Skip to main content


Log in

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

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript


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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others














  • Agarwal A, Miller J, Eastep J, Wentziaff D, Kasture H (2009) Self-aware computing. Technical report, MIT

    Google Scholar 

  • 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 178–186

  • Akl A, Taati B, Mihailidis A (2015) Autonomous unobtrusive detection of mild cognitive impairment in older adults. IEEE Trans Biomed Eng 62(5):1383–1394

    Article  Google Scholar 

  • ALFRED (2015) ALFRED \(\mid\) personal interactive assistant for independent living and active ageing. Retrieved on December 2015.

  • Andreu-Perez J, Poon CCY, Merrifield RD, Wong STC, Yang G-Z (2015) Big data for health. IEEE J Biomed Health Inform 19(4):1193–1208

    Article  Google Scholar 

  • 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, 2015

  • Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw 54(15):2787–2805

    Article  MATH  Google Scholar 

  • 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, 2016

  • 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, 2014

  • 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, 2012

  • 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, 2013

  • 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, 2012

  • Beyer M (2015) Gartner says solving ’Big Data’ challenge involves more than just managing volumes of data. Retrieved on December 2015.

  • 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):430–439

    Article  Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning. Springer-Verlag New York Inc, New York

    MATH  Google Scholar 

  • 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, 2014

  • 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 

  • 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, 2014

  • 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, 2010

  • 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, 2011

  • 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, 2013

  • 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):164–166

    Google Scholar 

  • 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):11728–11749

  • 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, 2013

  • 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, 2014

  • 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, 2014

  • 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, 2010

  • 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):13–27

    Google Scholar 

  • EDLAH (2015) Retrieved on December 2015.

  • Faetti T, Paradiso R (2012) A novel wearable system for elderly monitoring. Adv Sci Technol 85:17–22

    Article  Google Scholar 

  • 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, 2012

  • GeTVivid (2015) Retrieved on December 2015.

  • 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, 2011

  • Glaeser DH, Thomas LJ Jr (1975) Computer monitoring in patient care. Annu Rev Biophys Bioeng 4:449–476

    Article  Google Scholar 

  • 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):910–923

    Article  Google Scholar 

  • 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 46:100–112

    Article  Google Scholar 

  • 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 36–47

  • 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 56:701–718

    Article  Google Scholar 

  • HEREiAM (2015) Retrieved on December 2015.

  • Hickson M (2006) Malnutrition and ageing. Postgrad Med J 82(963):2–8

    Article  Google Scholar 

  • 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, 2012

  • Igual R, Medrano C, Plaza I (2013) Challenges, issues and trends in fall detection systems. BioMed Eng OnLine 12(1):1–24

    Article  Google Scholar 

  • 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, 2014

  • 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, 2012

  • 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):1968–1979

    Article  Google Scholar 

  • 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):47–65

    Article  Google Scholar 

  • Juang LH, Wu MN (2015) Fall down detection under smart home system. J Med Syst 39(10):1–12

    Article  Google Scholar 

  • Kalian K, Kainz W (2013) ASSISTANT—aiding sustainable independent senior travellers to navigate in towns. In: Proceedings of the European navigation conference, 2013

  • 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):1440001

    Article  Google Scholar 

  • Kasteren TL, Englebienne G, Krose BJ (2010) An activity monitoring system for elderly care using generative and discriminative models. Pers Ubiquit Comput 14(6):489–498

    Article  Google Scholar 

  • Kozina S, Lustrek M, Gams M (2011) Dynamic signal segmentation for activity recognition. In: Proceedings of international joint conference on artificial intelligence, 2011

  • 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, 2012

  • Laney D (2001) 3D data management: controlling data volume, velocity, and variety. Application Delivery Strategies by META Group Inc., Technical report

    Google Scholar 

  • 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):457–466

    Article  Google Scholar 

  • Lee JV, Chuah YD, Chieng KTH (2013) Smart elderly home monitoring system with an android phone. Int J Smart Home 7(3):17–32

    Google Scholar 

  • 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):696–704

    Article  Google Scholar 

  • 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):1324–1331

    Article  Google Scholar 

  • Lustrek M, Kaluza B (2009) Fall detection and activity recognition with machine learning. Informatica 33:205–212

    Google Scholar 

  • 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), 2014

  • 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, 2013

  • 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 179–187

  • 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 460–469

  • 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):814–823

    Article  Google Scholar 

  • 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):722–727

  • Memon M, Wagner SR, Pedersen CF, Beevi FHA, Hansen FO (2014) Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 14(3):4312–4341

    Article  Google Scholar 

  • 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, 2013

  • 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, 2014

  • 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 Syst

  • Morgan RJM, Williams F, Wright MM (1997) An early warning scoring system for detecting developing critical illness. Clin Intens Care 8(2):100

    Google Scholar 

  • 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, 2015

  • 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, 2012

  • Murphy KP (2012) Machine learning: a probabilistic perspective. The MIT Press, Cambridge

    MATH  Google Scholar 

  • 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, 2010

  • Neisse R, Steri G, Fovino IN, Baldini G (2015) SecKit: a model-based security toolkit for the internet of things. Comput Secur 54:60–76

    Article  Google Scholar 

  • NITICS (2015) Retrieved on December 2015.

  • 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):412–423

    Article  Google Scholar 

  • 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, 2015

  • OpenPR (2015)—Press release—Ascora GmbH—Project ALFRED: personal interactive assistant for independent living and active ageing, Retrieved on December 2015.

  • 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, Rockville

  • 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):4544–4553

    Article  Google Scholar 

  • 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 79–83

  • Planinc R, Kampel M (2012a) Introducing the use of depth data for fall detection. Pers Ubiquit Comput 17(6):1063–1072

    Article  Google Scholar 

  • 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 121–132

  • 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, 2014

  • 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 225–229

  • 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):37–45

    Article  Google Scholar 

  • 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, 2015

  • 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, 2015

  • 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, 2014

  • 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 York

    Book  Google Scholar 

  • 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):17–24

    Article  Google Scholar 

  • 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 71–80

  • Russell S, Norvig P (2013) Artificial intelligence: a modern approach, 3rd edn. Pearson, London

    MATH  Google Scholar 

  • 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):230–237

    Article  Google Scholar 

  • 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, 2014

  • 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, 2013

  • 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, 2014

  • Sicari S, Rizzardi A, Grieco LA, Coen-Porisini A (2015) Security, privacy and trust in internet of things: the road ahead. Comput Netw 76:146–164

    Article  Google Scholar 

  • Spinsante S, Gambi E (2012) Remote health monitoring for elderly through interactive television. BioMed Eng OnLine 11:54

    Article  Google Scholar 

  • 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, 2009

  • 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):228–250

    Article  Google Scholar 

  • Stratton R, Green C, Elia M (2003) Disease-related malnutrition: an evidence-based approach to treatment. CABI Publishing Series, CABI Pub., Cambridge

    Google Scholar 

  • 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, 2014

  • 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, 2013

  • 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 2014

  • 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), 2014

  • Touati F, Tabish R (2013) U-healthcare system: state-of-the-art review and challenges. J Med Syst 37:9949

    Article  Google Scholar 

  • 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, 2015

  • TVX2015-Workshop (2015) A workshop on “People, Context, and Devices: Defining the New Landscape of TV Experiences”. Retrieved on December 2015.

  • United Nations (2015) Probabilistic population projections based on the world population prospects: the 2015 revision. Population division, DESA., 2015.

  • 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 32:3–16

    Article  Google Scholar 

  • WHO (2011) Global health and ageing. WHO, Technical report

  • WHO (2014) World health statistics 2014: a wealth of information on global public health. WHO, Technical report

  • WHO (2016a) Nutrition for older persons. Retrieved on April 2016a.

  • WHO (2016b) Falls. Retrieved on April 2016b.

  • WHO/Europe (2015) Healthy ageing—risk factors of ill health among older people. Retrieved on December 2015.

  • WIISEL (2015) Retrieved on December 2015.

  • 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):e0124068

    Article  Google Scholar 

  • 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 388–393

  • 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, 2010

  • 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):144–152

    Article  Google Scholar 

  • 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, 2011

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Iman Azimi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Azimi, I., Rahmani, A.M., Liljeberg, P. et al. Internet of things for remote elderly monitoring: a study from user-centered perspective. J Ambient Intell Human Comput 8, 273–289 (2017).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: