).

[5]

Bland, J. M. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327, 8476 (1986).

[6]

Brouwer, a F. J., Roorda, R. J., and Brand, P. L. P. Home spirometry and asthma severity in children. The European Respiratory Journal 28, 6 (2006).

[7]

Brunette, W., Sodt, R., Chaudhri, R., et al. The Open Data Kit Sensors Framework?: Application-Level Sensor Drivers for Android. MobiSys, (2012).

Digital Library

[8]

Cochrane, G. M., Prieto, F., and Clark, T. J. Intrasubject variability of maximal expiratory flow volume curve. Thorax 32, 2 (1977).

[9]

Finkelstein J, Cabrera MR, H. G. internet-based home asthma telemonitoring: can patients handle the technology. Chest 117, 1 (2000).

[10]

Flanagan, J. Speech Analysis, Synthesis, and Perception. Springer-Verlag, Berlin - Heidelberg - New York, 1972.

[11]

Grimaldi, D., Kurylyak, Y., Lamonaca, F., and Nastro, A. Photoplethysmography detection by smartphone's videocamera. IDAACS, (2011).

[12]

Grzincich, G., Gagliardini, R., and Bossi, A. Evaluation of a home telemonitoring service for adult patients with cystic fibrosis: a pilot study. J. of Telemedicine, (2010).

[13]

Gupta, S., Chang, P., Anyigbo, N., and Sabharwal, A. mobileSpiro: accurate mobile spirometry for self-management of asthma. Proceedings of the First ACM Workshop on Mobile Systems, Applications, and Services for Healthcare, ACM (2011), 1:1--1:6.

Digital Library

[14]

Homs-Corbera, A. and Fiz, J. Time-frequency detection and analysis of wheezes during forced exhalation. IEEE Transactions 51, 1 (2004).

[15]

Kessler R, Stahl E, Vogelmeier C, Haughney J, Trudeau E, Lofdahl CG, et al. Patient understanding, detection, and experience of COPD exacerbations: an observational, interview-based study. Chest 130, (2006).

[16]

Knudson, R. J., Slatin, R. C., Lebowitz, M. D., and Burrows, B. The maximal expiratory flow-volume curve. Normal standards, variability, and effects of age. The American review of respiratory disease 113, 5 (1976).

[17]

Kroutil, J. and Laposa, A. Respiration monitoring during sleeping. ISABEL'11, (2011).

Digital Library

[18]

Künzli, N., Ackermann-Liebrich, U., Keller, R., Perruchoud, A. P., and Schindler, C. Variability of FVC and FEV1 due to technician, team, device and subject in an eight centre study: three quality control studies in SAPALDIA. European Respiratory Journal 8, 3 (1995).

[19]

Lafferty, J., McCallum, A., and Pereira, F. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. Proc. Int. Conf. on Machine Learning, (2001).

Digital Library

[20]

Larson, E. C., Lee, T., Liu, S., Rosenfeld, M., and Patel, S. N. Accurate and Privacy Preserving Cough Sensing using a Low-Cost Microphone. Proceedings of the 13th ACM international conference on Ubiquitous computing, (2011).

Digital Library

[21]

Majchrzak, T. and Chakravorty, A. Improving the Compliance of Transplantation Medicine Patients with an Integrated Mobile System. International Conference on System Sciences, (2012).

Digital Library

[22]

Miller, M. R., Hankinson, J., Brusasco, V., et al. Standardisation of spirometry. The European Respiratory Journal 26, 2 (2005).

[23]

MiravitallesS, M., Murio, C., Guerrero, T., and Gisbert, R. Pharmacoeconomic evaluation of acute exacerbations of chronic bronchitis and COPD. Chest 121, 5, 1449--1455.

[24]

Neuman, M. R. Vital Signs: Heart Rate. Pulse, IEEE 1, 3 (2010).

[25]

Nishimura, J. and Kuroda, T. Eating habits monitoring using wireless wearable in-ear microphone. ISWPC 2008, (2008).

[26]

Otulana, B., Higenbottam, T., Ferrari, L., Scott, J., Igboaka, G., and Wallwork, J. The use of home spirometry in detecting acute lung rejection and infection following heart-lung transplantation. Chest 97, 2 (1990).

