CoChat
Sign Up
Cited by 166Context-Aware Activity Recognition SystemsTechnology Use by Older AdultsWireless Sensor Networks for Data Analysis

Ambient Sensors for Elderly Care and Independent Living: A Survey

Open access
Uddin, Md. Zia, Khaksar, Weria, Torresen, Jim·2018·Sensors
DOI 10.3390/s18072027PMC PMC6068532
Open this paper in CoChat — free accountOpen PDF

Abstract

Elderly care at home is a matter of great concern if the elderly live alone, since unforeseen circumstances might occur that affect their well-being. Technologies that assist the elderly in independent living are essential for enhancing care in a cost-effective and reliable manner. Elderly care applications often demand real-time observation of the environment and the resident’s activities using an event-driven system. As an emerging area of research and development, it is necessary to explore the approaches of the elderly care system in the literature to identify current practices for future research directions. Therefore, this work is aimed at a comprehensive survey of non-wearable (i.e., ambient) sensors for various elderly care systems. This research work is an effort to obtain insight into different types of ambient-sensor-based elderly monitoring technologies in the home. With the aim of adopting these technologies, research works, and their outcomes are reported. Publications have been included in this survey if they reported mostly ambient sensor-based monitoring technologies that detect elderly events (e.g., activities of daily living and falls) with the aim of facilitating independent living. Mostly, different types of non-contact sensor technologies were identified, such as motion, pressure, video, object contact, and sound sensors. Besides, multicomponent technologies (i.e., combinations of ambient sensors with wearable sensors) and smart technologies were identified. In addition to room-mounted ambient sensors, sensors in robot-based elderly care works are also reported. Research that is related to the use of elderly behavior monitoring technologies is widespread, but it is still in its infancy and consists mostly of limited-scale studies. Elderly behavior monitoring technology is a promising field, especially for long-term elderly care. However, monitoring technologies should be taken to the next level with more detailed studies that evaluate and demonstrate their potential to contribute to prolonging the independent living of elderly people.

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 · 10.3390/s18072027

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

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

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

Cites

Flagged first, then by citations

Research works printed but not linked: 153

  1. [2]Duque G. Age-Related Physical and Physiologic Changes and Comorbidities in Older People: Association with Falls Medication-Related Falls in Older People Huang A.R. Mallet L. Springer Berlin, Germany 2016 Chapter 6 67 73
  2. [4]Singh M.A.F. Exercise, nutrition and managing hip fracture in older persons Current Opin. Clin. Nutr. Metab. Care 2013 17 12 24 24281377
  3. [5]Alwan M. Dalal S. Mack D. Kell S. Turner B. Leachtenauer J. Felder R. Impact of Monitoring Technology in Assisted Living: Outcome Pilot IEEE Trans. Inf. Technol. Biomed. 2006 10 192 198 10.1109/TITB.2005.855552 16445264 · DOI
  4. [6]Scanaill C.N. Carew S. Barralon P. Noury N. Lyons D. Lyons G.M. A Review of Approaches to Mobility Telemonitoring of the Elderly in Their Living Environment Ann. Biomed. Eng. 2006 34 547 563 10.1007/s10439-005-9068-2 16550450 · DOI
  5. [7]Perry M. Dowdall A. Lines L. Hone K. Multimodal and ubiquitous computing systems: Supporting independent-living older users IEEE Trans. Inf. Technol. Biomed. 2004 8 258 270 10.1109/TITB.2004.835533 15484431 · DOI
  6. [8]Al-Shaqi R. Mourshed M. Rezgui Y. Progress in ambient assisted systems for independent living by the elderly SpringerPlus 2016 5 10.1186/s40064-016-2272-8 27330890 PMC4870543 · DOI
  7. [9]Ni Q. Hernando A.G. de la Cruz I. The Elderly’s Independent Living in Smart Homes: A Characterization of Activities and Sensing Infrastructure Survey to Facilitate Services Development Sensors 2015 15 11312 11362 10.3390/s150511312 26007717 PMC4481963 · DOI
  8. [10]Alam M.R. Reaz M.B.I. Ali M.A.M. A Review of Smart Homes—Past, Present, and Future IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2012 42 1190 1203 10.1109/TSMCC.2012.2189204 · DOI
  9. [11]Rashidi P. Mihailidis A. A Survey on Ambient-Assisted Living Tools for Older Adults IEEE J. Biomed. Health Inf. 2013 17 579 590 10.1109/JBHI.2012.2234129 24592460 · DOI · Paper page
  10. [12]Salih A.S.M. Abraham A. A review of ambient intelligence assisted healthcare monitoring Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 2013 5 741 750
  11. [13]Peetoom K.K.B. Lexis M.A.S. Joore M. Dirksen C.D. de Witte L.P. Literature review on monitoring technologies and their outcomes in independently living elderly people Disabil. Rehabil. Assist. Technol. 2014 10 271 294 10.3109/17483107.2014.961179 25252024 · DOI
  12. [14]Khusainov R. Azzi D. Achumba I. Bersch S. Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations Sensors 2013 13 12852 12902 10.3390/s131012852 24072027 PMC3859040 · DOI
  13. [15]Avci A. Bosch S. Marin-Perianu M. Marin-Perianu R. Havinga P. Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey Proceedings of the 23th International Conference on Architecture of Computing Systems Hannover, Germany 22–23 Feburary 2010 1 10
  14. [16]Bulling A. Blanke U. Schiele B. A tutorial on human activity recognition using body-worn inertial sensors ACM Comput. Surv. 2014 46 1 33 10.1145/2499621 · DOI · Paper page
  15. [17]Pantelopoulos A. Bourbakis N.G. A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2010 40 1 12 10.1109/TSMCC.2009.2032660 · DOI · Paper page
  16. [18]Acampora G. Cook D.J. Rashidi P. Vasilakos A.V. A Survey on Ambient Intelligence in Healthcare Proc. IEEE 2013 101 2470 2494 10.1109/JPROC.2013.2262913 24431472 PMC3890262 · DOI
  17. [19]Helal S. Mann W. El-Zabadani H. King J. Kaddoura Y. Jansen E. The Gator Tech Smart House: A programmable pervasive space Computer 2005 38 50 60 10.1109/MC.2005.107 · DOI
  18. [20]Cook D.J. Crandall A.S. Thomas B.L. Krishnan N.C. CASAS: A Smart Home in a Box Computer 2013 46 62 69 10.1109/MC.2012.328 24415794 PMC3886862 · DOI
  19. [21]Vacher M. Lecouteux B. Istrate D. Joubert T. Portet F. Sehili M. Chahuara P. Experimental Evaluation of Speech Recognition Technologies for Voice-based Home Automation Control in a Smart Home Proceedings of the 4th workshop on speech and language processing for assistive technologies Grenoble, France 21–22 August 2013 99 105
  20. [22]Chahuara P. Portet F. Vacher M. Making Context Aware Decision from Uncertain Information in a Smart Home: A Markov Logic Network Approach Proceedings of the 4th International Joint Conference Dublin, Ireland 3–5 December 2013 78 93
  21. [23]Antoniou P.E. Konstantinidis E.I. Billis A.S. Bamidis P.D. Integrating the USEFIL Assisted Living Platform; Observation from the Field Proceedings of the 6th European Conference of the International Federation for Medical and Biological Engineering Dubrovnik, Croatia 7–11 September 2014 657 660
  22. [24]Billis A.S. Papageorgiou E.I. Frantzidis C. Konstantinidis E.I. Bamidis P.D. Towards a hierarchically-structured decision support tool for improving seniors’ independent living: The USEFIL decision support system Proceedings of the 6th International Conference on Pervasive Technologies Related to Assistive Environments Rhodes, Greece 29–31 May 2013 1 4
  23. [25]Zhang Q. Su Y. Yu P. Assisting an Elderly with Early Dementia Using Wireless Sensors Data in Smarter Safer Home Proceedings of the 15th IFIP WG 8.1 International Conference on Informatics and Semiotics in Organisations Shanghai, China 23–24 May 2014 398 404
  24. [29]De Hert P. Gutwirth S. Moscibroda A. Wright D. Fuster G.G. Legal safeguards for privacy and data protection in ambient intelligence Pers. Ubiquitous Comput. 2008 13 435 444 10.1007/s00779-008-0211-6 · DOI
  25. [30]Rouvroy A. Poullet Y. The Right to Informational Self-Determination and the Value of Self-Development: Reassessing the Importance of Privacy for Democracy Reinventing Data Protection? Springer Berlin, Germany 2009 45 76
  26. [31]Winn J.K. Technical Standards as Data Protection Regulation Reinventing Data Protection? Springer Berlin, Germany 2009 191 206
  27. [32]Alwan M. Leachtenauer J. Dalal S. Kell S. Turner B. Mack D. Felder R. Validation of rule-based inference of selected independent activities of daily living Telemed. e-Health 2005 11 594 599 10.1089/tmj.2005.11.594 16250824 · DOI
  28. [33]Austin D. Hayes T.L. Kaye J. Mattek N. Pavel M. On the disambiguation of passively measured in-home gait velocities from multi-person smart homes J. Ambient. Intell. Smart Environ. 2011 3 165 174 21572911 PMC3093199
  29. [34]Austin D. Hayes T.L. Kaye J. Mattek N. Pavel M. Unobtrusive monitoring of the longitudinal evolution of in-home gait velocity data with applications to elder care Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 6495 6498 PMC3402166 22255826
  30. [35]Barger T.S. Brown D.E. Alwan M. Health-Status Monitoring Through Analysis of Behavioral Patterns IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2005 35 22 27 10.1109/TSMCA.2004.838474 · DOI
  31. [36]Celler B.G. Earnshaw W. Ilsar E.D. Betbeder-Matibet L. Harris M.F. Clark R. Hesketh T. Lovell N.H. Remote monitoring of health status of the elderly at home. A multidisciplinary project on aging at the University of New South Wales Int. J. Bio-Med Comput. 1995 40 147 155 10.1016/0020-7101(95)01139-6 8847123 · DOI
  32. [37]Cook D.J. Schmitter-Edgecombe M. Assessing the Quality of Activities in a Smart Environment Methods Inf. Med. 2009 48 480 485 10.3414/ME0592 19448886 PMC2759863 · DOI
  33. [38]Dalai S. Alwan M. Seifrafi R. Kell S. Brown D. A Rule-Based Approach to the Analysis of Elders’ Activity Data: Detection of Health and Possible Emergency Conditions Proceedings of the AAAI Fall 2005 Symposium Arlington, VA, USA 4–6 November 2005 2545 2552
  34. [39]Demongeot J. Virone G. Duchêne F. Benchetrit G. Hervé T. Noury N. Rialle V. Multi-sensors acquisition, data fusion, knowledge mining and alarm triggering in health smart homes for elderly people Comptes Rendus Boil. 2002 325 673 682 10.1016/S1631-0691(02)01480-4 12360854 · DOI
  35. [40]Fernández-Luque F.J. Zapata J. Ruiz R. A system for ubiquitous fall monitoring at home via a wireless sensor network Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Buenos Aires, Argentina 31 August–4 September 2010 2246 2249 21096580
  36. [41]Franco C. Demongeot J. Villemazet C. Vuillerme N. Behavioral Telemonitoring of the Elderly at Home: Detection of Nycthemeral Rhythms Drifts from Location Data Proceedings of the IEEE 24th International Conference on Advanced Information Networking and Applications Workshops Perth, WA, Australia 20–23 April 2010 759 766
  37. [43]Glascock A.P. Kutzik D.M. Behavioral Telemedicine: A New Approach to the Continuous Nonintrusive Monitoring of Activities of Daily Living Telemed. J. 2000 6 33 44 10.1089/107830200311833 · DOI
  38. [44]Hagler S. Austin D. Hayes T.L. Kaye J. Pavel M. Unobtrusive and Ubiquitous In-Home Monitoring: A Methodology for Continuous Assessment of Gait Velocity in Elders IEEE Trans. Biomed. Eng. 2010 57 813 820 10.1109/TBME.2009.2036732 19932989 PMC2895023 · DOI
  39. [45]Hayes T.L. Pavel M. Kaye J.A. An unobtrusive in-home monitoring system for detection of key motor changes preceding cognitive decline Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society San Francisco, CA, USA 1–5 September 2004 2480 2483 17270775
  40. [47]Kaushik A.R. Lovell N.H. Celler B.G. Evaluation of PIR Detector Characteristics for Monitoring Occupancy Patterns of Elderly People Living Alone at Home Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Lyon, France 22–26 August 2007 3802 3805 18002826
  41. [48]Kaye J. Intelligent systems for assessment of aging changes (ISAAC): Deploying unobtrusive home-based technology Gerontechnology 2010 9 121 10.4017/gt.2010.09.02.107.00 · DOI
  42. [49]Lee S.-W. Kim Y.-J. Lee G.-S. Cho B.-Q. Lee N.-H. A remote behavioral monitoring system for elders living alone Proceedings of the International Conference on Control, Automation and Systems Seoul, South Korea 17–20 October 2007 2725 2730
  43. [50]Noury N. Hadidi T. Computer simulation of the activity of the elderly person living independently in a Health Smart Home Comput. Methods Programs Biomed. 2012 108 1216 1228 10.1016/j.cmpb.2012.07.004 22981436 · DOI
  44. [51]Shin J.H. Lee B. Park K.S. Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description IEEE Trans. Inf. Technol. Biomed. 2011 15 438 448 10.1109/TITB.2011.2113352 21317086 · DOI
  45. [52]Tomita M.R. Mann W.C. Stanton K. Tomita A.D. Sundar V. Use of Currently Available Smart Home Technology by Frail Elders Top. Geriatr. Rehabilit. 2007 23 24 34 10.1097/00013614-200701000-00005 · DOI
  46. [53]Virone G. Assessing everyday life behavioral rhythms for the older generation Pervasive Mob. Comput. 2009 5 606 622 10.1016/j.pmcj.2009.06.008 · DOI
  47. [54]Wang S. Skubic M. Zhu Y. Activity Density Map Visualization and Dissimilarity Comparison for Eldercare Monitoring IEEE Trans. Inf. Technol. Biomed. 2012 16 607 614 10.1109/TITB.2012.2196439 22547460 · DOI
  48. [56]Abidine M. Fergani B. News Schemes for Activity Recognition Systems Using PCA-WSVM, ICA-WSVM, and LDA-WSVM Information 2015 6 505 521 10.3390/info6030505 · DOI
  49. [58]Auvinet E. Reveret L. St-Arnaud A. Rousseau J. Meunier J. Fall detection using multiple cameras Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, BC, Canada 20–25 August 2008 2554 2557 19163224
  50. [59]Auvinet E. Multon F. Saint-Arnaud A. Rousseau J. Meunier J. Fall Detection with Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution IEEE Trans. Inf. Technol. Biomed. 2011 15 290 300 10.1109/TITB.2010.2087385 20952341 · DOI
  51. [60]Belshaw M. Taati B. Giesbrecht D. Mihailidis A. Intelligent vision-based fall detection system: Preliminary results from a realworld deployment Rehabil. Eng. Assist. Technol. Soc. N. Am. 2011 1 4 10.1155/2017/9474806 · DOI · Paper page
  52. [61]Belshaw M. Taati B. Snoek J. Mihailidis A. Towards a single sensor passive solution for automated fall detection Proceedings of the 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 1773 1776 PMC3465367 22254671
  53. [62]Berlin S.J. John M. Human interaction recognition through deep learning network Proceedings of the IEEE International Carnahan Conference on Security Technology (ICCST) Orlando, FL, USA 24–27 October 2016 1 4
  54. [63]Brulin D. Benezeth Y. Courtial E. Posture Recognition Based on Fuzzy Logic for Home Monitoring of the Elderly IEEE Trans. Inf. Technol. Biomed. 2012 16 974 982 10.1109/TITB.2012.2208757 22997188 · DOI
  55. [64]Chen H. Wang G. Xue J.H. He L. A novel hierarchical framework for human action recognition Pattern Recognit. 2016 55 148 159 10.1016/j.patcog.2016.01.020 · DOI
  56. [65]Lin C.W. Ling Z.H. Automatic Fall Incident Detection in Compressed Video for Intelligent Homecare Proceedings of the 16th International Conference on Computer Communications and Networks Honolulu, HI, USA 13–16 August 2007 1172 1177
  57. [66]Du Y. Wang W. Wang L. Hierarchical recurrent neural network for skeleton based action recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston, MA, USA 8–10 June 2015 1110 1118
  58. [67]Foroughi H. Aski B.S. Pourreza H. Intelligent video surveillance for monitoring fall detection of elderly in home environments Proceedings of the 11th International Conference on Computer and Information Technology Khulna, Bangladesh 24–27 December 2008 219 224
  59. [68]Huang Z. Wan C. Probst T. Gool L.V. Deep Learning on Lie Groups for Skeleton-Based Action Recognition arXiv Prepr Cornell University Library Ithaca, NY, USA 2016
  60. [69]Kreković M. Čerić P. Dominko T. Ilijaš M. Ivančić K. Skolan V. Šarlija J. A method for real-time detection of human fall from video Proceedings of the 35th International Convention MIPRO Opatija, Croatia 21–25 May 2012 1709 1712
  61. [70]Lan Z. Lin M. Li X. Hauptmann A.G. Raj B. Beyond gaussian pyramid: Multi-skip feature stacking for action recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston, MA, USA 8–10 June 2015 204 212
  62. [71]Li Y. Li W. Mahadevan V. Vasconcelos N. Vlad3: Encoding dynamics of deep features for action recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Las Vegas, NV, USA 26 June–1 July 2016 1951 1960
  63. [72]Li Y. Ho K.C. Popescu M. A Microphone Array System for Automatic Fall Detection IEEE Trans. Biomed. Eng. 2012 59 1291 1301 22532430
  64. [73]Lee Y. Kim J. Son M. Lee M. Implementation of Accelerometer Sensor Module and Fall Detection Monitoring System based on Wireless Sensor Network Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Lyon, France 22–26 August 2007 2315 2318 18002455
  65. [74]Lee T. Mihailidis A. An intelligent emergency response system: Preliminary development and testing of automated fall detection J. Telemed. Telecare 2005 11 194 198 10.1258/1357633054068946 15969795 · DOI
  66. [75]Lee Y.-S. Chung W.-Y. Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications Sensors 2012 12 573 584 10.3390/s120100573 22368486 PMC3279230 · DOI
  67. [76]Leone A. Diraco G. Siciliano P. Detecting falls with 3D range camera in ambient assisted living applications: A preliminary study Med. Eng. Phys. 2011 33 770 781 10.1016/j.medengphy.2011.02.001 21382737 · DOI
  68. [77]Mirmahboub B. Samavi S. Karimi N. Shirani S. Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area IEEE Trans. Biomed. Eng. 2013 60 427 436 10.1109/TBME.2012.2228262 23192468 · DOI
  69. [78]Mo L. Li F. Zhu Y. Huang A. Human physical activity recognition based on computer vision with deep learning model Proceedings of the IEEE International Instrumentation and Measurement Technology Conference Proceedings Taipei, Taiwan 23–26 May 2016 1 6
  70. [79]Peng X. Wang L. Wang X. Qiao Y. Bag of Visual Words and Fusion Methods for Action Recognition: Comprehensive Study and Good Practice Computer Vision and Image Understanding Elsevier Amsterdam, The Netherlands 2016 Volume 150 109 125
  71. [80]Peng X. Zou C. Qiao Y. Peng Q. Action recognition with stacked fisher vectors Computer Vision–Asian Conference on Computer Vision–ECCV Springer Berlin, Germany 2014 581 595
  72. [81]Rougier C. Meunier J. St-Arnaud A. Rousseau J. Fall Detection from Human Shape and Motion History Using Video Surveillance Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07) Niagara Falls, ON, Canada 21–23 May 2007 875 880
  73. [82]Shahroudy A. Ng T.T. Yang Q. Wang G. Multimodal multipart learning for action recognition in depth videos IEEE Trans. Pattern Anal. Mach. Intell. 2016 38 2123 2129 10.1109/TPAMI.2015.2505295 26660700 · DOI
  74. [83]Shi Y. Tian Y. Wang Y. Huang T. Sequential deep trajectory descriptor for action recognition with three-stream CNN IEEE Trans. Multimed. 2017 19 1510 1520 10.1109/TMM.2017.2666540 · DOI
  75. [84]Shieh W.-Y. Huang J.-C. Falling-incident detection and throughput enhancement in a multi-camera video-surveillance system Med. Eng. Phys. 2012 34 954 963 10.1016/j.medengphy.2011.10.016 22154761 · DOI
  76. [85]Simonyan K. Zisserman A. Two-stream convolutional networks for action recognition in videos Advances in Neural Information Processing Systems MIT Press Cambridge, MA, USA 2014 568 576
  77. [86]Uddin M.Z. Human activity recognition using segmented body part and body joint features with hidden Markov models Multimed. Tools Appl. 2016 76 13585 13614 10.1007/s11042-016-3742-2 · DOI
  78. [87]Uddin M.Z. Khaksar W. Torresen J. A robust gait recognition system using spatiotemporal features and deep learning Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Daegu, Korea 16–18 November 2017 156 161
  79. [88]Uddin M.Z. Khaksar W. Torresen J. Human activity recognition using robust spatiotemporal features and convolutional neural network Proceedings of the IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) Daegu, Korea 16–18 November 2017 144 149
  80. [89]Veeriah V. Zhuang N. Qi G.J. Differential recurrent neural networks for action recognition Proceedings of the IEEE International Conference on Computer Vision (ICCV) Santiago, Chile 11–18 December 2015 4041 4049
  81. [90]Wang J. Liu Z. Wu Y. Yuan J. Learning actionlet ensemble for 3D human action recognition IEEE Trans. Pattern Anal. Mach. Intell. 2014 36 914 927 10.1109/TPAMI.2013.198 26353226 · DOI
  82. [91]Wang P. Li W. Gao Z. Zhang J. Tang C. Ogunbona P.O. Action recognition from depth maps using deep convolutional neural networks IEEE Trans. Hum. Mach. Syst. 2016 46 498 509 10.1109/THMS.2015.2504550 · DOI
  83. [92]Wang P. Li W. Gao Z. Tang C. Zhang J. Ogunbona P. ConvNets-based action recognition from depth maps through virtual cameras and Pseudocoloring Proceedings of the 23rd ACM International Conference on Multimedia Brisbane, Australia 26–30 October 2015 1119 1122
  84. [94]Willems J. Debard G. Vanrumste B. Goedemé T. A Video-based Algorithm for Elderly Fall Detection Proceedings of the World Congress on Medical Physics and Biomedical Engineering Munich, Germany 7–12 September 2009 312 315
  85. [95]Yang X. Tian Y. Super normal vector for human activity recognition with depth cameras IEEE Trans. Pattern Anal. Mach. Intell. 2017 39 1028 1039 10.1109/TPAMI.2016.2565479 28113701 · DOI · Paper page
  86. [96]Yu M. Rhuma A. Naqvi S.M. Wang L. Chambers J. A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment IEEE Trans. Inf. Technol. Biomed. 2012 16 1274 1286 22922730
  87. [97]Zhen X. Shao L. Action recognition via spatio-temporal local features: A comprehensive study Image Vis. Comput. 2016 50 1 13 10.1016/j.imavis.2016.02.006 · DOI
  88. [98]Zhu W. Lan C. Xing J. Zeng W. Li Y. Shen L. Xie X. Co-Occurrence Feature Learning for Skeleton Based Action Recognition Using Regularized Deep LSTM Networks AAAI 2016 2 8
  89. [99]Arcelus A. Herry C.L. Goubran R.A. Knoefel F. Sveistrup H. Bilodeau M. Determination of Sit-to-Stand Transfer Duration Using Bed and Floor Pressure Sequences IEEE Trans. Biomed. Eng. 2009 56 2485 2492 10.1109/TBME.2009.2026733 19622434 · DOI
  90. [100]Arcelus A. Holtzman M. Goubran R. Sveistrup H. Guitard P. Knoefel F. Analysis of commode grab bar usage for the monitoring of older adults in the smart home environment Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Minneapolis, MN, USA 3–6 September 2009 6155 6158 19965075
  91. [101]Arcelus A. Goubran R. Sveistrup H. Bilodeau M. Knoefel F. Context-aware smart home monitoring through pressure measurement sequences Proceedings of the IEEE International Workshop on Medical Measurements and Applications Ottawa, ON, Canada 30 April–1 May 2010 32 37
  92. [102]Fleury A. Noury N. Vacher M. Glasson H. Seri J.-F. Sound and speech detection and classification in a Health Smart Home Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, BC, Canada 20–25 August 2008 4644 4647 19163751
  93. [103]Khan M.S. Yu M. Feng P. Wang L. Chambers J. An unsupervised acoustic fall detection system using source separation for sound interference suppression Signal Process. 2015 110 199 210 10.1016/j.sigpro.2014.08.021 · DOI
  94. [104]Li Y. Zeng Z. Popescu M. Ho K.C. Acoustic fall detection using a circular microphone array Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Buenos Aires, Argentina 31 August–4 September 2010 2242 2245 21096795
  95. [105]Li Y. Popescu M. Ho K.C. Nabelek D.P. Improving acoustic fall recognition by adaptive signal windowing Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 7589 7592 22256095
  96. [106]Popescu M. Li Y. Skubic M. Rantz M. An acoustic fall detector system that uses sound height information to reduce the false alarm rate Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, BC, Canada 20–25 August 2008 4628 4631 19163747
