Biofeedback in Healthcare: State of the Art and Meta Review

  • Hawazin Faiz Badawi
  • Abdulmotaleb El SaddikEmail author


This chapter consists of five main sections. It begins by discussing the scope of utilizing biofeedback technology in healthcare systems. Then, it presents a brief history of biofeedback technology and previous reviews. The second section highlights the sensory technology in biofeedback systems by presenting the different types of sensors and their features. The third section explores recent research of biofeedback-based healthcare systems by presenting a range of applications in different fields combined with the utilized sensors. The fourth section discusses the challenges and issues that affect the deployment of biofeedback in healthcare systems. The last section concludes this review.


Biofeedback Healthcare Systems Applications Sensors State of the art Classification Challenges 


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  1. 1.
    H. Al Osman, M. Eid, A. El Saddik, U-biofeedback: A multimedia-based reference model for ubiquitous biofeedback systems. Multimed. Tools Appl. 72(3), 3143–3168 (2014)CrossRefGoogle Scholar
  2. 2.
    Mayo Clinic, Biofeedback (Mayo Clinic, 2019), [Online] Accessed 26 Mar 2019
  3. 3.
    D.W. Shearn, Operant analysis in psychophysiology, in Handbook of Psychophysiology, ed. by N.S. Greenfield, R.A. Sternbach (Holt, Rinehart, Winston, New York, 1972)Google Scholar
  4. 4.
    K.R. Pelletier, Theory and applications of clinical biofeedback. J. Contemp. Psychother. 7(1), 29–34 (1975)CrossRefGoogle Scholar
  5. 5.
    J. Basmajian, Introduction: Principles and background. Biofeedback Princ. Pract. Clin., 1–4 (1989)Google Scholar
  6. 6.
    J. Kamiya, Operant control of the EEG alpha rhythm and some of its reported effects on consciousness, in Altered States Consciousness (Wiley, New York, 1969), pp. 489–501Google Scholar
  7. 7.
    H. Al Osman, H. Dong, A. El Saddik, Ubiquitous biofeedback serious game for stress management. IEEE Access 4, 1274–1286 (2016)CrossRefGoogle Scholar
  8. 8.
    H.F. Badawi, H. Dong, A. El Saddik, Mobile cloud-based physical activity advisory system using biofeedback sensors. Futur. Gener. Comput. Syst. 66, 59–70 (2017)CrossRefGoogle Scholar
  9. 9.
    Z. Zhang, H. Wu, W. Wang, B. Wang, A smartphone based respiratory biofeedback system, in Proceedings – 2010 3rd International Conference on Biomedical Engineering and Informatics, BMEI 2010, vol. 2 (2010), pp. 717–720Google Scholar
  10. 10.
    S. Hamdan, H. Al Osman, M. Eid, A. El Saddik, A biofeedback system for sleep management, in 2012 IEEE International Symposium on Robotic and Sensors Environments, ROSE 2012 – Proceedings (2012), pp. 133–137Google Scholar
  11. 11.
    H. Badawi, M. Eid, A. El Saddik, Diet advisory system for children using biofeedback sensor, in MeMeA 2012 – 2012 IEEE Symposium on Medical Measurements and Applications, Proceedings (2012)Google Scholar
  12. 12.
    D. Hillsman, Respiratory biofeedback and performance evaluation system, US3991304A, 1976Google Scholar
  13. 13.
    A.G. Hock, Biofeedback system for sensing body motion and flexure, US6032530A, 2000Google Scholar
  14. 14.
    J. M. J. F. F. M. Horton, Modular biofeedback training system, US4110918A, 1976Google Scholar
  15. 15.
    E.B. Blanchard, L.D. Young, Clinical applications of biofeedback training: a review of evidence. Arch. Gen. Psychiatry 30(5), 573–589 (1974)CrossRefGoogle Scholar
  16. 16.
    F. Butler, Biofeedback: A Survey of the Literature (1978)Google Scholar
  17. 17.
    T.J. Teyler, Biofeedback: a survey of the literature. Psyccritiques 24(1), 75 (1979)Google Scholar
  18. 18.
    G.D. Zimet, Locus of control and biofeedback: a review of the literature. Percept. Mot. Skills 49(3), 871–877 (1979)CrossRefGoogle Scholar
  19. 19.
    J. Johansson, L.-G. Öst, Self-control procedures in biofeedback: a review of temperature biofeedback in the treatment of migraine. Biofeedback Self. Regul. 7(4), 435–442 (1982)CrossRefGoogle Scholar
  20. 20.
    J.A. Turner, C.R. Chapman, Psychological interventions for chronic pain: a critical review. II. Operant conditioning, hypnosis, and cognitive-behavioral therapy. Pain 12(1), 23–46 (1982)CrossRefGoogle Scholar
  21. 21.
    D.L. Trudeau, The treatment of addictive disorders by brain wave biofeedback: a review and suggestions for future research. Clin. EEG Neurosci. 31(1), 13–22 (2000)Google Scholar
  22. 22.
    N.C. Moore, A review of EEG biofeedback treatment of anxiety disorders. Clin. EEG Neurosci. 31(1), 1–6 (2000)Google Scholar
  23. 23.
    M.S. Medlicott, S.R. Harris, A systematic review of the effectiveness of exercise, manual therapy, electrotherapy, relaxation training, and biofeedback in the management of temporomandibular disorder. Phys. Ther. 86(7), 955–973 (2006)Google Scholar
  24. 24.
