skip to main content
10.1145/2370216.2370261acmconferencesArticle/Chapter ViewAbstractPublication PagesubicompConference Proceedingsconference-collections
research-article

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

Published:05 September 2012Publication 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).Google ScholarGoogle Scholar
  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).Google ScholarGoogle ScholarCross RefCross Ref
  3. Amft, O. and Lukowicz, P. Analysis of chewing sounds for dietary monitoring. UbiComp'05, (2005). Google ScholarGoogle ScholarDigital LibraryDigital 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).Google ScholarGoogle ScholarCross RefCross Ref
  5. Bland, J. M. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 327, 8476 (1986).Google ScholarGoogle Scholar
  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).Google ScholarGoogle ScholarCross RefCross Ref
  7. Brunette, W., Sodt, R., Chaudhri, R., et al. The Open Data Kit Sensors Framework?: Application-Level Sensor Drivers for Android. MobiSys, (2012).Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Cochrane, G. M., Prieto, F., and Clark, T. J. Intrasubject variability of maximal expiratory flow volume curve. Thorax 32, 2 (1977).Google ScholarGoogle ScholarCross RefCross Ref
  9. Finkelstein J, Cabrera MR, H. G. internet-based home asthma telemonitoring: can patients handle the technology. Chest 117, 1 (2000).Google ScholarGoogle ScholarCross RefCross Ref
  10. Flanagan, J. Speech Analysis, Synthesis, and Perception. Springer-Verlag, Berlin - Heidelberg - New York, 1972.Google ScholarGoogle ScholarCross RefCross Ref
  11. Grimaldi, D., Kurylyak, Y., Lamonaca, F., and Nastro, A. Photoplethysmography detection by smartphone's videocamera. IDAACS, (2011).Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle Scholar
  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. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Homs-Corbera, A. and Fiz, J. Time-frequency detection and analysis of wheezes during forced exhalation. IEEE Transactions 51, 1 (2004).Google ScholarGoogle Scholar
  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).Google ScholarGoogle Scholar
  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).Google ScholarGoogle Scholar
  17. Kroutil, J. and Laposa, A. Respiration monitoring during sleeping. ISABEL'11, (2011). Google ScholarGoogle ScholarDigital LibraryDigital 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).Google ScholarGoogle ScholarCross RefCross Ref
  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). Google ScholarGoogle ScholarDigital LibraryDigital 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). Google ScholarGoogle ScholarDigital LibraryDigital 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). Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Miller, M. R., Hankinson, J., Brusasco, V., et al. Standardisation of spirometry. The European Respiratory Journal 26, 2 (2005).Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  24. Neuman, M. R. Vital Signs: Heart Rate. Pulse, IEEE 1, 3 (2010).Google ScholarGoogle Scholar
  25. Nishimura, J. and Kuroda, T. Eating habits monitoring using wireless wearable in-ear microphone. ISWPC 2008, (2008).Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle ScholarCross RefCross Ref
  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. Google ScholarGoogle ScholarDigital LibraryDigital 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.Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle ScholarCross RefCross Ref
  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). Google ScholarGoogle ScholarDigital LibraryDigital 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).Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle ScholarCross RefCross Ref
  33. Savitzky, A. and Golay, M. J. E. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. Analytical Chemistry 36, 8 (1964).Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle Scholar
  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).Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle ScholarCross RefCross Ref
  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).Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  39. Walters, J., Woodibaker, R., and Walls, J. Stability of the EasyOne ultrasonic spirometer for use in general practice. Respirology 11, 3 (2006).Google ScholarGoogle ScholarCross RefCross Ref
  40. Ölmez, T. and Dokur, Z. Classification of heart sounds using an artificial neural network. Pattern Recognition Letters 24, 1--3 (2003). Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

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

    Recommendations

    Comments

    Login options

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

    Sign in
    • 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

      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]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 5 September 2012

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      UbiComp '12 Paper Acceptance Rate58of301submissions,19%Overall Acceptance Rate764of2,912submissions,26%

      Upcoming Conference

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader