Internet of things for remote elderly monitoring: a study from user-centered perspective

  • Iman Azimi
  • Amir M. Rahmani
  • Pasi Liljeberg
  • Hannu Tenhunen
Original Research


Improvements in life expectancy achieved by technological advancements in the recent decades have increased the proportion of elderly people. Frailty of old age, susceptibility to diseases, and impairments are inevitable issues that these senior adults need to deal with in daily life. Recently, there has been an increasing demand on developing elderly care services utilizing novel technologies, with the aim of providing independent living. Internet of things (IoT), as an advanced paradigm to connect physical and virtual things for enhanced services, has been introduced that can provide significant improvements in remote elderly monitoring. Several efforts have been recently devoted to address elderly care requirements utilizing IoT-based systems. Nevertheless, there still exists a lack of user-centered study from an all-inclusive perspective for investigating the daily needs of senior adults. In this paper, we study the IoT-enabled systems tackling elderly monitoring to categorize the existing approaches from a new perspective and to introduce a hierarchical model for elderly-centered monitoring. We investigate the existing approaches by considering the elderly requirements at the center of the attention. In addition, we evaluate the main objectives and trends in IoT-based elderly monitoring systems in order to pave the way for future systems to improve the quality of elderly’s life.


Internet of things Elderly care Remote elderly monitoring Healthcare and well-being 


  1. Agarwal A, Miller J, Eastep J, Wentziaff D, Kasture H (2009) Self-aware computing. Technical report, MITGoogle Scholar
  2. Ahmed MU, Banaee H, Rafael-Palou X, Loutfi A (2015) Intelligent healthcare services to support health monitoring of elderly. In: Internet of things. user-centric IoT, volume 150 of lecture notes of the institute for computer sciences, social informatics and telecommunications engineering. Springer International Publishing, pp 178–186Google Scholar
  3. Akl A, Taati B, Mihailidis A (2015) Autonomous unobtrusive detection of mild cognitive impairment in older adults. IEEE Trans Biomed Eng 62(5):1383–1394CrossRefGoogle Scholar
  4. ALFRED (2015) ALFRED \(\mid\) personal interactive assistant for independent living and active ageing. Retrieved on December 2015.
  5. Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang G-Z (2015) Big data for health. IEEE J Biomed Health Inform 19(4):1193–1208CrossRefGoogle Scholar
  6. Anzanpour A, Rahmani AM, Liljeberg P, Tenhunen H (2015) Internet of things enabled in-home health monitoring system using early warning score. In: Proceedings of ACM international conference on wireless mobile communication and healthcare, 2015Google Scholar
  7. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805CrossRefzbMATHGoogle Scholar
  8. Azimi I, Anzanpour A, Rahmani AM, Liljeberg P, Tenhunen H (2016) Self-aware early warning score system for iot-based personalized healthcare. In: Proceedings of international conference on IoT and big data technologies for healthCare, 2016Google Scholar
  9. Badawika A, Kolakowski J (2014) UWB positioning system architecture based on paired anchor nodes. In: Proceedings of 20th international conference on microwaves, radar, and wireless communications, pp 1–4, 2014Google Scholar
  10. Bai Y, Li C, Yue Y, Jia W, Li J, Mao Z-H, Sun M (2012) Designing a wearable computer for lifestyle evaluation. In: 38th annual northeast bioengineering conference, pp 93–94, 2012Google Scholar
  11. Barham P (2013) ASSISTANT—creating a smartphone app to assist older people when travelling. In: de Waard D, Brookhuis K, Wiczorek R, di Nocera F, Brouwer R, Barham P, Weikert C, Kluge A, Gerbino W, Toffetti A (eds) (2014) Proceedings of the human factors and ergonomics society Europe chapter 2013 annual conference, 2013Google Scholar
  12. Berndt RD, Takenga MC, Kuehn S, Preik P, Berndt S, Brandstoetter M, Planinc R, Kampel M (2012) An assisted living system for the elderly FEARLESS concept. In: Proceedings of the IADIS multi conference on computer science and information systems, pp 131–138, 2012Google Scholar
  13. Beyer M (2015) Gartner says solving ’Big Data’ challenge involves more than just managing volumes of data. Retrieved on December 2015.
  14. Bian ZP, Hou J, Chau LP, Magnenat-Thalmann N (2015) Fall detection based on body part tracking using a depth camera. IEEE J Biomed Health Inform 19(2):430–439CrossRefGoogle Scholar
  15. Bishop CM (2006) Pattern recognition and machine learning. Springer-Verlag New York Inc, New YorkzbMATHGoogle Scholar
  16. Bonomi F, Milito R, Natarajan P, Zhu J (2014) Fog computing: a platform for internet of things and analytics. In: Big data and internet of things: a roadmap for smart environments, pp 169–186, 2014Google Scholar
  17. Borsella E, Mantovani E, Porcari A (2015) Information and communication technologies for health, demographic change and wellbeing: a survey of the technological scenario. Technical report, Italian Association for Industrial Research (AIRI, Italy)Google Scholar
  18. Boujelbane I, Said SH, Zaharia T (2014) Multi-object recognition and tracking with feature points matching and spatial layout consistency. In: Proceedings of IEEE fourth international conference on consumer electronics, Berlin, pp 355–359, 2014Google Scholar
  19. Brugger M, Christ T, Kemeth F, Nagy S, Schaefer M, Pietrzyk M (2010) The FMCW technology-based indoor localization system. In: Proceedings of ubiquitous positioning indoor navigation and location based service, pp 1–6, 2010Google Scholar
  20. Caon DRS, Simonnet T, Sendorek P, Boudy J, Chollet G (2011) vAssist: the virtual interactive assistant for daily home-care. In: Proceedings of 8th international conference on wearable nano and macro technologies for personalized health, 2011Google Scholar
  21. Carmien S, Obach M (2013) Back on track: lost and found on public transportation. In: Proceedings of the 7th international conference on universal access in human–computer interaction: user and context diversity, vol 2, pp 575–584, 2013Google Scholar
  22. Carus JL, Garcia S, Garcia R, Waterworth J, Erdt S (2014) The ELF@Home project: elderly sELF-care based on sELF-check of health conditions and sELF-fitness at home. Stud Health Technol Inform 200(16):164–166Google Scholar
  23. Charlon Y, Fourty N, Campo E (2013) A telemetry system embedded in clothes for indoor localization and elderly health monitoring. Sensors (Basel, Switzerland) 13(9):11728–11749Google Scholar
  24. Cheng J, Zhou B, Kunze K, Rheinländer CC, Wille S, Wehn N, Weppner J, Lukowicz P (2013) Activity recognition and nutrition monitoring in every day situations with a textile capacitive neckband. In: Proceedings of the 2013 ACM conference on pervasive and ubiquitous computing adjunct publication, pp 155–158, 2013Google Scholar
  25. Cheng SH (2014) An intelligent fall detection system using triaxial accelerometer integrated by active RFID. In: Prodeedings of 13th international conference on machine learning and cybernetics, pp 517–522, 2014Google Scholar
  26. Chifu VR, Salomie I, Chifu ES, Izabella B, Pop CB, Antal M (2014) Cuckoo search algorithm for clustering food offers. In: Proceedings of 2014 IEEE international conference on intelligent computer communication and processing, pp 17–22, 2014Google Scholar
  27. Dohr A, Modre-Opsrian R, Drobics M, Hayn D, Schreier G (2010) The internet of things for ambient assisted living. In: Proceedings of seventh international conference on information technology: new generations, pp 804–809, 2010Google Scholar
  28. Domenech S, Rivero J, Coll-Planas L, Sainz FJ, Reissner A, Miralles F (2013) Involving older people in the design of an innovative technological system promoting active aging: the SAAPHO project. J Access Des All 3(1):13–27Google Scholar
  29. EDLAH (2015) Retrieved on December 2015.
  30. Faetti T, Paradiso R (2012) A novel wearable system for elderly monitoring. Adv Sci Technol 85:17–22CrossRefGoogle Scholar
  31. Fang SH, Liang YC, Chiu KM (2012) Developing a mobile phone-based fall detection system on android platform. In: Proceedings of computing, communications and applications conference, pp 143–146, 2012Google Scholar
  32. GeTVivid (2015) Retrieved on December 2015.
  33. Gjoreski H, Lutrek M, Gams M (2011) Accelerometer placement for posture recognition and fall detection. In: Proceedings of 7th international conference on intelligent environments, pp 47–54, 2011Google Scholar
  34. Glaeser DH, Thomas LJ Jr (1975) Computer monitoring in patient care. Annu Rev Biophys Bioeng 4:449–476CrossRefGoogle Scholar
  35. Gokalp H, Clarke M (2013) Monitoring activities of daily living of the elderly and the potential for its use in telecare and telehealth: a review. Telemed J E Health 19(12):910–923CrossRefGoogle Scholar
  36. Hamdi O, Chalouf MA, Ouattara D, Krief F (2014) eHealth: survey on research projects, comparative study of telemonitoring architectures and main issues. J Netw Comput Appl 46:100–112CrossRefGoogle Scholar
  37. Hardy S, Feldwieser F, Dutz SGT, Steinmetz, Steinhagen-Thiessen E (2015) ALFRED back trainer: conceptualization of a serious game-based training system for low back pain rehabilitation exercises. In: Göbel S et al (eds) Serious games, volume 9090 of lecture notes in computer science. Springer International Publishing, New York, pp 36–47Google Scholar
  38. Henze M, Hermerschmidt L, Kerpen D, Haußling R, Rumpe B, Wehrle K (2016) A comprehensive approach to privacy in the cloud-based internet of things. Future Gener Comput Syst 56:701–718CrossRefGoogle Scholar
  39. HEREiAM (2015) Retrieved on December 2015.
  40. Hickson M (2006) Malnutrition and ageing. Postgrad Med J 82(963):2–8CrossRefGoogle Scholar
  41. Huaxin S, Qi X, Xiaodong L, Baoyan L, Shusong M, Xuezhong Z (2012) Constructing ideas of health service platform for the elderly. In: Proceedings of 14th international conference on e-health networking, applications and services, pp 526–529, 2012Google Scholar
  42. Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. BioMed Eng OnLine 12(1):1–24CrossRefGoogle Scholar
  43. Ionescu G, de la Osa CM, Deriaz M (2014) Improving distance estimation in object localisation with bluetooth low energy. In: Proceedings of the eighth international conference on sensor technologies and applications, 2014Google Scholar
  44. Iosifidis A, Marami E, Tefas A, Pitas I (2012) Eating and drinking activity recognition based on discriminant analysis of fuzzy distances and activity volumes. In: Proceedings of IEEE international conference on acoustics, speech and signal processing, pp 2201–2204, 2012Google Scholar
  45. Iosifidis A, Tefas A, Pitas I (2013) Minimum class variance extreme learning machine for human action recognition. IEEE Trans Circuits Syst Video Technol 23(11):1968–1979CrossRefGoogle Scholar
  46. Jara AJ, Zamora-Izquierdo MA, Skarmeta AF (2013) Interconnection framework for mhealth and remote monitoring based on the internet of things. IEEE J Sel Areas Commun 31(9):47–65CrossRefGoogle Scholar
  47. Juang LH, Wu MN (2015) Fall down detection under smart home system. J Med Syst 39(10):1–12CrossRefGoogle Scholar
  48. Kalian K, Kainz W (2013) ASSISTANT—aiding sustainable independent senior travellers to navigate in towns. In: Proceedings of the European navigation conference, 2013Google Scholar
  49. Kaluza B, Cvetkovic B, Dovgan E, Gjoreski H, Gams M, Lustrek M (2014) A multi-agent care system to support independent living. Int J Artif Intell Tools 23(1):1440001CrossRefGoogle Scholar
  50. Kasteren TL, Englebienne G, Krose BJ (2010) An activity monitoring system for elderly care using generative and discriminative models. Pers Ubiquit Comput 14(6):489–498CrossRefGoogle Scholar
  51. Kozina S, Lustrek M, Gams M (2011) Dynamic signal segmentation for activity recognition. In: Proceedings of international joint conference on artificial intelligence, 2011Google Scholar
  52. Krieg-Brckner B, Bothmer H, Budelmann C, Crombie D, Guern A, Heindorf A, Lifante J, Martnez AB, Millet S, Velleman E (2012) Assistance for safe mobility: the ASSAM project. AAL-Forum, 2012Google Scholar
  53. Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Application Delivery Strategies by META Group Inc., Technical reportGoogle Scholar
  54. Lattanzio F, Abbatecola AM, Bevilacqua R, Chiatti C, Corsonello A, Rossi L, Bustacchini S, Bernabei R (2014) Advanced technology care innovation for older people in Italy: necessity and opportunity to promote health and wellbeing. J Am Med Dir Assoc 15(7):457–466CrossRefGoogle Scholar
  55. Lee JV, Chuah YD, Chieng KTH (2013) Smart elderly home monitoring system with an android phone. Int J Smart Home 7(3):17–32Google Scholar
  56. Lin CC, Chiu MJ, Hsiao CC, Lee RG, Tsai YS (2006) Wireless health care service system for elderly with dementia. IEEE Trans Inform Technol Biomed 10(4):696–704CrossRefGoogle Scholar
  57. Liu N, Lin Z, Cao J, Koh Z, Zhang T, Huang G-B, Ser W, Ong MEH (2012) An intelligent scoring system and its application to cardiac arrest prediction. IEEE Trans Inf Technol Biomed 16(6):1324–1331CrossRefGoogle Scholar
  58. Lustrek M, Kaluza B (2009) Fall detection and activity recognition with machine learning. Informatica 33:205–212Google Scholar
  59. Macis S, Loi D, Angius G, Pani D, Raffo L, Rijnen W, Nap H (2014) Towards an integrated tv-based system for active ageing and tele-care. Poster presented by Silvia Macis (UniCa) at the 4th National Congress of Italian Group of Bioengineering (GNB), 2014Google Scholar
  60. Mandel C, Birbach O (2013) Localization in urban environments by matching sensor data to map information. In: Proceedings of the 6th European conference on mobile robots, 2013Google Scholar
  61. Marami E, Tefas A, Pitas I (2011) Nutrition assistance based on skin color segmentation and support vector machines. In: Czachórski T et al (eds) Man–machine interactions 2., volume 103 of advances in intelligent and soft computing. Springer, Berlin, pp 179–187Google Scholar
  62. Maronidis A, Tefas A, Pitas I (2010) Frontal view recognition using spectral clustering and subspace learning methods. In: Diamantaras K et al (eds) Artificial neural networks ICANN 2010, volume 6352 of lecture notes in computer science. Springer, Berlin, pp 460–469Google Scholar
  63. Maronidis A, Bolis D, Tefas A, Pitas I (2011) Improving subspace learning for facial expression recognition using person dependent and geometrically enriched training sets. Neural Netw 24(8):814–823CrossRefGoogle Scholar
  64. Mellone S, Tacconi C, Schwickert L, Klenk J, Becker C, Chiari L (2012) Smartphone-based solutions for fall detection and prevention: the FARSEEING approach. Zeitschrift für Gerontologie und Geriatrie 45(8):722–727Google Scholar
  65. Memon M, Wagner SR, Pedersen CF, Beevi FHA, Hansen FO (2014) Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 14(3):4312–4341CrossRefGoogle Scholar
  66. Milhorat P, Schlgl S, Chollet G, Boudy J (2013) What if everyone could do it?: a framework for easier spoken dialog system design. In: Proceedings of the 5th ACM SIGCHI symposium on engineering interactive computing systems, pp 217–222, 2013Google Scholar
  67. Milhorat P, Schlgl S, Chollet G, Boudy J, Espisoto A, Pelosi G (2014) Building the next generation of personal digital assistant. In: Proceeding of 1st international conference on advanced technologies for signal and image processing, pp 458–463, 2014Google Scholar
  68. Moosavi SR, Gia TN, Nigussie E, Rahmani AM, Virtanen S, Tenhunen H, Isoaho J (2016) End-to-end security scheme for mobility enabled healthcare internet of things. Future Gener Comput SystGoogle Scholar
  69. Morgan RJM, Williams F, Wright MM (1997) An early warning scoring system for detecting developing critical illness. Clin Intens Care 8(2):100Google Scholar
  70. Moser C, Kargl T, Tscheligi M, Feldbacher B, Collini-Nocker B, Harutunian M, Schiller F, Eitelberg M, Altaani N, Eisele M, Osl P (2015) A TV platform for P2P support exchange. In: TVX2015, 2015Google Scholar
  71. Mukherjee A, Pal A, Misra P (2012) Data analytics in ubiquitous sensor-based health information systems. In: Proceedings of sixth international conference on next generation mobile applications, services and technologies, pp 193–198, 2012Google Scholar
  72. Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, CambridgezbMATHGoogle Scholar
  73. Nani M, Caleb-Solly P, Dogramadzi S, Fear T, van den Heuvel H (2010) MOBISERV: an integrated intelligent home environment for the provision of health, nutrition and mobility services to the elderly. In: 4th companion robotics workshop in Brussels, 2010Google Scholar
  74. Neisse R, Steri G, Fovino IN, Baldini G (2015) SecKit: a model-based security toolkit for the internet of things. Comput Secur 54:60–76CrossRefGoogle Scholar
  75. NITICS (2015) Retrieved on December 2015.
  76. Niyato D, Hossain E, Camorlinga S (2009) Remote patient monitoring service using heterogeneous wireless access networks: architecture and optimization. IEEE J Sel Areas Commun 27(4):412–423CrossRefGoogle Scholar
  77. Odunmbaku A, Rahmani AM, Liljeberg P, Tenhunen H (2015) Elderly monitoring system with sleep and fall detector. In: International conference on IoT technologies for healthcare, 2015Google Scholar
  78. OpenPR (2015)—Press release—Ascora GmbH—Project ALFRED: personal interactive assistant for independent living and active ageing, Retrieved on December 2015.
  79. Pfuntner A, Wier L, Steiner C, (2013) Costs for hospital stays in the united states, 2011. HCUP Statistical Brief # 168. Agency for Healthcare Research and Quality, RockvilleGoogle Scholar
  80. Pierleoni P, Belli A, Palma L, Pellegrini M, Pernini L, Valenti S (2015) A high reliability wearable device for elderly fall detection. IEEE Sens J 15(8):4544–4553CrossRefGoogle Scholar
  81. Planinc R, Kampel M (2011) Emergency system for elderly—a computer vision based approach. In: Bravo J et al (eds) Ambient assisted living, volume 6693 of lecture notes in computer science. Springer, Berlin, pp 79–83Google Scholar
  82. Planinc R, Kampel M (2012a) Introducing the use of depth data for fall detection. Pers Ubiquit Comput 17(6):1063–1072CrossRefGoogle Scholar
  83. Planinc R, Kampel M (2012b) Robust fall detection by combining 3d data and fuzzy logic. In: Park J et al (eds) Computer vision—ACCV 2012 workshops, volume 7729 of lecture notes in computer science. Springer, Berlin, pp 121–132Google Scholar
  84. Planinc R, Kampel M (2014) Detecting unusual inactivity by introducing activity histogram comparisons. In: Proceedings of the international conference on computer vision theory and applications, pp 313–320, 2014Google Scholar
  85. Planinc R, Kampel M, Zambanini S (2011) Audiovisual Assistance for the elderly—an overview of the fearless project. In: Abdulrazak B et al (eds) Toward useful services for elderly and people with disabilities, volume 6719 of lecture notes in computer science. Springer, Berlin, pp 225–229Google Scholar
  86. Preden JS, Tammemae K, Jantsch A, Leier M, Riid A, Calis E (2015) The benefits of self-awareness and attention in fog and mist computing. Computer 48(7):37–45CrossRefGoogle Scholar
  87. Rafael-Palou X, Vargiu E, Miralles F (2015) Monitoring people that need assistance through a sensor-based system: evaluation and first results. In: 4th international workshop on artificial intelligence and assistive medicine, 2015Google Scholar
  88. Rahmani AM, Thanigaivelan NK, Gia TN, Granados J, Negash B, Liljeberg P, Tenhunen H (2015) Smart e-Health gateway: bringing intelligence to internet-of-things based ubiquitous healthcare systems. In: Proceedings of annual IEEE consumer communications and networking conference, pp 826–834, 2015Google Scholar
  89. Ray P (2014) Home health hub internet of things (H3IoT): an architectural framework for monitoring health of elderly people. In: Proceedings of international conference on science engineering and management research, pp 1–3, 2014Google Scholar
  90. Ritter FE, Baxter GD, Churchill EF (2014) Foundations for designing user-centered systems: what system designers need to know about people. Springer Publishing Company, Incorporated, New YorkCrossRefGoogle Scholar
  91. Rivero-Espinosa J, Iglesias-Prez A, Gutirrez-Dueas JA, Rafael-Palou X (2013) SAAPHO: an AAL architecture to provide accessible and usable active aging services for the elderly. ACM SIGACCESS Access Comput 8(107):17–24CrossRefGoogle Scholar
  92. Rosa MD, Stara V, Rossi L, Breuil F, Reixach E, Paredes JG (2015) Burkard S (2015) A wireless sensor insole to collect and analyse gait data in real environment: the WIISEL project. In: Andò B et al (eds) Ambient assisted living, volume 11 of biosystems and biorobotics. Springer International Publishing, New York, pp 71–80Google Scholar
  93. Russell S, Norvig P (2013) Artificial intelligence: a modern approach, 3rd edn. Pearson, LondonzbMATHGoogle Scholar
  94. Rusu L, Cramariuc B, Benta D, Mailat M (2015) Implementing BPMN 2.0 scenarios for AAL@Home solution. Int J Comput Commun Control 10(2):230–237CrossRefGoogle Scholar
  95. Said SH, Boujelbane I, Zaharia T (2014) Recognition of urban buildings with spatial consistency and a small-sized vocabulary tree. In: Proceedings of IEEE international conference on consumer electronics Berlin, pp 350–354, 2014Google Scholar
  96. Sanchez J, Sanchez V, Salomie I, Taweel A, Charvill J, Araujo M (2013) Dynamic nutrition behaviour awareness system for the elders. In: Proceedings of the 5th AAL forum norrkoping, impacting individuals, society and economic growth, 2013Google Scholar
  97. Sansen H, Baldinger J-L, Boudy J, Chollet G, Milhorat P, Schlgl S (2014) vAssist. Building the personal assistant for dependent people—helping dependent people to cope with technology through speech. In: Proceedings of international conference on health informatics, 2014Google Scholar
  98. Sicari S, Rizzardi A, Grieco LA, Coen-Porisini A (2015) Security, privacy and trust in internet of things: the road ahead. Comput Netw 76:146–164CrossRefGoogle Scholar
  99. Spinsante S, Gambi E (2012) Remote health monitoring for elderly through interactive television. BioMed Eng OnLine 11:54CrossRefGoogle Scholar
  100. Sposaro F, Tyson G (2009) iFall: an android application for fall monitoring and response. In: Proceedings of 31st annual international conference of the IEEE engineering in medicine and biology society, pp 6119–6122, 2009Google Scholar
  101. Stefanov DH, Bien Z, Bang W-C (2004) The smart house for older persons and persons with physical disabilities: structure, technology arrangements, and perspectives. IEEE Trans Neural Syst Rehabil Eng 12(2):228–250CrossRefGoogle Scholar
  102. Stratton R, Green C, Elia M (2003) Disease-related malnutrition: an evidence-based approach to treatment. CABI Publishing Series, CABI Pub., CambridgeGoogle Scholar
  103. Sun M, Burke LE, Mao Z-H, Chen Y, Chen H-C, Bai Y, Li Y, Li C, Jia W (2014) ebutton: a wearable computer for health monitoring and personal assistance. In: Proceedings design automation conference, pp 1–6, 2014Google Scholar