[27]

Pamplona, V. F., Mohan, A., Oliveira, M. M., and Raskar, R. NETRA: interactive display for estimating refractive errors and focal range. SIGGRAPH'10, ACM (2010), 77:1--77:8.

Digital Library

[28]

Pesola, G., O'Donnell, P., and Jr, G. P. Peak expiratory flow in normals: comparison of the Mini Wright versus spirometric predicted peak flows. Journal of Asthma, 4 (2009), 845--848.

[29]

Poh, M.-Z., McDuff, D. J., and Picard, R. W. Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18, 10 (2010).

[30]

Poh, M.-Z., Swenson, N. C., and Picard, R. W. Motion-Tolerant Magnetic Earring Sensor and Wireless Earpiece for Wearable Photoplethysmography. Information Technology in Biomedicine, IEEE Transactions on 14, 3 (2010).

Digital Library

[31]

Rebuck, D. a., Hanania, N. a., D'Urzo, a. D., and Chapman, K. R. The Accuracy of a Handheld Portable Spirometer. Chest 109, 1 (1996).

[32]

Rubini, A., Parmagnani, A., Redaelli, M., Bondì, M., Del Monte, D., and Catena, V. Daily variations of spirometric indexes and maximum expiratory pressure in young healthy adults. Biological Rhythm Research 41, 2 (2010).

[33]

Savitzky, A. and Golay, M. J. E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 36, 8 (1964).

[34]

Seemungal, T. a, Donaldson, G. C., Bhowmik, A., Jeffries, D. J., and Wedzicha, J. a. Time course and recovery of exacerbations in patients with chronic obstructive pulmonary disease. American journal of respiratory and critical care medicine 161, 5 (2000).

[35]

Sevick, M., Trauth, J., Ling, B., et al. Patients with Complex Chronic Diseases: Perspectives on Supporting Self-Management. Journal of General Internal Medicine 22, 0 (2007).

[36]

Swanney, M. P., Ruppel, G., Enright, P. L., et al. Using the lower limit of normal for the FEV1/FVC ratio reduces the misclassification of airway obstruction. Thorax 63, 12 (2008).

[37]

Townsend, M. C. Spirometry in the occupational health setting. Journal of occupational and environmental medicine/American College of Occupational and Environmental Medicine 53, 5 (2011).

[38]

Wakita, H. Direct estimation of the vocal tract shape by inverse filtering of acoustic speech waveforms. Audio and Electroacoustics, IEEE Transactions on 21, 5 (1973), 417--427.

[39]

Walters, J., Woodibaker, R., and Walls, J. Stability of the EasyOne ultrasonic spirometer for use in general practice. Respirology 11, 3 (2006).

[40]

Ölmez, T. and Dokur, Z. Classification of heart sounds using an artificial neural network. Pattern Recognition Letters 24, 1--3 (2003).

Digital Library

Cited By

Spence ABangay S(2024)Domain-Agnostic Representation of Side-ChannelsEntropy10.3390/e2608068426:8(684)Online publication date: 13-Aug-2024
https://doi.org/10.3390/e26080684
Bhargava HSalomon CSuresh SChang AKilian RStijn DOriol ALow DKnebel ATaraman S(2024)Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in PediatricsJournal of Medical Internet Research10.2196/4902226(e49022)Online publication date: 29-Feb-2024
https://doi.org/10.2196/49022
Ganatra VGun SJoshi PBalasubramaniam AMurali KKwatra NJain M(2024)DEDector: Smartphone-Based Noninvasive Screening of Dry Eye DiseaseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997428:4(1-26)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699742
Show More Cited By

Index Terms

SpiroSmart: using a microphone to measure lung function on a mobile phone
1. Human-centered computing

Recommendations

Bilicam: using mobile phones to monitor newborn jaundice
UbiComp '14: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing

Health sensing through smartphones has received considerable attention in recent years because of the devices' ubiquity and promise to lower the barrier for tracking medical conditions. In this paper, we focus on using smartphones to monitor newborn ...
Effects of homogeneous and heterogeneous changes in the lung periphery on spirometry results
Highlights
- The paper presents a computational model for the respiratory system during spirometric manoeuvre used for a systematic study on the effects of peripheral ...
Abstract Background and objectives
The most widespread chronic pulmonary disorders are associated with heterogeneous changes in the lung periphery and spirometry is the most commonly used test to monitor these diseases. So far only a ...
Data based Modelling of Expired Airflow Clarifies Chronic Obstructive Pulmonary Disease
BIOSTEC 2014: Proceedings of the International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4

One of the major health challenges of the future is Chronic Obstructive Pulmonary Disease (COPD). It is characterized by airflow limitations, although current diagnosis does not give attention to the flow measurements. We aimed to develop a data-based ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

UbiComp '12: Proceedings of the 2012 ACM Conference on Ubiquitous Computing

September 2012

1268 pages

ISBN:9781450312240

DOI:10.1145/2370216

General Chair:
Anind K. Dey
Carnegie Mellon University
,
Program Chairs:
Hao-Hua Chu
National Taiwan University, Taiwan
,
Gillian Hayes
University of California, Irvine

Copyright © 2012 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 September 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

Ubicomp '12

Sponsor:

Ubicomp '12: The 2012 ACM Conference on Ubiquitous Computing

September 5 - 8, 2012

Pennsylvania, Pittsburgh

Acceptance Rates

UbiComp '12 Paper Acceptance Rate 58 of 301 submissions, 19%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

159
Total Citations
View Citations
1,738
Total Downloads

Downloads (Last 12 months)145
Downloads (Last 6 weeks)24

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Spence ABangay S(2024)Domain-Agnostic Representation of Side-ChannelsEntropy10.3390/e2608068426:8(684)Online publication date: 13-Aug-2024
https://doi.org/10.3390/e26080684
Bhargava HSalomon CSuresh SChang AKilian RStijn DOriol ALow DKnebel ATaraman S(2024)Promises, Pitfalls, and Clinical Applications of Artificial Intelligence in PediatricsJournal of Medical Internet Research10.2196/4902226(e49022)Online publication date: 29-Feb-2024
https://doi.org/10.2196/49022
Ganatra VGun SJoshi PBalasubramaniam AMurali KKwatra NJain M(2024)DEDector: Smartphone-Based Noninvasive Screening of Dry Eye DiseaseProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997428:4(1-26)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699742
Kouaho WEpstein D(2024)Investigating Perspectives of and Experiences with Low Cost Commercial Fitness WearablesProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36997408:4(1-22)Online publication date: 21-Nov-2024
https://dl.acm.org/doi/10.1145/3699740
Abu Ali NRehman MMumtaz SKhan MHayajneh MUllah FShah R(2024)Contactless Diseases Diagnoses Using Wireless Communication Sensing: Methods and Challenges SurveyACM Computing Surveys10.1145/364835256:9(1-29)Online publication date: 16-Feb-2024
https://dl.acm.org/doi/10.1145/3648352
Adhikary RSadeh MBatra NGoel M(2024)JoulesEyeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36314227:4(1-29)Online publication date: 12-Jan-2024
https://dl.acm.org/doi/10.1145/3631422
Lee JYoon SLee KJeong ECho JPark WYim DHwang I(2024)Open Sesame? Open Salami! Personalizing Vocabulary Assessment-Intervention for Children via Pervasive Profiling and Bespoke Storybook GenerationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642580(1-32)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642580
Tripathi GSabherwal MSingh P(2024)“I know I have this till my Last Breath”: Unmasking the Gaps in Chronic Obstructive Pulmonary Disease (COPD) Care in IndiaProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642504(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642504
Fascetti APestonjamasp ABarry CWang E(2024)Postable Spirometry: User-Assembled Vortex Whistle Spirometer2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC53108.2024.10781682(1-4)Online publication date: 15-Jul-2024
https://doi.org/10.1109/EMBC53108.2024.10781682
Karaarslan OBelcastro KErgen O(2024)Respiratory sound-base disease classification and characterization with deep/machine learning techniquesBiomedical Signal Processing and Control10.1016/j.bspc.2023.10557087(105570)Online publication date: Jan-2024
https://doi.org/10.1016/j.bspc.2023.105570
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten