Abo Bibliothek: Guest
Digitales Portal Digitale Bibliothek eBooks Zeitschriften Referenzen und Berichte Forschungssammlungen
Critical Reviews™ in Biomedical Engineering
SJR: 0.207 SNIP: 0.376 CiteScore™: 0.79

ISSN Druckformat: 0278-940X
ISSN Online: 1943-619X

Volumen 47, 2019 Volumen 46, 2018 Volumen 45, 2017 Volumen 44, 2016 Volumen 43, 2015 Volumen 42, 2014 Volumen 41, 2013 Volumen 40, 2012 Volumen 39, 2011 Volumen 38, 2010 Volumen 37, 2009 Volumen 36, 2008 Volumen 35, 2007 Volumen 34, 2006 Volumen 33, 2005 Volumen 32, 2004 Volumen 31, 2003 Volumen 30, 2002 Volumen 29, 2001 Volumen 28, 2000 Volumen 27, 1999 Volumen 26, 1998 Volumen 25, 1997 Volumen 24, 1996 Volumen 23, 1995

Critical Reviews™ in Biomedical Engineering

DOI: 10.1615/CritRevBiomedEng.2019026605
pages 131-139

Toward the Development of a Wearable Optical Respiratory Sensor for Real-Time Use

Alejo Chavez-Gaxiola
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287
Zachary Fisher
School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287
Jeffrey T. La Belle
School of Biological and Health Systems Engineering, Arizona State University, Tempe, Arizona


Respiration rate is an important vital sign that can provide insight into a patient's status and health progression. This information is used from critical care to sports and human performance evaluation. The current state of the art has demonstrated effectiveness in monitoring respiration rate with the use of wearable sensors. However, their form factor, which refers to the embodiment of approach, size, and shape, makes it difficult to implement within a longterm monitoring setting. Problems relating to form factor, such as compliance, are a major issue in collecting useful and actionable data, because they directly impact comfort and ease of wear. We present a new approach based on an optical computer mouse sensor that can be rendered into a slim, wearable device without the need for a harness or shirt to hold the sensor in place. Its main objective is to achieve similar or better readings than those of the state of the art while reducing the overall size and thus, improve compliance by making it easier, more comfortable to wear. The principle of operation of the sensor allows for enhanced signal and computational noise reduction for movement artifacts. The sensor was tested to determine its limits of detection and was calibrated to expected distance of movement. Then, observations were made under normal breathing conditions, apnea, deep breathing, and hyperventilation covering a spectrum of 0 to 45 breathings per minute (BPM). The performance of the device was described by using the mean average error which was 0.37 and 0.83 under deep breathing and hyperventilation, respectively. Testing revealed that the device produces the best results when worn over the diaphragm and that its readings are comparable to the industry gold standard. The future version we are developing incorporates a slimmer, lighter design, Bluetooth data communication to remove leads and wires, adhesive electrodes and a reusable adhesive that is also waterproof.


  1. AHRQ: The conditions that cause the most readmissions [Internet]. Advisory Board. 2018 [cited 24 May 2018]. Available from: https://www.advisory.com/daily-briefing/ 2014/04/22/most-common-readmissions.

  2. Al-Khalidi FQ, Saatchi R, Burke D, Elphick H, Tan S. , Respiration rate monitoring methods: A review. Ped Pulmonol. 2011 Jun 31;46(6):523–9.

  3. Kundu S, Kumagai S, Sasaki M. , A wearable capacitive sensor for monitoring human respiratory rate. Japan J Appl Phys. 2013;52(4S):04CL05.

  4. Werthammer J, Krasner J, DiBenedetto J, Stark AR. , Apnea monitoring by acoustic detection of airflow. Pediatrics. 1983;71(1):53–5.

  5. Corbishley P, Rodriguez-Villegas E. , Breathing detection: Towards a miniaturized, wearable, battery-operated monitoring system. IEEE Trans Biomed Eng. 2008;55(1):196–204.

  6. Folke M, Granstedt F, Hök B, Scheer H. , Comparative provocation test of respiratory monitoring methods. J Clin Monitor Comp. 2002;17(2):97–103.

  7. Nepal K, Biegeleisen E, Taikang N., Apnea detection and respiration rate estimation through parametric modelling. Proceedings of the IEEE 28th Annual Northeast Bioengineering Conference (IEEE Cat No02CH37342); 2002 Apr 21–22; Philadelphia, PA, USA. USA: IEEE, 2002. p. 277–8.

  8. Lanata A, Scilingo E, Nardini E, Loriga G, Paradiso R, De-Rossi D. , Comparative evaluation of susceptibility to motion artifact in different wearable systems for monitoring respiratory rate. IEEE Trans Inf Technol Biomed. 2010;14(2):378–86.

  9. Reinvuo T, Hannula M, Sorvoja H, Alasaarela E, Myllyla R., Measurement of respiratory rate with high-resolution accelerometer and emfit pressure sensor. Proc of the 2006 IEEE Sensors Applications Symposium; 2006 Feb 7-9; Houston, Texas, USA. USA: IEEE, 2006. p. 192–5.

  10. Paradiso R, Loriga G, Taccini N. , A wearable health care system based on knitted integrated sensors. IEEE Trans Inf Technol Biomed. 2005;9(3):337–44.

  11. Rovira-Borràs C, Coyle S, Diamont D, Stroiescu F, Daly K, Corcoran B., Integration of textile-based sensors and Shimmer for breathing rate and volume measurement. Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare; 2011 May 23-26; Dublin, Ireland. IEEE, 2011. pp. 238–41.

  12. Brady S, Dunne L, Tynan R, Diamond D, Smyth B, O’Hare G, Garment-Based Monitoring of Respiration Rate Using a Foam Pressure Sensor. Ninth IEEE International Symposium on Wearable Computers (ISWC ’05); 2005 Oct 18-21; Osaka, Japan. IEEE, 2005. p. 214–5.

  13. Nishigaki Y, Mizuguchi H, Takeda E, Koike T, Ando T, Kawamura K, Shimbo T, Ishikawa H, Fujimoto M, Saotome I, Odo R, Omoda K, Yamashita S, Yamada T, Omi T, Matsushita Y, Takeda M, Sekiguchi S, Tanaka S, Fujie M, Inokuchi H, Fujitani J., Development of new measurement system of thoracic excursion with biofeedback: Reliability and validity. J Neuroeng Rehab. 2013;10(1):45.

  14. Borges L, Barroca N, Velez F, Lebres A., Smart-clothing wireless flex sensor belt network for foetal health monitoring. Proceedings of the 3d International ICST Conference on Pervasive Computing Technologies for Healthcare; 2009 Apr 1–3; London, UK. IEEE, 2009. pp. 1–4.

  15. Zhang X, Ding Q. , Respiratory rate monitoring from the photoplethysmogram via sparse signal reconstruction. Physiol Meas. 2016;37(7):1105–19.

  16. Wertheim D, Olden C, Savage E, Seddon P. , Extracting respiratory data from pulse oximeter plethysmogram traces in newborn infants. Arch Dis Childhood Fetal Neonat Ed. 2008;94(4):F301–3.

  17. Leonard P, Beatie TF, Addison PS, Watson JN. , Standard pulse oximeters can be used to monitor respiratory rate. Emerg Med J. 2003;20(6):524–25.

  18. Moody G, Mark R, Bump M, Weinstein J, Berman A, Mietus J, Goldberger A. , Clinical validation of ECG‐ derived respiration (EDR) technique. Comp Cardiol. 1986;13:507–10.

  19. Aoki H, Takemura Y, Mimura K, Nakajima M., Development of non-restrictive sensing system for sleeping person using fiber grating vision sensor. MHS2001 Proceedings of 2001 International Symposium on Micromechatronics and Human Science (Cat No01TH8583);2001Sept 9-12;Nagoya,Japan. IEEE, 2002p.155-60.

  20. Nakajima K, Matsumoto Y, Tamura T. , Development of real-time image sequence analysis for evaluating posture change and respiratory rate of a subject in bed. Physiol Meas. 2001;22(3):N21–8.

  21. Zhu Z, Fei J, Pavlidis I. , Tracking Human Breath in Infrared Imaging. Fifth IEEE Symposium on Bioinformatics and Bioengineering (BIBE’05); 2005 Oct 19–21; Minneapolis, MN, USA. USA: IEEE, 2005. p. 227–31.

  22. ADNS-5020 Optical Mouse Sensor Data Sheet [Internet]. Avago Technologies; [cited 2018 May 24]. Available from: http://forums.ni.com/attachments/ni/170/202305/1/ADNS.

  23. Kesner S, Howe R. , Design Principles for Rapid Prototyping Forces Sensors Using 3-D Printing. IEEE/ASME Transactions on Mechatronics. 2011;16(5):866–70.

  24. Kapel F. , Convert Optical Mouse into Arduino Web Camera [Internet]. Frenki.net. 2017 [cited 26 July 2017]. Available from: http://frenki.net/2013/12/convert-optical- mouse-into-arduino-web-camera/.

  25. Vehkaoja A, Peltokangas M, Lekkala J. , Extracting the respiration cycle lengths from ECG signal recorded with bed sheet electrodes. J Phys Conf Series. 2013; 459: 012015.

  26. Tillaart R. , RobTillaart/Arduino [Internet]. Arduino / libraries / RunningAverage /. GitHub; 2018 [cited 2018 May 24]. Available from: https://github.com/RobTillaart/ Arduino/tree/master/libraries/RunningAverage.

  27. Spirometry for Health Care Providers [Internet]. Global Initiative for Chronic Obstructive Lung Disease; 2010 [cited 21 May 2018]. Available from: http://goldcopd.org/wp-content/ uploads/2016/04/GOLD_Spirometry_2010.pdf.

  28. Why is my Spirometry Volume Channel drifting and how can this be minimized? [Internet]. ADInstruments. 2018 [cited 21 May 2018]. https://www. adinstruments.com/support/knowledge-base/why-myspirometry- volume-channel-drifting-and-how-can-beminimized.