  104. Tapu R, Mocanu B, Bursuc A, Zaharia T (2013) A smartphone-based obstacle detection and classification system for assisting visually impaired people. In: Proceeding of IEEE international conference on computer vision, workshop on wearable computer vision systems, pp 444–451, 2013Google Scholar
  105. Tapu R, Vizintin M, Zaharia T (2014) An efficient and affordable device to improve cognition and navigation of visually impaired people. AAL solutions for Europe—AAL forum 2014Google Scholar
  106. Tsiourti C, Joly E, Wings C, Moussa MB, Wac K (2014) Virtual assistive companion for older adults: field study and design implications. In: Proceedings of 8th international conference on pervasive computing technologies for healthcare (PervasiveHealth), 2014Google Scholar
  107. Touati F, Tabish R (2013) U-healthcare system: state-of-the-art review and challenges. J Med Syst 37:9949CrossRefGoogle Scholar
  108. Tsukiyama T (2015) In-home health monitoring system for solitary elderly. Procedia computer science. 5th international conference on current and future trends of information and communication technologies in healthcare, vol 63, pp 229–235, 2015Google Scholar
  109. TVX2015-Workshop (2015) A workshop on “People, Context, and Devices: Defining the New Landscape of TV Experiences”. Retrieved on December 2015.
  110. United Nations (2015) Probabilistic population projections based on the world population prospects: the 2015 revision. Population division, DESA., 2015.
  111. Vucinic M, Tourancheau B, Rousseau F, Duda A, Damon L, Guizzetti R (2015) OSCAR: object security architecture for the internet of things. Ad Hoc Netw 32:3–16CrossRefGoogle Scholar
  112. WHO (2011) Global health and ageing. WHO, Technical reportGoogle Scholar
  113. WHO (2014) World health statistics 2014: a wealth of information on global public health. WHO, Technical reportGoogle Scholar
  114. WHO (2016a) Nutrition for older persons. Retrieved on April 2016a.
  115. WHO (2016b) Falls. Retrieved on April 2016b.
  116. WHO/Europe (2015) Healthy ageing—risk factors of ill health among older people. Retrieved on December 2015.
  117. WIISEL (2015) Retrieved on December 2015.
  118. Xiang Y, Ping Tang Y, Qing Ma B, Chen Yan H, Jiang J, Yuan Tia X (2015) Remote safety monitoring for elderly persons based on omni-vision analysis. PLoS One 10(5):e0124068CrossRefGoogle Scholar
  119. Yang X, Li Z, Geng Z, Zhang H (2012) A multi-layer security model for internet of things. In: Wang Y et al (eds) Internet of things, volume 312 of communications in computer and information science. Springer, Berlin, pp 388–393Google Scholar
  120. Zamora-Cadenas L, Arrue N, Jimenez-Irastorza A, Velez I (2010) Design of an IR-UWB indoor localization system based on a novel RTT ranging estimator. In: Proceedings of first international conference on sensor device technologies and applications, pp 52–57, 2010Google Scholar
  121. Zecchin C, Facchinetti A, Sparacino G, Cobelli C (2014) Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information. Comput Methods Programs Biomed 113(1):144–152CrossRefGoogle Scholar
  122. Zoidi O, Tefas A, Pitas I (2011) Object tracking based on local steering kernels for drinking activity recognition. In: Proceeding of 33rd international conference on information technology interfaces, pp 237–242, 2011Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Iman Azimi
    • 1
  • Amir M. Rahmani
    • 1
  • Pasi Liljeberg
    • 1
  • Hannu Tenhunen
    • 1
    • 2
  1. 1.Department of Information TechnologyUniversity of TurkuTurkuFinland
  2. 2.Department of Industrial and Medical ElectronicsKTH Royal Institute of TechnologyStockholmSweden

Personalised recommendations