Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 239

The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.

A survey on health monitoring systems for health smart homes

Mshali, Haider, Lemlouma, Tayeb, Moloney, Maria, Magoni, Damien·2018·International Journal of Industrial Ergonomics
DOI 10.1016/j.ergon.2018.02.002
Open this paper in CoChat — free account

Abstract

Abstract Aging population ratios are rising significantly. Health monitoring systems (HMS) in smart environments have evolved rapidly to become a viable alternative to traditional healthcare solutions. The aim of HMS is to not only reduce costs but to also provide timely e-health services to individuals wishing to maintain their independence. In this way, elderly people can avoid, for as long as possible, any interaction with healthcare institutions (e.g. nursing homes and hospitals), which in turn reduces pressure on the health system. To fully realise this vision of seamless e-health services supporting people in need of them, a number of challenges that need further investigation still exist. To this end, we provide an overview of the current state of the art for smart health monitoring systems. We review HMS in smart environments from a general perspective and with a particular focus on systems for the elderly and dependent people. We look at the challenges for these systems from the perspective of developing the technology itself, system requirements, system design and modelling. We present a consolidated picture of the most important functions and services offered by HMS for monitoring and detecting human behaviour including its concepts, approaches, and processing techniques. Moreover, we provide an extensive, in-depth analysis and evaluation of the existing research findings in the area of e-health systems. Finally, we present challenges and open issues facing the smart HMS field and we make recommendations on how to improve future systems.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ergon.2018.02.002

Cites

10 of 10 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 10 references

Research works printed but not linked: 104

  1. Abowd, 2000, Charting past, present, and future research in ubiquitous computing, ACM Trans. Comput. Hum. Interact., 7, 29, 10.1145/344949.344988 · DOI · Paper page
  2. Ahlan, 2014, User acceptance of health information technology (hit) in developing countries: a conceptual model, Procedia Technol., 16, 1287, 10.1016/j.protcy.2014.10.145 · DOI
  3. Alam, 2012, A review of smart homes––past, present, and future, IEEE Trans. Syst. Man Cybern. C Appl. Rev., 42, 1190, 10.1109/TSMCC.2012.2189204 · DOI · Paper page
  4. Alwan, 2006, A smart and passive floor-vibration based fall detector for elderly, vol. 1, 1003
  5. Ameen, 2012, Security and privacy issues in wireless sensor networks for healthcare applications, J. Med. Syst., 36, 93, 10.1007/s10916-010-9449-4 · DOI · Paper page
  6. Ashbrook, 2003, Using gps to learn significant locations and predict movement across multiple users, Personal Ubiquitous Comput., 7, 275, 10.1007/s00779-003-0240-0 · DOI · Paper page
  7. Atallah, 2009, The use of pervasive sensing for behaviour profiling - a survey, Pervasive Mob. Comput., 5, 447, 10.1016/j.pmcj.2009.06.009 · DOI
  8. Bae, 2014, An ontology-based approach to adl recognition in smart homes, Future Generat. Comput. Syst., 33, 32, 10.1016/j.future.2013.04.004 · DOI
  9. Bagga, 2015, Applications of mobile agents in healthcare domain: a literature survey, Int. J. Grid Distrib. Comp., 8, 55, 10.14257/ijgdc.2015.8.5.05 · doi:10.14257/ijgdc.2015.8.5.05
  10. Benharref, 2014, Novel cloud and soa-based framework for e-health monitoring using wireless biosensors, IEEE J. Biomed. Health InfoM., 18, 46, 10.1109/JBHI.2013.2262659 · DOI
  11. Bouchard, 2006, A smart home agent for plan recognition of cognitively-impaired patients, J. Comput., 1, 53
  12. Brdiczka, 2009, Learning situation models in a smart home, IEEE Trans. Syst. Man Cybern. B Cybern., 39, 56, 10.1109/TSMCB.2008.923526 · DOI · Paper page
  13. Bricon-Souf, 2007, Context awareness in health care: a review, Int. J. Med. Inf., 76, 2, 10.1016/j.ijmedinf.2006.01.003 · DOI · Paper page
  14. Brown, 1998, Triggering information by context, Pers. Technol., 2, 18, 10.1007/BF01581843 · doi:10.1007/bf01581843
  15. Brulin, 2012, Posture recognition based on fuzzy logic for home monitoring of the elderly, IEEE Trans. Inf. Technol. Biomed., 16, 974, 10.1109/TITB.2012.2208757 · DOI
  16. Chen, 2011, Body area networks: a survey, J. Mob. Netw. Appl., 16, 171, 10.1007/s11036-010-0260-8 · DOI · Paper page
  17. Chen, 2012, A knowledge-driven approach to activity recognition in smart homes, IEEE Trans. Knowl. Data Eng., 24, 961, 10.1109/TKDE.2011.51 · DOI · Paper page
  18. Chen, 2012, A reliable transmission protocol for zigbee-based wireless patient monitoring, IEEE Trans. Inf. Technol. Biomed., 16, 6, 10.1109/TITB.2011.2171704 · DOI
  19. Chernbumroong, 2013, Elderly activities recognition and classification for applications in assisted living, Expert Syst. Appl., 40, 1662, 10.1016/j.eswa.2012.09.004 · DOI · Paper page
  20. Chernbumroong, 2014, A practical multi-sensor activity recognition system for home-based care, Decis. Support Syst., 66, 61, 10.1016/j.dss.2014.06.005 · DOI
  21. Cook, 2012, Learning setting-generalized activity models for smart spaces, IEEE Intell. Syst., 27, 32, 10.1109/MIS.2010.112 · DOI · Paper page
  22. Cook, 2009, Assessing the quality of activities in a smart environment, Meth. Inf. Med., 48, 480, 10.3414/ME0592 · DOI · Paper page
  23. Corchado, 2010, Using heterogeneous wireless sensor networks in a telemonitoring system for healthcare, IEEE Trans. Inf. Technol. Biomed., 14, 234, 10.1109/TITB.2009.2034369 · doi:10.1109/titb.2009.2034369
  24. Dey, 2001, Understanding and using context, Personal Ubiquitous Comput., 5, 4, 10.1007/s007790170019 · DOI · Paper page
  25. Farella, 2010, Aware and smart environments: the casattenta project, IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng., 41, 697
  26. Fleury, 2010, Svm-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results, IEEE Trans. Inf. Technol. Biomed., 14, 274, 10.1109/TITB.2009.2037317 · DOI · Paper page
  27. Forkan, 2014, Cocamaal: a cloud-oriented context-aware middleware in ambient assisted living, Future Generat. Comput. Syst., 35, 114, 10.1016/j.future.2013.07.009 · DOI
  28. Forkan, 2015, A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living, Pattern Recogn., 48, 628, 10.1016/j.patcog.2014.07.007 · DOI · Paper page
  29. Gao, 2014, Evaluation of accelerometer based multi-sensor versus single-sensor activity recognition systems, Med. Eng. Phys., 36, 779, 10.1016/j.medengphy.2014.02.012 · DOI · Paper page
  30. Garcia-Sancheza, 2013, Deploying intelligent e-health services in a mobile gateway, Expert Syst. Appl., 40, 1231, 10.1016/j.eswa.2012.08.068 · doi:10.1016/j.eswa.2012.08.068
  31. Gellert, 2006, Person movement prediction using hidden markov models, Stud. Inf. Contr., 15, 17
  32. Gurses, 2012, Time to accelerate integration of human factors and ergonomics in patient safety, BMJ Qual. Saf., 21, 347, 10.1136/bmjqs-2011-000421 · DOI · Paper page
  33. Hackmann, 2014, Cyber-physical codesign of distributed structural health monitoring with wireless sensor networks, IEEE Trans. Parallel Distr. Syst., 25, 63, 10.1109/TPDS.2013.30 · doi:10.1109/tpds.2013.30
  34. Halperin, 2008, Security and privacy for implantable medical devices, IEEE Perv. Comput., 7, 30, 10.1109/MPRV.2008.16 · DOI · Paper page
  35. Hawley, 2013, A voice-input voice-output communication aid for people with severe speech impairment, IEEE Trans. Neural Syst. Rehabil. Eng., 21, 23, 10.1109/TNSRE.2012.2209678 · DOI · Paper page
  36. Hignett, 2013, State of science: human factors and ergonomics in healthcare, Ergonomics, 56, 1491, 10.1080/00140139.2013.822932 · DOI
  37. Hong, 2010, Conamsn: a context-aware messenger using dynamic bayesian networks with wearable sensors, Expert Syst. Appl., 37, 4680, 10.1016/j.eswa.2009.12.040 · doi:10.1016/j.eswa.2009.12.040
  38. Hossain, 2014, Perspectives of human factors in designing elderly monitoring system, Comput. Hum. Behav., 33, 63, 10.1016/j.chb.2013.12.010 · DOI
  39. Hristoskova, 2014, Ontology-driven monitoring of patient's vital signs enabling personalized medical detection and alert, J. Sens., 14, 1598
  40. Hsu, 2010, Rfid-based human behavior modeling and anomaly detection for elderly care, Mobile Inf. Syst., 6, 341, 10.1155/2010/460103 · DOI
  41. Hsu, 2011, A novel sensor-assisted rfid-based indoor tracking system for the elderly living alone, J. Sens., 11, 10094
  42. Jiang, 2011, A method to deal with installation errors of wearable accelerometers for human activity recognition, Physiol. Meas., 32, 347, 10.1088/0967-3334/32/3/006 · DOI
  43. Katz, 1963, Studies of illness in the aged, the index of adl: a standardized measure of biological and psychosocial function, J. Am. Med. Assoc., 185, 914, 10.1001/jama.1963.03060120024016 · DOI · Paper page
  44. Kau, 2015, A smart phone-based pocket fall accident detection, positioning, and rescue system, IEEE J. Biomed. Health InfoM., 19, 44, 10.1109/JBHI.2014.2328593 · DOI · Paper page
  45. Khan, 2010, A triaxial accelerometer-based physical-activity recognition via augmented-signal features and a hierarchical recognizer, IEEE Trans. Inf. Technol. Biomed., 14, 1166, 10.1109/TITB.2010.2051955 · DOI · Paper page
  46. Kim, 2010, Human activity recognition and pattern discovery, IEEE Perv. Comput., 9, 48, 10.1109/MPRV.2010.7 · DOI · Paper page
  47. Kim, 2013, Rfid-based indoor location tracking to ensure the safety of the elderly in smart home environments, Personal Ubiquitous Comput., 17, 1699, 10.1007/s00779-012-0604-4 · DOI
  48. Kim, 2016, Coexistence of zigbee-based wban and wifi for health telemonitoring systems, IEEE J. Biomed. Health InfoM., 20, 222, 10.1109/JBHI.2014.2387867 · DOI
  49. Kumar, 2011, Security issues in healthcare applications using wireless medical sensor networks: a survey, J. Sens., 12, 55
  50. Lawton, 1970, Assessment of older people: self maintaining and instrumental activities of daily living, Nurs. Res., 19, 179, 10.1097/00006199-197005000-00029 · DOI · Paper page
  51. Lee, 2009, Wireless sensor network based wearable smart shirt for ubiquitous health and activity monitoring, J. Sens. Actuat.: Chem., 140, 390, 10.1016/j.snb.2009.04.040 · DOI · Paper page
  52. Lee, 2010, Statistical and entropy based multi purpose human motion analysis, vol. 1, 734
  53. Lee, 2011, Survey and trend analysis of context-aware systems, Inform. Int. Interdis. J., 14, 527
  54. Lemlouma, 2013, Smart media services through tv sets for elderly and dependent persons, Springer Lecture Notes of the Institute for Computer Sciences, Soc. Inform. Telecom. Eng., 61, 30
  55. Liao, 2007, Learning and inferring transportation routines, Artif. Intell., 171, 311, 10.1016/j.artint.2007.01.006 · DOI · Paper page
  56. Lin, 2005, Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks, vol. 2, 1569
  57. Lu, 2009, Robust location-aware activity recognition using wireless sensor network in an attentive home, IEEE J. Automat. Sci. Eng., 6, 598, 10.1109/TASE.2009.2021981 · DOI
  58. Mileo, 2010, Support for context-aware monitoring in home healthcare, J. Ambient Intell. Smart Environ., 2, 49, 10.3233/AIS-2010-0052 · DOI
  59. Misic, 2010, Bridge performance in a multitier wireless network for healthcare monitoring, IEEE J. Wireless Commun., 17, 90, 10.1109/MWC.2010.5416355 · doi:10.1109/mwc.2010.5416355
  60. Morsi, 2013, Fuzzy logic in heart rate and blood pressure measuring system, vol. 2013, 113
  61. Mubashir, 2013, A survey on fall detection: principles and approaches, Neurocomputing, 100, 144, 10.1016/j.neucom.2011.09.037 · DOI · Paper page
  62. Nguyen, 2005, Learning and detecting activities from movement trajectories using the hierarchical hidden markov model, vol. 2, 955
  63. Noury, 2012, Computer simulation of the activity of the elderly person living independently in a health smart home, Comput. Meth. Progr. Biomed., 108, 1216, 10.1016/j.cmpb.2012.07.004 · DOI
  64. Omar, 2006, E-health support services based on service-oriented architecture, IT Prof., 8, 35, 10.1109/MITP.2006.32 · doi:10.1109/mitp.2006.32
  65. Park, 2011, Bayesian network-based high-level context recognition for mobile context sharing in cyber-physical system, Int. J. Distributed Sens. Netw., 7, 1
  66. Parkka, 2010, Personalization algorithm for real-time activity recognition using pda, wireless motion bands, and binary decision tree, IEEE Trans. Inf. Technol. Biomed., 14, 1211, 10.1109/TITB.2010.2055060 · DOI
  67. Peetoom, 2015, Literature review on monitoring technologies and their outcomes in independently living elderly people, Disabil. Rehabil. Assist. Technol., 10, 271, 10.3109/17483107.2014.961179 · DOI · Paper page
  68. Poon, 2015, Body sensor networks: in the era of big data and beyond, IEEE Rev. Biomed. Eng., 8, 4, 10.1109/RBME.2015.2427254 · DOI
  69. Pung, 2009, Context-aware middleware for pervasive elderly homecare, IEEE J. Sel. Area. Commun., 27, 510, 10.1109/JSAC.2009.090513 · DOI
  70. Raad, 2009, A ubiquitous smart home for elderly, J. Inform. Syst. Frontiers, 11, 529, 10.1007/s10796-008-9119-y · doi:10.1007/s10796-008-9119-y
  71. Rao, 2004, Predicting inhabitant action using action and task models with application to smart homes, Int. J. Artif. Intell. Tool., 13, 81, 10.1142/S0218213004001533 · DOI
  72. Rashidi, 2013, A survey on ambient-assisted living tools for older adults, IEEE J. Biomed. Health InfoM., 17, 579, 10.1109/JBHI.2012.2234129 · DOI · Paper page
  73. Reeder, 2013, Framing the evidence for health smart homes and home-based consumer health technologies as a public health intervention for independent aging: a systematic review, Int. J. Med. Inf., 82, 565, 10.1016/j.ijmedinf.2013.03.007 · DOI · Paper page
  74. Rialle, 2003, Telemonitoring of patients at home: a software agent approach, Comput. Meth. Progr. Biomed., 72, 257, 10.1016/S0169-2607(02)00161-X · doi:10.1016/s0169-2607(02)00161-x
  75. Riboni, 2011, Cosar: hybrid reasoning for context-aware activity recognition, Personal Ubiquitous Comput., 15, 271, 10.1007/s00779-010-0331-7 · DOI · Paper page
  76. Rivera-Illingworth, 2006, Towards the detection of temporal behavioural patterns in intelligent environments, vol. 1, 119
  77. Robles, 2010, A review on security in smart home development, Int. J. Adv. Sci. Technol., 15, 13
  78. Robles, 2010, Review: context aware tools for smart home development, Int. J. Smart Home, 4, 1
  79. Rodriguez, 2012, Assessing the salsa architecture for developing agent-based ambient computing applications, Sci. Comput. Program., 77, 46, 10.1016/j.scico.2010.12.003 · doi:10.1016/j.scico.2010.12.003
  80. Sacco, 2012, Detection of activities of daily living impairment in alzheimer's disease and mild cognitive impairment using information and communication technology, Clin. Interv. Aging, 2012, 539, 10.2147/CIA.S36297 · DOI
  81. Satterfield, 2012, Application of structural case-based reasoning to activity recognition in smart home environments, vol. 1, 1
  82. Shnayder, 2005, Sensor networks for medical care, vol. 5, 314
  83. Singla, 2010, Recognizing independent and joint activities among multiple residents in smart environments, J. Amb. Intell. Human. Comput., 1, 57, 10.1007/s12652-009-0007-1 · DOI · Paper page
  84. Spinsante, 2012, Remote health monitoring by osgi technology and digital tv integration, IEEE Trans. Consum. Electron., 58, 1434, 10.1109/TCE.2012.6415017 · DOI
  85. Suryadevara, 2012, Wireless sensor network based home monitoring system for wellness determination of elderly, IEEE Sensor. J., 12, 1965, 10.1109/JSEN.2011.2182341 · DOI · Paper page
  86. Suryadevara, 2013, Forecasting the behavior of an elderly using wireless sensors data in a smart home, Eng. Appl. Artif. Intell., 26, 2641, 10.1016/j.engappai.2013.08.004 · DOI · Paper page
  87. Taleb, 2009, Angelah: a framework for assisting elders at home, IEEE J. Sel. Area. Commun., 27, 480, 10.1109/JSAC.2009.090511 · DOI
  88. Tao, 2012, Privacy-preserved behavior analysis and fall detection by an infrared ceiling sensor network, Sensors, 12, 16920, 10.3390/s121216920 · DOI
  89. Tapia, 2004, Activity recognition in the home using simple and ubiquitous sensors, Perv. Comput. Lect. Notes Comput. Sci., 3001, 158
  90. Torabi, 2014, Cross-layer design for prompt and reliable transmissions over body area networks, IEEE J. Biomed. Health InfoM., 18, 1303, 10.1109/JBHI.2013.2283232 · doi:10.1109/jbhi.2013.2283232
  91. Trabelsi, 2013, An unsupervised approach for automatic activity recognition based on hidden markov model regression, IEEE Trans. Autom. Sci. Eng., 10, 829, 10.1109/TASE.2013.2256349 · DOI · Paper page
  92. Triantafyllidis, 2013, A pervasive health system integrating patient monitoring, status logging, and social sharing, IEEE J. Biomed. Health InfoM., 17, 30, 10.1109/TITB.2012.2227269 · DOI
  93. Tseng, 2006, Mining from time series human movement data, vol. 4, 3241
  94. Varkey, 2012, Human motion recognition using a wireless sensor-based wearable system, Personal Ubiquitous Comput., 16, 897, 10.1007/s00779-011-0455-4 · DOI
  95. Varshney, 2008, Improving wireless health monitoring using incentive-based router cooperation, IEEE J. Comp., 41, 56, 10.1109/MC.2008.157 · doi:10.1109/mc.2008.157
  96. Varshney, 2008, A framework for supporting emergency messages in wireless patient monitoring, Decis. Support Syst., 45, 981, 10.1016/j.dss.2008.03.006 · DOI
  97. Wang, 2011, Recognizing multi-user activities using wearable sensors in a smart home, Pervasive Mob. Comput., 7, 287, 10.1016/j.pmcj.2010.11.008 · DOI
  98. Wyffels, 2014, Distributed, signal strength-based indoor localization algorithm for use in healthcare environments, IEEE J. Biomed. Health InfoM., 18, 1887, 10.1109/JBHI.2014.2302840 · doi:10.1109/jbhi.2014.2302840
  99. Ye, 2010, Anomaly event detection in temporal sensor network data of intelligent environments, vol. 7, 414
  100. Yuan, 2014, Context-aware hybrid reasoning framework for pervasive healthcare, Personal Ubiquitous Comput., 18, 865, 10.1007/s00779-013-0696-5 · DOI
  101. Zhang, 2005, Enabling context-aware smart home with semantic web technologies, Int. J. Human-friendly Welf. Robot. Syst., 6, 12
  102. Zhang, 2013, Survey on context-awareness in ubiquitous media, Multimed. Tool. Appl., 67, 179, 10.1007/s11042-011-0940-9 · DOI
  103. Zhang, 2015, Security and privacy for mobile healthcare networks: from a quality of protection perspective, IEEE Wireless Commun., 22, 104, 10.1109/MWC.2015.7224734 · DOI · Paper page
  104. Zou, 2015, An approach of reliable data transmission with random redundancy for wireless sensors in structural health monitoring, IEEE Sensor. J., 15, 809, 10.1109/JSEN.2014.2352612 · DOI

Cited by

See all 239

Metadata

Authors
Mshali, H., Lemlouma, T., Moloney, M., & Magoni, D.
Venue
International Journal of Industrial Ergonomics
Publisher
Elsevier BV
Published
2018-07
Volume
66
Pages
26-56
ISSN
0169-8141 (ISSN-L)
DOI
10.1016/j.ergon.2018.02.002
OpenAlex
W2789150890
Cited by
239
References
10
FWCI
13.96
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A survey on health monitoring systems for health smart homes. https://app.cochat.ai/papers/10.1016/j.ergon.2018.02.002
The paper
Mshali, H., Lemlouma, T., Moloney, M., & Magoni, D. (2018). A survey on health monitoring systems for health smart homes. International Journal of Industrial Ergonomics. https://doi.org/10.1016/j.ergon.2018.02.002

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.