The Development of Cyber-Physical System in Health Care Industry

Part of the Studies in Computational Intelligence book series (SCI, volume 540)


A cyber physical system involves the combination of sensors, actuators, and computation modules to solve issues that lie across the physical and computational areas. This emerging technology will lead to a significant improvement in the health care industry and also will enhance the quality of life of our communities, including older and disabled persons. Our objective in this chapter is to give insights from the current research to provide future perspectives for scientific research and development. We reviewed the current research and inventions in the field of cyber physical systems (CPS) focusing on the health care industry where computational intelligence is used for decision support. In this chapter, we discuss the current state of the art and trends in cyber physical system in health care industry and summarize the issues that need to be overcome. We conclude by identifying the future challenges in this technology that needs to be addressed in order to identify and facilitate priority research in this emerging field.


Cyber physical system Telehealthcare Wireless sensor networks 


  1. 1.
    Y. Lun, L. Cheng, The research on the model of the context-aware for reliable sensing and explanation in cyber-physical system. Procedia Eng. 15, 1753–1757 (2011)CrossRefGoogle Scholar
  2. 2.
    K. Wan, K. Man, D. Hughes, Specification, analyzing challenges and approaches for cyber-physical systems (cps). Eng. Lett. 18(3), 308 (2010)Google Scholar
  3. 3.
    K.S. Nikita, J.C. Lin, D.I. Fotiandis, M.T.A. Waldmeyer, Editorial: special issue on mobile and wireless technology for healthcare delivery. IEEE Trans. Biomed. Eng. 59(11), 3083–3089 (2012)CrossRefGoogle Scholar
  4. 4.
    W. Swan, Australia to 2050: future challenges. (Commonwealth of Australia, Sydney, 2010)Google Scholar
  5. 5.
    S. Krishna, S.A. Boren, E.A. Balas, Healthcare via cell phones: a systematic review. Telemed. e-Health 15(3), 231–240 (2009)CrossRefGoogle Scholar
  6. 6.
    E. Kyriacou, M. Pattichis, C. Pattichis, A. Panayides, A. Pitsillides, M-health e-emergency systems: current status and future directions [wireless corner]. Antennas Propag. Mag. IEEE 49(1), 216–231 (2007)CrossRefGoogle Scholar
  7. 7.
    H. Alemdar, C. Ersoy, Wireless sensor networks for healthcare: a survey. Comput. Netw. 54(15), 2688–2710 (2010)CrossRefGoogle Scholar
  8. 8.
    E.E. Egbogah, A.O. Fapojuwo, A survey of system architecture requirements for health care-based wireless sensor networks. Sensors 11(5), 4875–4898 (2011)CrossRefGoogle Scholar
  9. 9.
    S. Ullah, P. Khan, N. Ullah, S. Saleem, H. Higgins, K.S. Kwak, A review of wireless body area networks for medical applications. Int. J. Commun. Netw. Syst. Sci. 2, 797–803 (2009)Google Scholar
  10. 10.
    A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic, Alarm-net: wireless sensor networks for assisted-living and residential monitoring. Technical Report, Department of Computer Science, University of Virginia, 2006Google Scholar
  11. 11.
    G. Shobha, R.R. Chittal, K. Kumar, Medical applications of wireless networks, in Proceedings of the 2nd International Conference on Systems and Networks Communications, 2007, pp. 82–82Google Scholar
  12. 12.
    E. Jovanov, Wireless technology and system integration in body area networks for m-health applications, in Proceedings of the 27th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 7158–7160Google Scholar
  13. 13.
    H. Huo, Y. Xu, H. Yan, S. Mubeen, H. Zhang, An elderly health care system using wireless sensor networks at home, in Proceedings of the 3rd International Conference on Sensor Technologies and Applications, 2009, pp. 158–163Google Scholar
  14. 14.
    V. Hessels, G.S. Le Prell, W.C. Mann, Advances in personal emergency response and detection systems. Assistive Technol. 23(3), 152–161 (2011)CrossRefGoogle Scholar
  15. 15.
    W.C. Mann, P. Belchior, M.R. Tomita, B.J. Kemp, Use of personal emergency response systems by older individuals with disabilities. Assistive Technol. 17(1), 82–88 (2005)CrossRefGoogle Scholar
  16. 16.
    E. Porter, Wearing and using personal emergency response system buttons: older frail widows’ intentions. J. Gerontol. Nurs. 31, 26–33 (2005)Google Scholar
  17. 17.
    M. Bernstein, “Low-tech” personal emergency response systems reduce costs and improve outcomes. Manag. Care Q. 8(1), 38 (2000)Google Scholar
  18. 18.
    D.A. Levine, R. Tideiksaar et al., Personal emergency response systems: factors associated with use among older persons. Mount Sinai J. Med. 62(4), 293 (1995)Google Scholar
  19. 19.
    M. Hamill, V. Young, J. Boger, A. Mihailidis, Development of an automated speech recognition interface for personal emergency response systems. J. NeuroEng. Rehabil. 6(1), 26 (2009)CrossRefGoogle Scholar
  20. 20.
    J. Lau, Building a national technology and innovation infrastructure for an aging society, Ph.D. Dissertation, University of Pennsylvania, 2005Google Scholar
  21. 21.
    R.W. Pew, S.B. Van Hemel et al., Technology for Adaptive Aging. (National Academies Press, Washington DC, 2004)Google Scholar
  22. 22.
    M.J.S. Gibson, R.O. Andres, B. Isaacs, T. Radebaugh, J. Worm-Petersen, The prevention of falls in later life. Dan. Med. Bull. 34(4), 1–24 (1987)Google Scholar
  23. 23.
    A. Tovell, K. McKenna, C. Bradley, S. Pointer, Hospital separations due to injury and poisoning, Australia, Technical Report, Australian Institute of Health and Welfare, 2012Google Scholar
  24. 24.
    C. Bradley, Hospitalisations due to falls in older people, Australia, 2003–2004, Technical Report, Australian Institute of Health and Welfare, 2012Google Scholar
  25. 25.
    K. Hauer, S.E. Lamb, E.C. Jorstad, C. Todd, C. Becker et al., Systematic review of definitions and methods of measuring falls in randomised controlled fall prevention trials. Age Ageing 35(1), 5–10 (2006)CrossRefGoogle Scholar
  26. 26.
    X. Yu, Approaches and principles of fall detection for elderly and patient, in Proceedings of the 10th International Conference on e-health Networking, Applications and Services, 2008, pp. 42–47Google Scholar
  27. 27.
    T. Lee, A. Mihailidis, An intelligent emergency response system: preliminary development and testing of automated fall detection. J. Telemed. Telecare 11(4), 194–198 (2005)CrossRefGoogle Scholar
  28. 28.
    A. Ariani, Simulation of a wireless sensor network for unobtrusively detecting falls in the home, Ph.D. Dissertation, University of New South Wales, 2012Google Scholar
  29. 29.
    A. Leone, G. Diraco, P. Siciliano, Detecting falls with 3d range camera in ambient assisted living applications: a preliminary study. Med. Eng. Phys. 33(6), 770–781 (2011)CrossRefGoogle Scholar
  30. 30.
    J. Shim, M.-h. Shim, Y.-s. Baek, T.-d. Han, The development of a detection system for seniors’ accidental fall from bed using cameras, in Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, 2011, p. 102Google Scholar
  31. 31.
    Y. Zigel, D. Litvak, I. Gannot, A method for automatic fall detection of elderly people using floor vibrations and sound-proof of concept on human mimicking doll falls, IEEE T. Bio-Med. Eng. 56(12), 2858–2867 (2009)Google Scholar
  32. 32.
    J. Willems, G. Debard, B. Vanrumste, T. Goedem´e, A video-based algorithm for elderly fall detection, in world congress on medical physics and biomedical engineering, Munich, Germany, 7–12 Sept, 2009, pp. 312–315Google Scholar
  33. 33.
    R. Steele, A. Lo, C. Secombe, Y.K. Wong, Elderly persons’ perception and acceptance of using wireless sensor networks to assist healthcare. Int. J. Med. Inform. 78(12), 788–801 (2009)CrossRefGoogle Scholar
  34. 34.
    A. Rowe, A resource-centric design paradigm for scalable sensor networks, Ph.D. Dissertation, Carnegie Mellon University, 2010Google Scholar
  35. 35.
    Y. Zigel, D. Litvak, I. Gannot, A method for automatic fall detection of elderly people using floor vibrations and sound-proof of concept on human mimicking doll falls. IEEE Trans. Biomed. Eng. 56(12), 2858–2867 (2009)CrossRefGoogle Scholar
  36. 36.
    O. Ojetola, E.I. Gaura, J. Brusey, Fall detection with wearable sensors–safe (smart fall detection), in Proceedings of the 7th International Conference on Intelligent Environments, 2011, pp. 318–321Google Scholar
  37. 37.
    M. Tolkiehn, L. Atallah, B. Lo, G.-Z. Yang, Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor, in Proceedings of the 32th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011, pp. 369–372Google Scholar
  38. 38.
    V.Q. Viet, G. Lee, D. Choi, Fall detection based on movement and smart phone technology, in Proceedings of the 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), 2012, pp. 1–4Google Scholar
  39. 39.
    M. Alwan, P.J. Rajendran, S. Kell, D. Mack, S. Dalal, M. Wolfe, R. Felder, A smart and passive floor-vibration based fall detector for elderly, in Proceedings of the 2nd International Conference on Information & Communication Technologies: from Theory to Applications, 2006, pp. 1003–1007Google Scholar
  40. 40.
    M. Stikic, T. Huynh, K.V. Laerhoven, B. Schiele, ADL recognition based on the combination of RFID and accelerometer sensing, in Proceedings of the 2nd International Conference on Pervasive Computing Technologies for Healthcare, 2008, pp. 258–263Google Scholar
  41. 41.
    D.L. Algase, Biomechanical activity devices to index wandering behaviour in dementia. Am. J. Alzheimer Dis. Dement. 18(2), 85–92 (2003)CrossRefGoogle Scholar
  42. 42.
    A. Ariani, S.J. Redmond, D. Chang, N.H. Lovell, Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors, in Proceedings of the 32rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, pp. 2115–2118Google Scholar
  43. 43.
    D. Litvak, I. Gannot, Y. Zigel, Detection of falls at home using floor vibrations and sound, in Proceedings of the IEEE 25th Convention of Electrical and Electronics Engineers, 2008, pp. 514–518Google Scholar
  44. 44.
    T. Liu, X. Guo, G. Wang, Elderly-falling detection using distributed direction-sensitive pyroelectric infrared sensor arrays. Multidimens. Syst. Signal Process. 23(4), 451–467 (2012)CrossRefMATHMathSciNetGoogle Scholar
  45. 45.
    A. Ariani, S.J. Redmond, D. Chang, N.H. Lovell, Simulated unobtrusive falls detection with multiple persons. IEEE Trans. Biomed. Eng. 59(11), 3185–3196 (2012)CrossRefGoogle Scholar
  46. 46.
    A.R. Kaushik, B.G. Celler, Characterization of passive infrared sensors for monitoring occupancy pattern, in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006, pp. 5257–5260Google Scholar
  47. 47.
    N. Cumming, Security: A Guide to Security System Design and Equipment Selection and Installation. (Butterworth-Heinemann, Oxford, 1994), ch.4, pp. 115–176Google Scholar
  48. 48.
    N. Noury, A. Galay, J. Pasquier, M. Ballussaud, Preliminary investigation into the use of autonomous fall detectors, in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, pp. 2828–2831Google Scholar
  49. 49.
    K. Johnston, A. Worley, K. Grimmer-Somers, M. Sutherland, L. Amos, Personal alarm use to call the ambulance after a fall in older people: characteristics of clients and falls. J. Emerg. Prim. Health Care 8(4), 1–9 (2010)Google Scholar
  50. 50.
    C.A. Otto, X. Chen, Automated fall detection: saving senior lives one fall at a time. Caring: National Association for Home Care magazine. 28(3) 44 (2009)Google Scholar
  51. 51.
    G. Diraco, A. Leone, P. Siciliano., An active vision system for fall detection and posture recognition in elderly healthcare, in Design, Automation & Test in Europe Conference & Exhibition (DATE), 2010, pp. 1536–1541Google Scholar
  52. 52.
    S. McLean, D. Protti, A. Sheikh, Telehealthcare for long term conditions. BMJ 342, 374–378 (2011)CrossRefGoogle Scholar
  53. 53.
    E.B. Allely, Synchronous and asynchronous telemedicine. J. Med. Syst. 19(3), 207–212 (1995)CrossRefGoogle Scholar
  54. 54.
    S.D. Anker, F. Koehler, W.T. Abraham, Telemedicine and remote management of patients with heart failure. Lancet 378, 731–739 (2011)CrossRefGoogle Scholar
  55. 55.
    M. Raad, L. Yang, A ubiquitous smart home for elderly, Inf. Syst. Front. 11(5) 1–5 (2009)Google Scholar
  56. 56.
    U. Hansmann, L. Merk, M.S. Nicklous, T. Stober, Pervasive Computing: The mobile world (Springer, New York, 2003)Google Scholar
  57. 57.
    J. Kim, M. Kang, B. Hwang, A method for detecting arrhythmia using a RR interval from ECG data in u-health system, in Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication, 2011, p. 15Google Scholar
  58. 58.
    B. Baby, M.S. Manikandan, K. Soman, Automated cardiac event change detection for continuous remote patient monitoring devices, in Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief, 2011, pp. 225–232Google Scholar
  59. 59.
    K. Li, N. Du, A. Zhang, Detecting ECG abnormalities via transductive transfer learning, in Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2012, pp. 210–217Google Scholar
  60. 60.
    V. Gay, P. Leijdekkers, E. Barin, A mobile rehabilitation application for the remote monitoring of cardiac patients after a heart attack or a coronary bypass surgery, in Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments, 2009, p. 21Google Scholar
  61. 61.
    H. Witte, L.D. Iasemidis, B. Litt, Special issue on epileptic seizure prediction. IEEE Trans. Biomed. Eng. 50(5), 537–539 (2003)CrossRefGoogle Scholar
  62. 62.
    A. Dalton, S. Patel, A. Chowdhury, M. Welsh, T. Pang, S. Schachter, G. OLaighin, P. Bonato, Development of a body sensor network to detect motor patterns of epileptic seizures. IEEE Trans. Biomed. Eng. 59(11), 3204–3211 (2012)CrossRefGoogle Scholar
  63. 63.
    C. Deckers, P. Genton, G. Sills, D. Schmidt et al., Current limitations of antiepileptic drug therapy: a conference review. Epilepsy Res. 53(1–2), 1 (2003)CrossRefGoogle Scholar
  64. 64.
    W.O. Tatum IV, L. Winters, M. Gieron, E.A. Passaro, S. Benbadis, J. Ferreira, J. Liporace, Outpatient seizure identification: results of 502 patients using computer-assisted ambulatory EEG. J. Clin. Neurophysiol. 18(1), 14–19 (2001)CrossRefGoogle Scholar
  65. 65.
    D. Schmidt, C. Elger, G.L. Holmes, Pharmacological overtreatment in epilepsy: mechanisms and management. Epilepsy Res. 52(1), 3–14 (2002)CrossRefGoogle Scholar
  66. 66.
    A.H. Shoeb, J.V. Guttag, Application of machine learning to epileptic seizure detection, in Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010, pp. 975–982Google Scholar
  67. 67.
    A. Economics, Listen Hear!: The Economic Impact and Cost of Hearing Loss in Australia (Access Economics, Sydney, 2006)Google Scholar
  68. 68.
    P. Hyvärinen, Utilization of the chirp stimulus in auditory brainstem response measurements, Ph.D. Dissertation, Aalto University, 2012Google Scholar
  69. 69.
    W.G. Noble, Assessment of Hearing Impairment: A Critique and a New Method (Academic Press, New York, 1978)Google Scholar
  70. 70.
    D.S. Dalton, K.J. Cruickshanks, B.E. Klein, R. Klein, T.L. Wiley, D.M. Nondahl, The impact of hearing loss on quality of life in older adults. Gerontologist 43(5), 661–668 (2003)CrossRefGoogle Scholar
  71. 71.
    A. Al-Afsaa, S. Soegijoko, Development of a chirp stimulus pc-based auditory brainstem response audiometer. ITB J. Eng. Sci. 36(1), 81–94 (2004)CrossRefGoogle Scholar
  72. 72.
    T. Dau, O. Wegner, V. Mellert, B. Kollmeier, Auditory brainstem responses with optimized chirp signals compensating basilar-membrane dispersion. J. Acoustical Soc. Am. 107, 1530–1540 (2000)CrossRefGoogle Scholar
  73. 73.
    K.R. Taylor, A.P. DeLuca, A. Eliot Shearer, M.S. Hildebrand, E. Ann Black-Ziegelbein, V. Nikhil Anand, C. M. Sloan, R.W. Eppsteiner, T.E. Scheetz, P.L. Huygen et al., Audiogene: predicting hearing loss genotypes from phenotypes to guide genetic screening, Hum. Mutat. 34(4) 539–545 (2012)Google Scholar
  74. 74.
    ABS, Musculoskeletal Conditions in Australia: A Snapshot, 200405 (Australian Bureau of Statistic, Canberra, 2006)Google Scholar
  75. 75.
    ABS, Injury in Australia: A snapshot, 2004-05 (Australian Bureau of Statistic, Canberra, 2006)Google Scholar
  76. 76.
    P. Hattam, The effectiveness of orthopaedic triage by extended scope physiotherapists. Clin. Gov. Int. J. 9(4), 244–252 (2004)CrossRefGoogle Scholar
  77. 77.
    T. Russell, P. Truter, R. Blumke, B. Richardson, The diagnostic accuracy of telerehabilitation for nonarticular lower-limb musculoskeletal disorders. Telemed. e-Health 16(5), 585–594 (2010)CrossRefGoogle Scholar
  78. 78.
    O.S. Lowe, Australia: services for Australian rural and remote allied health, inc. National Allied Health Workforce Report, Technical Report, 2004Google Scholar
  79. 79.
    D. Theodoros, T. Russell et al., Telerehabilitation: current perspectives. Stud. Health Technol. Inform. 131, 191–210 (2008)Google Scholar
  80. 80.
    Z. Cao, R. Zhu, R. Que, A wireless portable system with micro sensors for monitoring respiratory diseases. IEEE Trans. Biomed. Eng. 59(11), 3110–3116 (2012)CrossRefGoogle Scholar
  81. 81.
    J.G. Park, K. Ramar, E.J. Olson, Updates on definition, consequences, and management of obstructive sleep apnea. Mayo Clin. Proc. 86(6), 549–555 (2011)CrossRefGoogle Scholar
  82. 82.
    W. Lee, S. Nagubadi, M.H. Kryger, B. Mokhlesi, Epidemiology of Obstructive Sleep Apnea: A Population-Based Perspective (Expert Reviews Ltd, London, 2008)Google Scholar
  83. 83.
    T. Young, P.E. Peppard, D.J. Gottlieb, Epidemiology of obstructive sleep apnea a population health perspective. Am. J. Respir. Crit. Care Med. 165(9), 1217–1239 (2002)CrossRefGoogle Scholar
  84. 84.
    C.D. Mathers, D. Loncar, Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med. 3(11), e442 (2006)CrossRefGoogle Scholar
  85. 85.
    F. Agostinis, C. Foglia, M. Landi, M. Cottini, C. Lombardi, G.W. Canonica, G. Passalacqua, Gina report, global strategy for asthma management and prevention. Allergy 63(12), 1637–1639 (2008)CrossRefGoogle Scholar
  86. 86.
    M.J. Coma-del Corral, M.L. Alonso-Álvarez, M. Allende, J. Cordero, E. Ordax, F. Masa, J. Terán-Santos, Reliability of telemedicine in the diagnosis and treatment of sleep apnea syndrome. Telemed. e-Health 19(1) 7–12 (2013)Google Scholar
  87. 87.
    R.M. Sapolsky, G. Gurley, D. Demarest, E. internationales Télé-Film, Why zebras Don’t Get Ulcers (Times Books, New York, 2004)Google Scholar
  88. 88.
    F.H. Wilhelm, P. Grossman, Emotions beyond the laboratory: theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biol. Psychol. 84(3), 552–569 (2010)CrossRefGoogle Scholar
  89. 89.
    E. Ertin, N. Stohs, S. Kumar, A. Raij, M. al’Absi, S. Shah, Autosense: unobtrusively wearable sensor suite for inferring the onset, causality, and consequences of stress in the field, in Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, 2011, pp. 274–287Google Scholar
  90. 90.
    K.D. Kochanek, J. Xu, S.L. Murphy, A.M. Minino, H.-C. Kung, Deaths: preliminary data for 2009. Nat. Vital Stat. Rep. 59(4), 1–51 (2011)Google Scholar
  91. 91.
    S.L. Murphy, J. Xu, K.D. Kochanek, Deaths: preliminary data for 2010. Nat. Vital Stat. Rep. 60(4), 1–51 (2012)Google Scholar
  92. 92.
    V.L. Roger, A.S. Go, D.M. Lloyd-Jones, E.J. Benjamin, J.D. Berry, W.B. Borden, D.M. Bravata, S. Dai, E.S. Ford, C.S. Fox et al., Heart disease and stroke statistics 2012 update a report from the American heart association. Circulation 125(1), e2–e220 (2012)CrossRefGoogle Scholar
  93. 93.
    L.B. Goldstein, C.D. Bushnell, R.J. Adams, L.J. Appel, L.T. Braun, S. Chaturvedi, M.A. Creager, A. Culebras, R.H. Eckel, R.G. Hart et al., Guidelines for the primary prevention of stroke a guideline for healthcare professionals from the American heart association/American stroke association. Stroke 42(2), 517–584 (2011)CrossRefGoogle Scholar
  94. 94.
    J.N. Brownstein, Addressing heart disease and stroke prevention through comprehensive population-level approaches. Prev. Chronic Dis. 5(2), A31 (2008)Google Scholar
  95. 95.
    X. Ma, X. Tu, J. Huang, J. He, A cyber-physical system based framework for motor rehabilitation after stroke, in Proceedings of the 1st International Conference on Wireless Technologies for Humanitarian Relief, 2011, pp. 285–290Google Scholar
  96. 96.
    D.J. Foley, A.A. Monjan, S.L. Brown, E.M. Simonsick et al., Sleep complaints among elderly persons: an epidemiologic study of three communities. Sleep J. Sleep Res. Sleep Med. 18(6) 425–432 (1995)Google Scholar
  97. 97.
    D. Foley, S. Ancoli-Israel, P. Britz, J. Walsh, Sleep disturbances and chronic disease in older adults: results of the 2003 national sleep foundation sleep in america survey. J. Psychosom. Res. 56(5), 497–502 (2004)CrossRefGoogle Scholar
  98. 98.
    H. Ni, B. Abdulrazak, D. Zhang, S. Wu, Unobtrusive sleep posture detection for eldercare in smart home, in Aging Friendly Technology for Health and Independence (Springer, New York, 2010), pp. 67–75Google Scholar
  99. 99.
    WHO, Global Tuberculosis Report 2012 (World Health Organization, Geneva, 2012)Google Scholar
  100. 100.
    A.P. Koesoema, Y.S. Irawan, S. Soegijoko, Preliminary design of a community telemedicine system for tuberculosis control, in World Congress on Medical Physics and Biomedical Engineering 2006, 2007, pp. 366–369Google Scholar
  101. 101.
    H. Rachmat, L.I. Octavia, S. Soegijoko, Development of a simple e-health system for tuberculosis management at community health center level in Indonesia, in Med-e-Tel 2009 Proceedings, 2009, pp. 366–369Google Scholar
  102. 102.
    S. Soegijoko, Application specific e-health & telemedicine systems: implementation experience for community healthcare and systematic review of disaster publications, in Proceedings of the 2nd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, 2011, pp. 415–416Google Scholar
  103. 103.
    E. Priya, S. Srinivasan, Automated decision support system for tuberculosis digital images using evolutionary learning machines. Eur. J. Biomed. Inform. 9 en3–en8 (2013)Google Scholar
  104. 104.
    A.U. Alahakone, S.A. Senanayake, A real-time system with assistive feedback for postural control in rehabilitation. IEEE/ASME Trans. Mechatron. 15(2), 226–233 (2010)CrossRefGoogle Scholar
  105. 105.
    O. Aziz, L. Atallah, B. Lo, M. ElHelw, L. Wang, G.-Z. Yang, A. Darzi, A pervasive body sensor network for measuring postoperative recovery at home. Surg. Innovation 14(2), 83–90 (2007)CrossRefGoogle Scholar
  106. 106.
    E. Monton, J. Hernandez, J. Blasco, T. Herve, J. Micallef, I. Grech, A. Brincat, V. Traver, Body area network for wireless patient monitoring. IET Commun. 2(2), 215–222 (2008)CrossRefGoogle Scholar
  107. 107.
    V. Leonov, T. Torfs, P. Fiorini, C. Van Hoof, Thermoelectric converters of human warmth for self-powered wireless sensor nodes. Sens. J. IEEE 7(5), 650–657 (2007)CrossRefGoogle Scholar
  108. 108.
    B.-S. Lin, B.-S. Lin, N.-K. Chou, F.-C. Chong, S.-J. Chen, RTWPMS: a real-time wireless physiological monitoring system. IEEE Trans. Inf Technol. Biomed. 10(4), 647–656 (2006)CrossRefGoogle Scholar
  109. 109.
    A. Milenkovi′c, C. Otto, E. Jovanov, Wireless sensor networks for personal health monitoring: Issues and an implementation. Comput. Commun. 29(13), 2521–2533 (2006)CrossRefGoogle Scholar
  110. 110.
    J. Yao, R. Schmitz, S. Warren, A wearable point-of-care system for home use that incorporates plug-and-play and wireless standards. IEEE Trans. Inf Technol. Biomed. 9(3), 363–371 (2005)CrossRefGoogle Scholar
  111. 111.
    Z. Lv, F. Xia, G. Wu, L. Yao, Z. Chen, iCare: a mobile health monitoring system for the elderly, in Proceedings of the 2010 IEEE/ACM Int’l Conference on Green Computing and Communications & Int’l Conference on Cyber, Physical and Social Computing, 2010, pp. 699–705Google Scholar
  112. 112.
    W. Wu, J. Cao, Y. Zheng, Y.-P. Zheng, Waiter: a wearable personal healthcare and emergency aid system, in Proceedings of the 6th Annual IEEE International Conference on Pervasive Computing and Communications, 2008, pp. 680–685Google Scholar
  113. 113.
    A. Pantelopoulos, N.G. Bourbakis et al., Prognosis-a wearable health-monitoring system for people at risk: methodology and modeling. IEEE Trans. Inf Technol. Biomed. 14(3), 613–621 (2010)CrossRefGoogle Scholar
  114. 114.
    H.-T. Chang, C.-G. Chung, M.-W. Chen, An e-caring chair for physiological signal measurement and recording. Med. Eng. Phys. 35, 277–282 (2013)CrossRefGoogle Scholar
  115. 115.
    G.F. Fletcher, G. Balady, S.N. Blair, J. Blumenthal, C. Caspersen, B. Chaitman, S. Epstein, E.S.S. Froelicher, V.F. Froelicher, I.L. Pina et al., Statement on exercise: Benefits and recommendations for physical activity programs for all Americans a statement for health professionals by the committee on exercise and cardiac rehabilitation of the council on clinical cardiology, American heart association. Circulation 94(4), 857–862 (1996)CrossRefGoogle Scholar
  116. 116.
    L. Mo, S. Liu, R. Gao, D. John, J. Staudenmayer, P. Freedson, Wireless design of a multi-sensor system for physical activity monitoring. IEEE Trans. Biomed. Eng. 59(11), 3230–3237 (2012)CrossRefGoogle Scholar
  117. 117.
    A. Aridarma, T. Mengko, S. Soegijoko, Personal medical assistant: Future exploration, in Proceedings of the 2011 International Conference on Electrical Engineering and Informatics (ICEEI), 2011, pp. 1–6Google Scholar
  118. 118.
    E. Sutjiredjeki, S. Soegijoko, T.L. R. Mengko, S. Tjondronegoro, K. Astami, H. U. Muhammad et al., Application of a mobile telemedicine system with multi communication links for disaster reliefs in indonesia, in World Congress on Medical Physics and Biomedical Engineering 2009, 2009, pp. 344–347Google Scholar
  119. 119.
    E. Sutjiredjeki, S. Soegijoko, Development of a communication arbiter for mobile telemedicine system with multi communication links, in World Congress on Medical Physics and Biomedical Engineering 2006, 2007, pp. 715–718Google Scholar
  120. 120.
    E. Sutjiredjeki, S. Soegijoko, T.L.R. Mengko, S. Tjondronegoro, Development of a mobile telemedicine system with multi communication links for urban and rural areas in Indonesia, in Proceedings of the 3rd Kuala Lumpur International Conference on Biomedical Engineering, 2007, pp. 660–663Google Scholar
  121. 121.
    A. Gaddam, S. Mukhopadhyay, G. Sen Gupta, H. Guesgen, Wireless sensors networks based monitoring: Review, challenges and implementation issues, in Proceedings of the 3 rd International Conference on Sensing Technology, 2008, pp. 533–538Google Scholar
  122. 122.
    J.-C. Huang, Innovative health care delivery system: a questionnaire survey to evaluate the influence of behavioral factors on individuals’ acceptance of telecare. Comput. Biol. Med. 43, 281–286 (2013)CrossRefGoogle Scholar
  123. 123.
    M. Souil and A. Bouabdallah, On QoS provisioning in context-aware wireless sensor networks for healthcare, in Proceedings of the 20th International Conference on Computer Communications and Networks, 2011, pp. 1–6Google Scholar
  124. 124.
    A. Mihailidis, A. Cockburn, C. Longley, J. Boger, The acceptability of home monitoring technology among community-dwelling older adults and baby boomers. Assistive Technol. 20(1), 1–12 (2008)CrossRefGoogle Scholar
  125. 125.
    D. Malan, T. Fulford-Jones, M. Welsh, S. Moulton, Codeblue: an ad hoc sensor network infrastructure for emergency medical care, in Proceedings of the MobiSys 2004 Workshop on Applications of Mobile Embedded Systems, 2004, pp. 12–14Google Scholar
  126. 126.
    M. Sung, C. Marci, A. Pentland, Wearable feedback systems for rehabilitation. J. Neuroeng. Rehabil. 2(17), 1–12 (2005)Google Scholar
  127. 127.
    S. Brage, N. Brage, P. Franks, U. Ekelund, N. Wareham, Reliability and validity of the combined heart rate and movement sensor actiheart. Eur. J. Clin. Nutr. 59(4), 561–570 (2005)CrossRefGoogle Scholar
  128. 128.
    T. Huynh, B. Schiele, Analyzing features for activity recognition, in Proceedings of the 2005 Joint Conference on Smart Objects and Ambient Intelligence: Innovative Context Aware Services: Usages and Technologies, 2005, pp. 159–163Google Scholar
  129. 129.
    P. Lukowicz, J.A. Ward, H. Junker, M. Stäger, G. Tröster, A. Atrash, T. Starner, Recognizing workshop activity using body worn microphones and accelerometers, in Pervasive Computing (Springer, 2004), pp. 18–32Google Scholar
  130. 130.
    D.L. Hall, J. Llinas, An introduction to multisensor data fusion. Proc. IEEE 85(1), 6–23 (1997)CrossRefGoogle Scholar
  131. 131.
    D. Kotz, S. Avancha, A. Baxi, A privacy framework for mobile health and home-care systems, in Proceedings of the 1st ACM workshop on Security and privacy in medical and home-care systems, 2009, pp. 1–12Google Scholar
  132. 132.
    R. Simpson, D. Schreckenghost, E.F. LoPresti, N. Kirsch, Plans and planning in smart homes, in Designing Smart Homes (Springer, New York, 2006), pp. 71–84Google Scholar
  133. 133.
    G. Mantas, D. Lymberopoulos, N. Komninos, Integrity mechanism for e-health telemonitoring system in smart home environment, in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009, pp. 3509–3512Google Scholar
  134. 134.
    M. Guennoun, K. El-Khatib, Securing medical data in smart homes, in Proceedings of the 4th IEEE International Workshop on Medical Measurements and Applications, 2009, pp. 104–107Google Scholar
  135. 135.
    B. Fong, M. Pecht, Prognostics in wireless telecare networks: a perspective on serving the rural chinese population, in Proceedings of the 2010 Annual Conference of the Prognostics and Health Management Society, 2010, pp. 1–6Google Scholar
  136. 136.
    M. Wang, K. Fishkin, A Flexible, Low-Overhead Ubiquitous System for Medication Monitoring (Intel Corporation, Tech. Rep., 2003)Google Scholar
  137. 137.
    J.K. Vinjumur, E. Becker, S. Ferdous, G. Galatas, F. Makedon, Web based medicine intake tracking application, in Proceedings of the 3rd International Conference on Pervasive Technologies Related To Assistive Environments. ACM, 2010, p. 37Google Scholar
  138. 138.
    D. Hess, R.M. Kacmarek, M.H. Kollef, Essentials of Mechanical Ventilation (McGraw-Hill, Health Professions Division, New York, 1996)Google Scholar
  139. 139.
    A.M. Cheng, Cyber-physical medical and medication systems, in Proceedings of the 28th International Conference on Distributed Computing Systems Workshop, 2008, pp. 529–532Google Scholar
  140. 140.
    M. Pajic, R. Mangharam, O. Sokolsky, D. Arney, J. Goldman, I. Lee, Model-driven safety analysis of closed-loop medical systems. IEEE Trans. Industr. Inf. 99, 1–13 (2012)Google Scholar
  141. 141.
    T.K. Nuckols, A.G. Bower, S.M. Paddock, L.H. Hilborne, P. Wallace, J.M. Rothschild, A. Griffin, R.J. Fairbanks, B. Carlson, R.J. Panzer, R.H. Brook, Programmable infusion pumps in icus: an analysis of corresponding adverse drug events. J. Gen. Intern. Med. 23(1), 41–45 (2008)CrossRefGoogle Scholar
  142. 142.
    J.T. Matthews, Existing and emerging healthcare devices for elders to use at home. Generations 30(2), 13–19 (2006)MathSciNetGoogle Scholar
  143. 143.
    K. Daniel, C.L. Cason, S. Ferrell, Assistive technologies for use in the home to prolong independence, in Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments, 2009, p. 26Google Scholar
  144. 144.
    E. Becker, V. Metsis, R. Arora, J. Vinjumur, Y. Xu, F. Makedon, Smartdrawer: RFID based smart medicine drawer for assistive environments, in Proceedings of the 2nd International Conference on Pervasive Technologies Related to Assistive Environments, 2009, p. 49Google Scholar
  145. 145.
    L.-A. Tang, X. Yu, S. Kim, Q. Gu, J. Han, A. Leung, T. La Porta, Trustworthiness analysis of sensor data in cyber-physical systems. J. Comput. Syst. Sci. 79, 383–401 (2012)CrossRefGoogle Scholar
  146. 146.
    E.A. Lee, Cyber physical systems: design challenges, in Proceedings of the 11th IEEE International Symposium on Object Oriented Real-Time Distributed Computing (ISORC), 2008, pp. 363–369Google Scholar
  147. 147.
    P. Buonadonna, D. Gay, J.M. Hellerstein, W. Hong, S. Madden, Task: sensor network in a box, in Proceedings of the 2nd European Workshop on Wireless Sensor Networks, 2005, pp. 133–144Google Scholar
  148. 148.
    R. Szewczyk, J. Polastre, A. Mainwaring, D. Culler, Lessons from a sensor network expedition, in Wireless Sensor Networks. Springer, 2004, pp. 307–322Google Scholar
  149. 149.
    K. Ni, G. Pottie, Bayesian selection of non-faulty sensors, in Proceedings of the IEEE International Symposium on Information Theory, 2007, pp. 616–620Google Scholar
  150. 150.
    H. Gupta, Z. Zhou, S.R. Das, Q. Gu, Connected sensor cover: self-organization of sensor networks for efficient query execution. IEEE/ACM Trans. Netw. 14(1), 55–67 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  1. 1.Indonesian Biomedical Engineering Society (IBES), Biomedical Engineering Program, School of Electrical Engineering and InformaticsInstitut Teknologi Bandung (ITB)BandungIndonesia
  2. 2.Biomedical Engineering Program, School of Electrical Engineering and InformaticsInstitut Teknologi Bandung (ITB)BandungIndonesia

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