Methods for Breathing Rate Measurement through Mobile Platform: a Review
Resumo
Breathing rate is a vital sign that can indicate someone’s health status and even detect early diseases. Mobile health applications might become the main tool for estimating breathing rate out of the clinical environment. In this research, a review of the literature is conducted, aiming at finding out the most recent researches that have been proposed as solutions for respiratory measurement or monitoring using mobile devices. We discuss and compare their methods, highlighting pros and cons regarding ubiquity and feasibility. The results indicate that the combination of methods is a key aspect to improve measurements.
Referências
Black J., Gerdtz, M., Nicholson, P., Crellin, D., Browning, L., Simpson, J., Bell, L. and Santamaria, N. (2015) “Can simple mobile phone applications provide reliable counts of respiratory rates in sick and children? An initial evaluation of three new applications”, International Journal of Nursing Studies 52, pages 963-969. http://dx.doi.org/10.1016/j.ijnurstu.2015.01.016
Cho, Y., Julier, S. J., Marquardt, N. and Berthouze, N. B. (2017) “Robust tracking of respiratory rate in high-dynamic range scene using mobile thermal imaging”, Biomedical Optics Express, Vol. 7, No. 10, pages 4480-4503. http://dx.doi.org/10.1364/BOE.8.004480
Gan, H., Karlen, W., Dunsmuir, D., Zhou, G., Chiu, M., Dumont, G. A. and Ansermino, J. M. (2015) “The performance of a Mobile Phone Respiratory Rate Counter Compared to the WHO ARI Timer”, Journal of Healthcare Engineering, Vol. 6, No. 4, pages 691-704. http://dx.doi.org/10.1260/2040-2295.6.4.691
Gu, F., Niu, J., Das, S. K., He, Z. and Jin, X. (2017) “Detecting breathing frequency and maintaining a proper running rhythm”, Pervasive and Mobile Computing 42, pages498-512. http://dx.doi.org/10.1016/j.pmcj.2017.06.015
Hashizume, T., Arizono, T. and Yatani, K. (2017) “Auth ‘n’ Scan: Opportunistic Photoplethysmography in Mobile Fingerprint Authentication”, PACM Interact. Mob. Wearable Ubiquitous Technologies, Vol. 1, No. 4, Article 137. http://dx.doi.org/10.1145/3161189
Huang, C., Chen, H., Yang, L. and Zhang, Q. (2018) “BreathLive: Liveness Detection for Heart Sound Authentication with Deep Breathing”, PACM Interact. Mob. Wearable Ubiquitous Technologies, Vol. 2, No. 1, Article 12. http://dx.doi.org/10.1145/3191744
Karlen, W., Gan, H., Chiu, M., Dunsmuir, D., Zhou, G., Dumont, G. A. and Ansermino, J. M. (2014) “Improving the Accuracy and Efficiency of Respiratory Rate Measurements in Children Using Mobile Devices”, PloS ONE 9(6): e99266, Vol. 9, Issue 6. http://dx.doi.org/10.1371/journal.pone.0099266
Lázaro, J., Nam, Y., Gil, E., Laguna, P. and Chon, K. H. (2015) “Respiratory rate derived from smartphone-camera-acquired pulse photoplethysmographic signals”, Physiological Measurement 36, Pages 2317-2333.
Liu, B., Dai, X., Gong, H., Guo, Z., Liu, N., Wang, X. and Liu, M. (2018) “Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model”, Hindawi Mobile Information Systems, Vol. 2018, Article ID 5214067. http://dx.doi.org/10.1155/2018/5214067
Milosevic, M., Milenkovic, A. and Jovanov, E. (2013) “mHealth@UAH: Computing infrastructure for mobile health and wellness monitoring”, XRDS, Vol. 20, No. 2 pages 43-49. http://dx.doi.org/10.1145/2539269
Nam, Y., Reyes, B. A. and Cho K. H. (2016) “Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset”, IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 6, pages1493-1501. http://dx.doi.org/10.1109/JBHI.2015.2480838
Narayan, S., Shivdare, P., Niranjan, T., Williams, K., Freudman, J. and Sehra, R. (2018) “Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography”, Sleep Breath (2018).
Oletic, D. and Bilas, V. (2016) “Energy-Efficient Respiratory Sounds Sensing for Personal Mobile Asthma Monitoring”, IEEE Sensors Journal, Vol. 16, No. 23, pages 8295-8303. http://dx.doi.org/10.1109/JSEN.2016.2585039
Patwari, N., Wilson, J., Ananthanarayanan, S., Kasera, S. K. and Westenskow, D. R. (2014) “Monitoring Breathing via Signal Strength in Wireless Networks”, IEEE Transactions on Mobile Computing, Vol. 13, No. 8, pages 1774-1786. http://dx.doi.org/10.1109/TMC.2013.117
Sohn, K., Merchant, F. M., Sayadi, O., Puppala, D., Doddamani, R., Sahani, A., Singh, J. P., Heist, E. K., Isselbacher, E. M. and Armoundas, A. A. (2017) “A Novel Point-of-Care Smartphone Based System for Monitoring the Cardiac and Respiratory Systems”, Scientific Report 7, 44946.
Sun, X., Qiu, L., Wu, Y., Tang, Y. and Cao, G. (2017) “SleepMonitor: Monitoring Respiratory Rate and Body Position During Sleep Using Smartwatch”, PACM Interact. Mob. Wearable Ubiquitous Technologies, Vol. 1, No. 3, Article 104. http://dx.doi.org/10.1145/3130969
Wanneburg, J. and Malekian, R. (2015) “Body Sensor Network for Mobile Health Monitoring, a Diagnosis and anticipating System”, IEEE Sensors Journal, Vol. 15, No. 12, pages 6839-6852. http://dx.doi.org/10.1109/JSEN.2015.2464773
Cho, Y., Julier, S. J., Marquardt, N. and Berthouze, N. B. (2017) “Robust tracking of respiratory rate in high-dynamic range scene using mobile thermal imaging”, Biomedical Optics Express, Vol. 7, No. 10, pages 4480-4503. http://dx.doi.org/10.1364/BOE.8.004480
Gan, H., Karlen, W., Dunsmuir, D., Zhou, G., Chiu, M., Dumont, G. A. and Ansermino, J. M. (2015) “The performance of a Mobile Phone Respiratory Rate Counter Compared to the WHO ARI Timer”, Journal of Healthcare Engineering, Vol. 6, No. 4, pages 691-704. http://dx.doi.org/10.1260/2040-2295.6.4.691
Gu, F., Niu, J., Das, S. K., He, Z. and Jin, X. (2017) “Detecting breathing frequency and maintaining a proper running rhythm”, Pervasive and Mobile Computing 42, pages498-512. http://dx.doi.org/10.1016/j.pmcj.2017.06.015
Hashizume, T., Arizono, T. and Yatani, K. (2017) “Auth ‘n’ Scan: Opportunistic Photoplethysmography in Mobile Fingerprint Authentication”, PACM Interact. Mob. Wearable Ubiquitous Technologies, Vol. 1, No. 4, Article 137. http://dx.doi.org/10.1145/3161189
Huang, C., Chen, H., Yang, L. and Zhang, Q. (2018) “BreathLive: Liveness Detection for Heart Sound Authentication with Deep Breathing”, PACM Interact. Mob. Wearable Ubiquitous Technologies, Vol. 2, No. 1, Article 12. http://dx.doi.org/10.1145/3191744
Karlen, W., Gan, H., Chiu, M., Dunsmuir, D., Zhou, G., Dumont, G. A. and Ansermino, J. M. (2014) “Improving the Accuracy and Efficiency of Respiratory Rate Measurements in Children Using Mobile Devices”, PloS ONE 9(6): e99266, Vol. 9, Issue 6. http://dx.doi.org/10.1371/journal.pone.0099266
Lázaro, J., Nam, Y., Gil, E., Laguna, P. and Chon, K. H. (2015) “Respiratory rate derived from smartphone-camera-acquired pulse photoplethysmographic signals”, Physiological Measurement 36, Pages 2317-2333.
Liu, B., Dai, X., Gong, H., Guo, Z., Liu, N., Wang, X. and Liu, M. (2018) “Deep Learning versus Professional Healthcare Equipment: A Fine-Grained Breathing Rate Monitoring Model”, Hindawi Mobile Information Systems, Vol. 2018, Article ID 5214067. http://dx.doi.org/10.1155/2018/5214067
Milosevic, M., Milenkovic, A. and Jovanov, E. (2013) “mHealth@UAH: Computing infrastructure for mobile health and wellness monitoring”, XRDS, Vol. 20, No. 2 pages 43-49. http://dx.doi.org/10.1145/2539269
Nam, Y., Reyes, B. A. and Cho K. H. (2016) “Estimation of Respiratory Rates Using the Built-in Microphone of a Smartphone or Headset”, IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 6, pages1493-1501. http://dx.doi.org/10.1109/JBHI.2015.2480838
Narayan, S., Shivdare, P., Niranjan, T., Williams, K., Freudman, J. and Sehra, R. (2018) “Noncontact identification of sleep-disturbed breathing from smartphone-recorded sounds validated by polysomnography”, Sleep Breath (2018).
Oletic, D. and Bilas, V. (2016) “Energy-Efficient Respiratory Sounds Sensing for Personal Mobile Asthma Monitoring”, IEEE Sensors Journal, Vol. 16, No. 23, pages 8295-8303. http://dx.doi.org/10.1109/JSEN.2016.2585039
Patwari, N., Wilson, J., Ananthanarayanan, S., Kasera, S. K. and Westenskow, D. R. (2014) “Monitoring Breathing via Signal Strength in Wireless Networks”, IEEE Transactions on Mobile Computing, Vol. 13, No. 8, pages 1774-1786. http://dx.doi.org/10.1109/TMC.2013.117
Sohn, K., Merchant, F. M., Sayadi, O., Puppala, D., Doddamani, R., Sahani, A., Singh, J. P., Heist, E. K., Isselbacher, E. M. and Armoundas, A. A. (2017) “A Novel Point-of-Care Smartphone Based System for Monitoring the Cardiac and Respiratory Systems”, Scientific Report 7, 44946.
Sun, X., Qiu, L., Wu, Y., Tang, Y. and Cao, G. (2017) “SleepMonitor: Monitoring Respiratory Rate and Body Position During Sleep Using Smartwatch”, PACM Interact. Mob. Wearable Ubiquitous Technologies, Vol. 1, No. 3, Article 104. http://dx.doi.org/10.1145/3130969
Wanneburg, J. and Malekian, R. (2015) “Body Sensor Network for Mobile Health Monitoring, a Diagnosis and anticipating System”, IEEE Sensors Journal, Vol. 15, No. 12, pages 6839-6852. http://dx.doi.org/10.1109/JSEN.2015.2464773
Publicado
11/06/2019
Como Citar
LEMOS, Diego O.; SIEBRA, Clauirton A..
Methods for Breathing Rate Measurement through Mobile Platform: a Review. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO APLICADA À SAÚDE (SBCAS), 19. , 2019, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 288-293.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2019.6264.