    C. Imperatori, M. Mancini, G. Della Marca, E.M. Valenti, B. Farina, Feedback-based treatments for eating disorders and related symptoms: a systematic review of the literature. Nutrients 10(11) (2018)CrossRefGoogle Scholar
  25. 25.
    Y. Nestoriuc, A. Martin, W. Rief, F. Andrasik, Biofeedback treatment for headache disorders: a comprehensive efficacy review. Appl. Psychophysiol. Biofeedback 33(3), 125–140 (2008)CrossRefGoogle Scholar
  26. 26.
    A. Tremback-Ball, E. Gherghel, A. Hegge, K. Kindig, H. Marsico, R. Scanlon, The effectiveness of biofeedback therapy in managing Bladder Bowel Dysfunction in children: a systematic review. J. Pediatr. Rehabil. Med. 11(3), 161–173 (2018)CrossRefGoogle Scholar
  27. 27.
    D.M. Thompson, D.A. Hall, D.-M. Walker, D.J. Hoare, Psychological therapy for people with tinnitus: a scoping review of treatment components. Ear Hear. 38(2), 149–158 (2017)CrossRefGoogle Scholar
  28. 28.
    D. Bega, P. Gonzalez-Latapi, C. Zadikoff, T. Simuni, A review of the clinical evidence for complementary and alternative therapies in Parkinson’s disease. Curr. Treat. Options Neurol. 16(10) (2014)Google Scholar
  29. 29.
    G. Chiarioni, Biofeedback treatment of chronic constipation: myths and misconceptions. Tech. Coloproctol. 20(9), 611–618 (2016)CrossRefGoogle Scholar
  30. 30.
    H. Thabrew, P. Ruppeldt, J.J. Sollers, Systematic review of biofeedback interventions for addressing anxiety and depression in children and adolescents with long-term physical conditions. Appl. Psychophysiol. Biofeedback 43(3), 179–192 (2018)CrossRefGoogle Scholar
  31. 31.
    Scopus, [Online] Accessed 28 Nov 2018
  32. 32.
    A. Stubberud, M. Linde, Digital technology and mobile health in behavioral migraine therapy: a narrative review. Curr. Pain Headache Rep. 22(10) (2018)Google Scholar
  33. 33.
    K. Bodner et al., A cross-sectional review of the prevalence of integrative medicine in pediatric pain clinics across the United States. Complement. Ther. Med. 38, 79–84 (2018)CrossRefGoogle Scholar
  34. 34.
    T. Rose, C.S. Nam, K.B. Chen, Immersion of virtual reality for rehabilitation – Review. Appl. Ergon. 69, 153–161 (2018)CrossRefGoogle Scholar
  35. 35.
    G.J. Macfarlane et al., EULAR revised recommendations for the management of fibromyalgia. Ann. Rheum. Dis. 76(2), 318–328 (2017)MathSciNetCrossRefGoogle Scholar
  36. 36.
    D. Vadas, L. Kalichman, Post-stroke hip fracture in older people: a narrative review. Int. J. Ther. Rehabil. 23(2), 58–63 (2016)CrossRefGoogle Scholar
  37. 37.
    M.D. Fejka, Fecal incontinence: a review of current treatment options. J. Am. Acad. Physician Assist. 29(9), 27–30 (2016)CrossRefGoogle Scholar
  38. 38.
    J. Lake, The integrative management of PTSD: a review of conventional and CAM approaches used to prevent and treat PTSD with emphasis on military personnel. Adv. Integr. Med. 2(1), 13–23 (2015)CrossRefGoogle Scholar
  39. 39.
    C.A. Anderson, M.I. Omar, S.E. Campbell, K.F. Hunter, J.D. Cody, C.M.A. Glazener, Conservative management for postprostatectomy urinary incontinence. Cochrane Database Syst. Rev. 2015(1) (2015)Google Scholar
  40. 40.
    J.T. Olin et al., Inducible laryngeal obstruction during exercise: moving beyond vocal cords with new insights. Phys. Sportsmed. 43(1), 13–21 (2015)CrossRefGoogle Scholar
  41. 41.
    R.A. Adam, P.A. Norton, Non-surgical treatment of urinary incontinence, in Clinical Gynecology, 2nd edn. (2015), pp. 410–416Google Scholar
  42. 42.
    A. Snaith, D. Wade, Dystonia. BMJ Clin. Evid. 2014 (2014)Google Scholar
  43. 43.
    M. Santoro, T. Cronan, A systematic review of neurofeedback as a treatment for fibromyalgia syndrome symptoms. J. Musculoskelet. Pain 22(3), 286–300 (2014)CrossRefGoogle Scholar
  44. 44.
    E. Collins, F. Hibberts, M. Lyons, A.B. Williams, A.M.P. Schizas, Outcomes in non-surgical management for bowel dysfunction. Br. J. Nurs. 23(14), 776–780 (2014)CrossRefGoogle Scholar
  45. 45.
    M. Feretis, M. Chapman, The role of anorectal investigations in predicting the outcome of biofeedback in the treatment of faecal incontinence. Scand. J. Gastroenterol. 48(11), 1265–1271 (2013)CrossRefGoogle Scholar
  46. 46.
    G.K. Kristofersson, M.J. Kaas, Stress management techniques in the prison setting. J. Forensic Nurs. 9(2), 111–119 (2013)CrossRefGoogle Scholar
  47. 47.
    M. Hashefi, J.D. Katz, M.C. Reid, Nonpharmacologic, complementary, and alternative interventions for managing chronic pain in older adults. Clin. Geriatr. 21(3), 18–27 (2013)Google Scholar
  48. 48.
    R. Bize, B. Burnand, Y. Mueller, M. Rège-Walther, J.Y. Camain, J. Cornuz, Biomedical risk assessment as an aid for smoking cessation. Cochrane Database Syst. Rev. 12 (2012)Google Scholar
  49. 49.
    T. Pelton, P. van Vliet, K. Hollands, Interventions for improving coordination of reach to grasp following stroke: a systematic review. Int. J. Evid. Based. Healthc. 10(2), 89–102 (2012)CrossRefGoogle Scholar
  50. 50.
    A. El Saddik, Digital twins: the convergence of multimedia technologies. IEEE Multimed. 25(2), 87–92 (2018)CrossRefGoogle Scholar
  51. 51.
    J.S. Wilson, Sensor Technology Handbook (Elsevier, 2004)Google Scholar
  52. 52.
    G. Roberts-Grey, PHILIPS – Sleep Apnea -Sleep Better, Save More Money (PHILIPS, 2019), [Online] Accessed 28 Mar 2019
  53. 53.
    GE Healthcare Life Sciences – Sensor Chips [Online] Accessed 28 Mar 2019
  54. 54.
    GARMIN [Online] Accessed 28 Mar 2019
  55. 55.
    Fitbit [Online] Accessed 28 Mar 2019
  56. 56.
    OURA [Online] Accessed 28 Mar 2019
  57. 57.
    M. Thompson, L. Thompson, The Neurofeedback Book: An Introduction to Basic Concepts in Applied Psychophysiology (Association for Applied Psychophysiology and Biofeedback, Wheat Ridge, CO, 2003)Google Scholar
  58. 58.
    R. Merletti, Standards for reporting EMG data. J. Electromyogr. Kinesiol. 9(1), 3–4 (1999)Google Scholar
  59. 59.
    K. Iniewski, Biological and Medical Sensor Technologies (CRC Press, 2017)Google Scholar
  60. 60.
    M. Chen, S. Gonzalez, A. Vasilakos, H. Cao, V.C.M. Leung, Body area networks: a survey. Mob. Netw. Appl. 16(2), 171–193 (2011)CrossRefGoogle Scholar
  61. 61.
    T. O’Donovan, J. O’Donoghue, C. Sreenan, D. Sammon, P. O’Reilly, K.A. O’Connor, A context aware wireless Body Area Network (BAN), in 3rd International Conference on Pervasive Computing Technologies for Healthcare – Pervasive Health 2009, PCTHealth 2009 (2009)Google Scholar
  62. 62.
    G. Yang, Y. Cao, J. Chen, H. Tenhunen, L.-R. Zheng, An Active-Cable connected ECG monitoring system for ubiquitous healthcare, in Proceedings – 3rd International Conference on Convergence and Hybrid Information Technology, ICCIT 2008, vol. 1 (2008), pp. 392–397Google Scholar
  63. 63.
    V. Shnayder, B. Chen, K. Lorincz, T.R.F. Fulford-Jones, M. Welsh, Sensor Networks for Medical Care (2005)CrossRefGoogle Scholar
  64. 64.
    C. Park, P.H. Chou, Y. Bai, R. Matthews, A. Hibbs, An ultra-wearable, wireless, low power ECG monitoring system, in IEEE 2006 Biomedical Circuits and Systems Conference Healthcare Technology, BioCAS 2006 (2006), pp. 241–244Google Scholar
  65. 65.
    J. Liang, Y. Wu, Wireless ECG monitoring system based on OMAP, in Proceedings – 12th IEEE International Conference on Computational Science and Engineering, CSE 2009, vol. 2 (2009), pp. 1002–1006Google Scholar
  66. 66.
    C.J. Deepu, X.Y. Xu, X.D. Zou, L.B. Yao, Y. Lian, An ECG-on-chip for wearable cardiac monitoring devices, in Proceedings – 5th IEEE International Symposium on Electronic Design, Test and Applications, DELTA 2010 (2010), pp. 225–228Google Scholar
  67. 67.
    PHILIPS- Products and Services [Online] Accessed 28 Mar 2019
  68. 68.
    V. Galetic et al., Ericsson mobile health solution overview, in MIPRO 2010 – 33rd International Convention on Information and Communication Technology, Electronics and Microelectronics, Proceedings (2010), pp. 350–354Google Scholar
  69. 69.
  70. 70.
    GE Healthcare [Online] Accessed 28 Mar 2019
  71. 71.
    NOKIA-Healthcare [Online] Accessed 28 Mar 2019
  72. 72.
    J. Welch, F. Guilak, S.D. Baker, A wireless ECG smart sensor for broad application in life threatening event detection, in Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings, vol. 26 V (2004), pp. 3447–3449Google Scholar
  73. 73.
    R. Fensli, E. Gunnarson, O. Hejlesen, A wireless ECG system for continuous event recording and communication to a clinical alarm station, in Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings, vol. 26 III (2004), pp. 2208–2211Google Scholar
  74. 74.
    R. Fensli, E. Gunnarson, T. Gundersen, A wearable ECG-recording system for continuous arrhythmia monitoring in a wireless tele-home-care situation, in Proceedings – IEEE Symposium on Computer-Based Medical Systems (2005), pp. 407–412Google Scholar
  75. 75.
    J. Proulx, R. Clifford, S. Sorensen, D.-J. Lee, J. Archibald, Development and evaluation of a bluetooth EKG monitoring sensor, in Proceedings – IEEE Symposium on Computer-Based Medical Systems, vol. 2006 (2006), pp. 507–511Google Scholar
  76. 76.
    P. Hamilton, Open source ECG analysis. Comput. Cardiol. 29, 101–104 (2002)CrossRefGoogle Scholar
  77. 77.
    F. Chiarugi, P.J. Lees, C.E. Chronaki, M. Tsiknakis, S.C. Orphanoudakis, Developing manufacturer-independent components for ECG viewing and for data exchange with ECG devices: can the SCP-ECG standard help? Comput. Cardiol. 28, 185–188 (2001)Google Scholar
  78. 78.
    A. Searle, L. Kirkup, A direct comparison of wet, dry and insulating bioelectric recording electrodes. Physiol. Meas. 21(2), 271–283 (2000)CrossRefGoogle Scholar
  79. 79.
    T.S. Poo, K. Sundaraj, Design and development of low cost biceps tendonitis monitoring system using EMG sensor, in Proceedings – CSPA 2010: 2010 6th International Colloquium on Signal Processing and Its Applications (2010)Google Scholar
  80. 80.
    R. Merletti, H. Hermens, R. Kadefors, European community projects on surface electromyography. Annu. Int. Conf. IEEE Eng. Med. Biol. 2, 1119–1122 (2001)Google Scholar
  81. 81.
    J. Sipilä, A. Tolvanen, P. Taelman, sEMG measuring by garments (2007)Google Scholar
  82. 82.
    W. Youn, J. Kim, Development of a compact-size and wireless surface EMG measurement system, in ICCAS-SICE 2009 – ICROS-SICE International Joint Conference 2009, Proceedings (2009), pp. 1625–1628Google Scholar
  83. 83.
    Y. Makino, S. Ogawa, H. Shinoda, EMG sensor integration based on two dimensional communication, in Proceedings of INSS 2008 – 5th International Conference on Networked Sensing Systems (2008), p. 257Google Scholar
  84. 84.
    Y.-H. Liu, H.-P. Huang, Towards a high-stability EMG recognition system for prosthesis control: a one-class classification based non-target EMG pattern filtering scheme, in Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics (2009), pp. 4752–4757Google Scholar
  85. 85.
    Q. Wang, X. Zhang, X. Chen, R. Chen, W. Chen, Y. Chen, A novel pedestrian dead reckoning algorithm using wearable EMG sensors to measure walking strides, in 2010 Ubiquitous Positioning Indoor Navigation and Location Based Service, UPINLBS 2010 (2010)Google Scholar
  86. 86.
    Y. Makino, S. Ogawa, H. Shinoda, Flexible EMG sensor array for haptic interface, in Proceedings of the SICE Annual Conference (2008), pp. 1468–1473Google Scholar
  87. 87.
    B.D. Farnsworth, D.M. Talyor, R.J. Triolo, D.J. Young, Wireless in vivo EMG sensor for intelligent prosthetic control, in TRANSDUCERS 2009 – 15th International Conference on Solid-State Sensors, Actuators and Microsystems (2009), pp. 358–361Google Scholar
  88. 88.
    T. Ebrahimi, J.-M. Vesin, G. Garcia, Brain-computer interface in multimedia communication. IEEE Signal Process. Mag. 20(1), 14–24 (2003)CrossRefGoogle Scholar
  89. 89.
    E. Waterhouse, New horizons in ambulatory electroencephalography. IEEE Eng. Med. Biol. Mag. 22(3), 74–80 (2003)CrossRefGoogle Scholar
  90. 90.
    A.J. Casson, E. Rodriguez-Villegas, Data reduction techniques to facilitate wireless and long term AEEG epilepsy monitoring, in Proceedings of the 3rd International IEEE EMBS Conference on Neural Engineering (2007), pp. 298–301Google Scholar
  91. 91.
    A.J. Casson, E. Rodriguez-Villegas, Toward online data reduction for portable electroencephalography systems in epilepsy. IEEE Trans. Biomed. Eng. 56(12), 2816–2825 (2009)CrossRefGoogle Scholar
  92. 92.
    A.J. Casson, L. Logesparan, E. Rodriguez-Villegas, An introduction to future truly wearable medical devices-from application to ASIC, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10 (2010), pp. 3430–3431Google Scholar
  93. 93.
    U. Barcaro et al., A decision support system for the acquisition and elaboration of EEG signals: the AmI-GRID environment, in Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings (2007), pp. 4331–4334Google Scholar
  94. 94.
    S. Saini, S. Kaur, K. Das, V. Saini, Using the first drop of blood for monitoring blood glucose values in critically ill patients: an observational study. Indian J. Crit. Care Med. 20(11), 658–661 (2016)CrossRefGoogle Scholar
  95. 95.
    A. Trabelsi, M. Boukadoum, C. Fayomi, E. M. Aboulhamid, Blood glucose sensor implant using NIR spectroscopy: preliminary design study, in Proceedings of the International Conference on Microelectronics, ICM (2010), pp. 176–179Google Scholar
  96. 96.
    A. Tura, A. Maran, G. Pacini, Non-invasive glucose monitoring: assessment of technologies and devices according to quantitative criteria. Diabetes Res. Clin. Pract. 77(1), 16–40 (2007)CrossRefGoogle Scholar
  97. 97.
    T. Karacolak, A.Z. Hood, E. Topsakal, Design of a dual-band implantable antenna and development of skin mimicking gels for continuous glucose monitoring. IEEE Trans. Microw. Theory Tech. 56(4), 1001–1008 (2008)CrossRefGoogle Scholar
  98. 98.
    B. Freer, J. Venkataraman, Feasibility study for non-invasive blood glucose monitoring, in 2010 IEEE International Symposium on Antennas and Propagation and CNC-USNC/URSI Radio Science Meeting – Leading the Wave, AP-S/URSI 2010 (2010)Google Scholar
  99. 99.
    B.R. Jean, E.C. Green, M.J. McClung, A microwave frequency sensor for non-invasive blood-glucose measurement, in 2008 IEEE Sensors Applications Symposium, SAS-2008 – Proceedings (2008), pp. 4–7Google Scholar
  100. 100.
    T. Yilmaz, Y. Hao, Electrical property characterization of blood glucose for on-body sensors, in Proceedings of the 5th European Conference on Antennas and Propagation, EUCAP 2011 (2011), pp. 3659–3662Google Scholar
  101. 101.
    M. Stemmann, F. Ståhl, J. Lallemand, E. Renard, R. Johansson, Sensor calibration models for a non-invasive blood glucose measurement sensor, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10 (2010), pp. 4979–4982Google Scholar
  102. 102.
    H.M. Saraoğlu, M. Koçan, Determination of blood glucose level-based breath analysis by a quartz crystal microbalance sensor array. IEEE Sens. J. 10(1), 104–109 (2010)CrossRefGoogle Scholar
  103. 103.
    F. Ravariu, C. Ravariu, O. Nedelcu, The modeling of a sensor for the human blood pressure. Proc. Int. Semiconductor Conf., CAS 1, 67–70 (2002)CrossRefGoogle Scholar
  104. 104.
    R. Melamud et al., Development of an SU-8 Fabry-Perot blood pressure sensor, in Proceedings of the IEEE International Conference on Micro Electro Mechanical Systems (MEMS) (2005), pp. 810–813Google Scholar
  105. 105.
    P.A. Shaltis, A. Reisner, H.H. Asada, Wearable, cuff-less PPG-based blood pressure monitor with novel height sensor, in Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings (2006), pp. 908–911Google Scholar
  106. 106.
    K.-C. Park, H. Kang, Y. Huh, K.C. Kim, Cuffless and noninvasive measurement of systolic blood pressure, diastolic blood pressure, mean arterial pressure and pulse pressure using radial artery tonometry pressure sensor with concept of Korean traditional medicine, in 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2007), pp. 3597–3600Google Scholar
  107. 107.
    J. Muehlsteff, X.A. Aubert, G. Morren, Continuous cuff-less blood pressure monitoring based on the pulse arrival time approach: the impact of posture, in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS’08 – “Personalized Healthcare through Technology” (2008), pp. 1691–1694Google Scholar
  108. 108.
    L. Lading, F. Nyboe, D. Nilsson, H. Pranov, T.W. Hansen, Sensor for vascular compliance and blood pressure, in Proceedings of IEEE Sensors (2009), pp. 181–184Google Scholar
  109. 109.
    H. Fassbender et al., Fully implantable blood pressure sensor for hypertonic patients, in Proceedings of IEEE Sensors (2008), pp. 1226–1229Google Scholar
  110. 110.
    J. Fiala et al., Implantable sensor for blood pressure determination via pulse transit time, in Proceedings of IEEE Sensors (2010), pp. 1226–1229Google Scholar
  111. 111.
    K.-U. Kirstein, J. Sedivy, T. Salo, C. Hagleitner, T. Vancura, A. Hierlemann, A CMOS-based tactile sensor for continuous blood pressure monitoring, in Proceedings – Design, Automation and Test in Europe, DATE ’05, vol. 2005 (2005), pp. 210–214Google Scholar
  112. 112.
    E. Kaniusas et al., Method for continuous nondisturbing monitoring of blood pressure by magnetoelastic skin curvature sensor and ECG. IEEE Sens. J. 6(3), 819–828 (2006)CrossRefGoogle Scholar
  113. 113.
    J.P. Buschmann, J. Huang, New ear sensor for mobile, continuous and long term pulse oximetry, in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC’10 (2010), pp. 5780–5783Google Scholar
  114. 114.
    S. Reichelt et al., Development of an implantable pulse oximeter. IEEE Trans. Biomed. Eng. 55(2), 581–588 (2008)CrossRefGoogle Scholar
  115. 115.
    N. Watthanawisuth, T. Lomas, A. Wisitsoraat, A. Tuantranont, Wireless wearable pulse oximeter for health monitoring using ZigBee wireless sensor network, in ECTI-CON 2010 – The 2010 ECTI International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (2010), pp. 575–579Google Scholar
  116. 116.
    S.-J. Jung, Y.-D. Lee, Y.-S. Seo, W.-Y. Chung, Design of a low-power consumption wearable reflectance pulse oximetry for ubiquitous healthcare system, in 2008 International Conference on Control, Automation and Systems, ICCAS 2008 (2008), pp. 526–528Google Scholar
  117. 117.
    Y. Chuo, B. Omrane, C. Landrock, J.N. Patel, B. Kaminska, Platform for all-polymer-based pulse-oximetry sensor, in Proceedings of IEEE Sensors (2010), pp. 155–159Google Scholar
  118. 118.
    J. Solà et al., SpO2 sensor embedded in a finger ring: design and implementation, in Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings (2006), pp. 4295–4298Google Scholar
  119. 119.
    S.B. Duun, R.G. Haahr, K. Birkelund, E.V. Thomsen, A ring-shaped photodiode designed for use in a reflectance pulse oximetry sensor in wireless health monitoring applications. IEEE Sens. J. 10(2), 261–268 (2010)CrossRefGoogle Scholar
  120. 120.
    C. Schreiner, P. Catherwood, J. Anderson, J. McLaughlin, Blood oxygen level measurement with a chest-based Pulse Oximetry prototype System. Comput. Cardiol. 37, 537–540 (2010)Google Scholar
  121. 121.
    J. Solà, O. Chételat, J. Krauss, On the reliability of pulse oximetry at the sternum, in Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings (2007), p. 1537Google Scholar
  122. 122.
    J.P. Phillips, R.M Langford, S.H Chang, K. Maney, P.A Kyriacou, D.P Jones, Evaluation of a fiber-optic esophageal pulse oximeter, in Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009 (2009), pp. 1509–1512Google Scholar
  123. 123.
    H. Bezuidenhout, D. Woods, J. Wyatt, J. Lawn, Are you blue yet? Developing low cost, alternative powered pulse oximetry for ill babies and children. IET Seminar Digest 2006(11370), 83–87 (2006)Google Scholar
  124. 124.
    P. Venkata Reddy, A. Kumar, S.M.K. Rahman, T.S. Mundra, A new method for fingerprint antispoofing using pulse oxiometry, in IEEE Conference on Biometrics: Theory, Applications and Systems, BTAS’07 (2007)Google Scholar
  125. 125.
    L.T. Foster, C.P. Keller, B. McKee, A. Ostry, British Columbia Atlas of Wellness, 2nd edn. (Western Geographical Press, 2011)Google Scholar
  126. 126.
    M.J. James, J.F. Fee, R.M. Horton, Modular biofeedback training system, US Patent 4,110,918, 1978Google Scholar
  127. 127.
    T.W. Glynn, M.J. James, Biofeedback training method and system, US Patent 3,942,516, 1976Google Scholar
  128. 128.
    D. Hillsman, Metered dose inhaler biofeedback training and evaluation system, US4984158A, 1991Google Scholar
  129. 129.
    D. Kilis, C.J. Matson, Inhalation device training system, US5167506A, 1992Google Scholar
  130. 130.
    T.L. Zucker, K.W. Samuelson, F. Muench, M.A. Greenberg, R.N. Gevirtz, The effects of respiratory sinus arrhythmia biofeedback on heart rate variability and posttraumatic stress disorder symptoms: A pilot study. Appl. Psychophysiol. Biofeedback 34(2), 135–143 (2009)CrossRefGoogle Scholar
  131. 131.
    M.B. Sterman, L.R. Macdonald, R.K. Stone, Biofeedback training of the sensorimotor electroencephalogram rhythm in man: effects on epilepsy. Epilepsia 15(3), 395–416 (1974)CrossRefGoogle Scholar
  132. 132.
    R. Markiewicz, The use of EEG biofeedback/neurofeedback in psychiatric rehabilitation. Psychiatr. Pol. 51(6), 1095–1106 (2017)CrossRefGoogle Scholar
  133. 133.
    S.R. Criswell, R. Sherman, S. Krippner, Cognitive behavioral therapy with heart rate variability biofeedback for adults with persistent noncombat-related posttraumatic stress disorder. Perm. J. 22 (2018)Google Scholar
  134. 134.
    B.G. Travers et al., Biofeedback-based, videogame balance training in autism. J. Autism Dev. Disord. 48(1), 163–175 (2018)CrossRefGoogle Scholar
  135. 135.
    Y.-Z. Lai, C.-H. Tai, Y.-S. Chang, K.-H. Chung, A mobile cloud-based biofeedback platform for evaluating medication response, in Proceedings – 2017 IEEE 7th International Symposium on Cloud and Service Computing, SC2 2017, vol. 2018 (2018), pp. 183–188Google Scholar
  136. 136.
    C.P. Wooldridge, G. Russell, Head position training with the cerebral palsied child: an application of biofeedback techniques. Arch. Phys. Med. Rehabil. 57(9), 407–414 (1976)Google Scholar
  137. 137.
    H. Hasegawa, T. Tanaka, T. Wakaiki, K. Shimatani, Y. Kurita, Biofeedback for Training Pelvic Floor Muscles with EMG Signals of Synergistic Muscles, vol. 789 (2019)Google Scholar
  138. 138.
    L. Rousset, B. Halioua, Stress and psoriasis. Int. J. Dermatol. 57(10), 1165–1172 (2018)CrossRefGoogle Scholar
  139. 139.
    P.-W. Meyer, H.-C. Friederich, A. Zastrow, Breathe to ease – respiratory biofeedback to improve heart rate variability and coping with stress in obese patients: a pilot study. Ment. Heal. Prev. 11, 41–46 (2018)CrossRefGoogle Scholar
  140. 140.
    H. Badawi, F. Laamarti, F. Arafsha, A. El Saddik, Standardizing a shoe insole based on ISO/IEEE 11073 Personal Health Device (X73-PHD) standards. Adv. Intell. Syst. Comput. 918, 764–778 (2019)Google Scholar
  141. 141.
    T. Zhang, J. Lu, F. Hu, Q. Hao, Bluetooth low energy for wearable sensor-based healthcare systems, in 2014 IEEE Healthcare Innovation Conference, HIC 2014 (2014), pp. 251–254Google Scholar
  142. 142.
    B.M. Dugan, S.M. Santisi, J.P. Latrille, Systems and methods for providing authenticated biofeedback information to a mobile device and for using such information, US20090270743A1, 2009Google Scholar
  143. 143.
    H.S. Merki, R.-R.C. Dries, Apparatus for the biofeedback control of body functions, US5002055A, 1991Google Scholar
  144. 144.
    L.M. Nashner, D.F. Goldstein, Apparatus and method for assessment and biofeedback training of body coordination skills critical and ball-strike power and accuracy during athletic activities, US5697791A, 1997Google Scholar
  145. 145.
    F. Lamaarti, F. Arafsha, B. Hafidh, A. El Saddik, Automated Athlete Haptic Training System for Soccer Sprinting, in Proceedings - 2nd International Conference on Multimedia Information Processing and Retrieval, MIPR 2019, 2019, pp. 303–309CrossRefGoogle Scholar
  146. 146.
    M. Owlia, C. Ng, K. Ledda, M. Kamachi, A. Longfield, T. Dutta, Preventing back injury in caregivers using real-time posture-based feedback. Adv. Intell. Syst. Comput. 820, 750–758 (2019)Google Scholar
  147. 147.
    B.J. Munro, T.E. Campbell, G.G. Wallace, J.R. Steele, The intelligent knee sleeve: a wearable biofeedback device. Sensors Actuators, B Chem. 131(2), 541–547 (2008)CrossRefGoogle Scholar
  148. 148.
    A. Khalil, S. Glal, StepUp: a step counter mobile application to promote healthy lifestyle, in Proceedings of the 2009 International Conference on the Current Trends in Information Technology, CTIT 2009 (2009), pp. 208–212Google Scholar
  149. 149.
    I.M. Albaina, T. Visser, C.A. Van Der Mast, M.H. Vastenburg, Flowie: a persuasive virtual coach to motivate elderly individuals to walk, in 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare – Pervasive Health 2009, PCTHealth 2009 (2009)Google Scholar
  150. 150.
    M.D. Rodríguez, J.R. Roa, A.L. Morán, S. Nava-Muñoz, CAMMInA: a mobile ambient information system to motivate elders to exercise. Pers. Ubiquitous Comput. 17(6), 1127–1134 (2013)CrossRefGoogle Scholar
  151. 151.
    Y. Lin, J. Jessurun, B. De Vries, H. Timmermans, in Motivate: Context Aware Mobile Application for Activity Recommendation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 7040 (2011), pp. 210–214Google Scholar
  152. 152.
    E. Buckeridge, M.C. LeVangie, B. Stetter, S.R. Nigg, B.M. Nigg, An on-ice measurement approach to analyse the biomechanics of ice hockey skating. PLoS One 10(5), e0127324 (2015)CrossRefGoogle Scholar
  153. 153.
    A. Isaev, Y. Romanov, V. Erlikh, Integrative activity of the kickboxer’s body within modern sport training using biofeedback. Gazz. Medica Ital. Arch. Per Le Sci. Mediche 177(3), 43–55 (2018)Google Scholar
  154. 154.
    S.M.N. Arosha Senanayake, A.G. Naim, Smart sensing and biofeedback for vertical jump in sports. Smart Sensors Measur. Instrum. 29, 63–81 (2019)CrossRefGoogle Scholar
  155. 155.
    A. Riposan-Taylor, I.J. Taylor, Personal connected devices for healthcare, in The Internet of Things for Smart Urban Ecosystems (2019), pp. 333–361Google Scholar
  156. 156.
    R. Elsaadi, M. Shafik, Deployment of assisted living technology using intelligent body sensors platform for elderly people health monitoring. Adv. Transdiscip. Eng. 3, 219–224 (2016)Google Scholar
  157. 157.
    H. Badawi, A. El Saddik, Towards a context-aware biofeedback activity recommendation mobile application for healthy lifestyle, in Procedia Computer Science, vol. 21 (2013)CrossRefGoogle Scholar
  158. 158.
    A. Rinaldi, C. Becchimanzi, F. Tosi, Wearable Devices and Smart Garments for Stress Management, vol. 824 (2019)Google Scholar
  159. 159.
    G. Kolev, N. Smykova, M. Selivanova, Wi-FIT project: self-learning medical expert system for lifestyle enhancement, in IET Conference on Assisted Living 2009 (2009)Google Scholar
  160. 160.
    A. Danesh, F. Laamarti, A. El Saddik, in HAVAS: The Haptic Audio Visual Sleep Alarm System. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9194 (2015), pp. 247–256Google Scholar
  161. 161.
    R.-X. Yu et al., Spectroscopic biofeedback on cutaneous carotenoids as part of a prevention program could be effective to raise health awareness in adolescents. J. Biophotonics 7(11–12), 926–937 (2014)CrossRefGoogle Scholar
  162. 162.
    G.B. Whatmore, D.R. Kohli, The Physiopathology and Treatment of Functional Disorders; Including Anxiety States and Depression and the Role of Biofeedback Training (1974)Google Scholar
  163. 163.
    A. Albraikan, B. Hafidh, A. El Saddik, IAware: a real-time emotional biofeedback system based on physiological signals. IEEE Access 6, 78780–78789 (2018)CrossRefGoogle Scholar
  164. 164.
    N. Peira, M. Fredrikson, G. Pourtois, Controlling the emotional heart: heart rate biofeedback improves cardiac control during emotional reactions. Int. J. Psychophysiol. 91(3), 225–231 (2014)CrossRefGoogle Scholar
  165. 165.
    Y. Ma, B. Xu, Y. Bai, G. Sun, R. Zhu, Daily mood assessment based on mobile phone sensing, in Proceedings – BSN 2012: 9th International Workshop on Wearable and Implantable Body Sensor Networks (2012), pp. 142–147Google Scholar
  166. 166.
    R. Al Rihawi, B. Ahmed, R. Gutierrez-Osuna, Dodging stress with a personalized biofeedback game, in CHI PLAY 2014 – Proceedings of the 2014 Annual Symposium on Computer-Human Interaction in Play (2014), pp. 399–400Google Scholar
  167. 167.
    O. Hilborn, H. Cederholm, J. Eriksson, C. Lindley, in A Biofeedback Game for Training Arousal Regulation During a Stressful Task: The Space Investor. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 8008, no. PART 5 (2013), pp. 403–410Google Scholar
  168. 168.
    P. Jerčić et al., A serious game using physiological interfaces for emotion regulation training in the context of financial decision-making, in ECIS 2012 – Proceedings of the 20th European Conference on Information Systems (2012)Google Scholar
  169. 169.
    S. Orguc, H.S. Khurana, K.M. Stankovic, H.S. Leel, A.P. Chandrakasan, EMG-based real time facial gesture recognition for stress monitoring, in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, vol. 2018 (2018), pp. 2651–2654Google Scholar
  170. 170.
    U. Chauhan, N. Reithinger, J.R. MacKey, Real-time stress assessment through PPG sensor for VR biofeedback, in Proceedings of the 20th International Conference on Multimodal Interaction, ICMI 2018 (2018)Google Scholar
  171. 171.
    B. Choi, Breathing information extraction algorithm from PPG signal for the development of respiratory biofeedback app. Trans. Korean Inst. Electr. Eng. 67(6), 794–798 (2018)Google Scholar
  172. 172.
    K. Horgan, S. Howard, F. Gardiner-Hyland, Pre-service teachers and stress during microteaching: an experimental investigation of the effectiveness of relaxation training with biofeedback on psychological and physiological indices of stress. Appl. Psychophysiol. Biofeedback 43(3), 217–225 (2018)CrossRefGoogle Scholar
  173. 173.
    R. Alharthi, R. Alharthi, B. Guthier, A. El Saddik, CASP: context-aware stress prediction system. Multimed. Tools Appl. 78, 9011–9031 (2017)CrossRefGoogle Scholar
  174. 174.
    G.E. Schwartz, Biofeedback as therapy. Some theoretical and practical issues. Am. Psychol. 28(8), 666–673 (1973)CrossRefGoogle Scholar
  175. 175.
    N.E. Miller, B.R. Dworkin, Critical issues in therapeutic applications of biofeedback. Biofeedback Theory Res., 129–161 (1977)Google Scholar
  176. 176.
    R.J. Quy, S.J. Hutt, S. Forrest, Sensorimotor rhythm feedback training and epilepsy: some methodological and conceptual issues. Biol. Psychol. 9(2), 129–149 (1979)CrossRefGoogle Scholar
  177. 177.
    F. Andrasik, E.B. Blanchard, J.G. Arena, N.L. Saunders, K.D. Barron, Psychophysiology of recurrent headache: methodological issues and new empirical findings. Behav. Ther. 13(4), 407–429 (1982)CrossRefGoogle Scholar
  178. 178.
    H.E. Adams, P.J. Brantley, J.K. Thompson, Biofeedback and headache: methodological issues. Clin. Biofeedback Effic. Mech. 358–367 (1982)Google Scholar
  179. 179.
    A.H. Black, J. Brener, L.V. DiCara, P.A. Obrist, Cardiovascular Psychophysiology: Current Issues in Response Mechanisms, Biofeedback and Methodology (Routledge, 2017)Google Scholar
  180. 180.
    E. Swinnen, Future challenges in functional gait training for children and young adults with cerebral palsy. Dev. Med. Child Neurol. 60(9), 852 (2018)CrossRefGoogle Scholar
  181. 181.
    G.O.D. Amorim, P.M.M. Balata, L.G. Vieira, T. Moura, H.J.D. Silva, Biofeedback in dysphonia – progress and challenges. Braz. J. Otorhinolaryngol. 84(2), 240–248 (2018)CrossRefGoogle Scholar
  182. 182.
    V. Attanasio, F. Andrasik, E.J. Burke, Clinical issues in utilizing biofeedback with children. Clin. Biofeedback Heal. 8(2), 134–141 (1985)Google Scholar
  183. 183.
    S. Ancoli, J. Kamiya, Methodological issues in alpha biofeedback training. Biofeedback Self. Regul. 3(2), 159–183 (1978)CrossRefGoogle Scholar
  184. 184.
    C.J. Fisher, C.S. Moravec, L. Khorshid, The ‘how and why’ of group biofeedback for chronic disease management. Appl. Psychophysiol. Biofeedback 43(4), 333–340 (2018)CrossRefGoogle Scholar
  185. 185.
    R. Argent, P. Slevin, A. Bevilacqua, M. Neligan, A. Daly, B. Caulfield, Clinician perceptions of a prototype wearable exercise biofeedback system for orthopaedic rehabilitation: a qualitative exploration. BMJ Open 8(10) (2018)CrossRefGoogle Scholar
  186. 186.
    A. Kos, V. Milutinović, A. Umek, Challenges in wireless communication for connected sensors and wearable devices used in sport biofeedback applications. Futur. Gener. Comput. Syst. 92, 582–592 (2019)CrossRefGoogle Scholar
  187. 187.
    A. Umek, A. Kos, The role of high performance computing and communication for real-time biofeedback in sport. Math. Probl. Eng. 2016 (2016)Google Scholar
  188. 188.
    A. Kos, A. Umek, S. Tomazic, Biofeedback in sport: challenges in real-time motion tracking and processing, in 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering, BIBE 2015 (2015)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Multimedia Communications Research Laboratory (MCRLab)University of OttawaOttawaCanada

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