  97. [107]Popescu M. Mahnot A. Acoustic fall detection using one-class classifiers Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Minneapolis, MN, USA 3–6 September 2009 19964801
  98. [108]Vacher M. Istrate D. Portet F. Joubert T. Chevalier T. Smidtas S. Meillon B. Lecouteux B. Sehili M. Chahuara P. The sweet-home project: Audio technology in smart homes to improve well-being and reliance Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 5291 5294 22255532
  99. [109]Zhuang X. Huang J. Potamianos G. Hasegawa-Johnson M. Acoustic fall detection using Gaussian mixture models and GMM supervectors Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing Taipei, Taiwan 19–24 April 2009 69 72
  100. [110]Alwan M. Rajendran P.J. Kell S. Mack D. Dalal S. Wolfe M. Felder R. A Smart and Passive Floor-Vibration Based Fall Detector for Elderly Proceedings of the 2nd International Conference on Information & Communication Technologies Damascus, Syria 24–28 April 2006 1003 1007
  101. [111]Lombardi M. Vezzani R. Cucchiara R. Detection of Human Movements with Pressure Floor Sensors Image Analysis and Processing—ICIAP Murino V. Puppo E. Springer Berlin, Germany 2015 620 630
  102. [112]Serra R. di Croce P. Peres R. Knittel D. Human step detection from a piezoelectric polymer floor sensor using normalization algorithms Proceedings of the IEEE SENSORS Valencia, Spain 2–5 November 2014 1169 1172
  103. [113]Forouzanfar M. Mabrouk M. Rajan S. Bolic M. Dajani H.R. Groza V.Z. Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar IEEE Trans. Biomed. Eng. 2017 64 479 491 10.1109/TBME.2016.2566619 27187940 · DOI
  104. [114]Kim Y. Toomajian B. Hand Gesture Recognition Using Micro-Doppler Signatures with Convolutional Neural Network IEEE Access 2016 4 7125 7130 10.1109/ACCESS.2016.2617282 · DOI
  105. [115]Lien J. Gillian N. Karagozler M.E. Amihood P. Schwesig C. Olson E. Raja H. Poupyrev I. Soli ACM Trans. Graph. 2016 35 1 19 10.1145/2897824.2925953 · DOI · Paper page
  106. [116]Rui L. Chen S. Ho K.C. Rantz M. Skubic M. Estimation of human walking speed by Doppler radar for elderly care J. Ambient. Intell. Smart Environ. 2017 9 181 191 10.3233/AIS-170425 · DOI
  107. [117]Wan Q. Li Y. Li C. Pal R. Gesture recognition for smart home applications using portable radar sensors Proceedings of the 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Chicago, IL, USA 26–30 August 2014 6414 6417 25571464
  108. [118]Alwan M. Leachtenauer J. Dalal S. Mack D. Kell S. Turner B. Felder R. Psychosocial Impact of Monitoring Technology in Assisted Living: A Pilot Study Proceedings of the 2nd International Conference on Information & Communication Technologies Damascus, Syria 24–28 April 2006 998 1002
  109. [119]Alwan M. Kell S. Turner B. Dalal S. Mack D. Felder R. Psychosocial Impact of Passive Health Status Monitoring on Informal Caregivers and Older Adults Living in Independent Senior Housing Proceedings of the 2nd International Conference on Information & Communication Technologies Damascus, Syria 24–28 April 2006 808 813
  110. [120]Alwan M. Sifferlin E.B. Turner B. Kell S. Brower P. Mack D.C. Dalal S. Felder R.A. Impact of Passive Health Status Monitoring to Care Providers and Payers in Assisted Living Telemed. e-Health 2007 13 279 285 10.1089/tmj.2006.0056 17603830 · DOI
  111. [121]Ariani A. Redmond S.J. Chang D. Lovell N.H. Simulated Unobtrusive Falls Detection with Multiple Persons IEEE Trans. Biomed. Eng. 2012 59 3185 3196 10.1109/TBME.2012.2209645 22835529 · DOI
  112. [122]Bamis A. Lymberopoulos D. Teixeira T. Savvides A. Towards precision monitoring of elders for providing assistive services Proceedings of the 1st ACM international conference on PErvasive Technologies Related to Assistive Environments—PETRA’08 Athens, Greece 16–18 July 2008 1 8
  113. [123]Bamis A. Lymberopoulos D. Teixeira T. Savvides A. The BehaviorScope framework for enabling ambient assisted living Pers. Ubiquitous Comput. 2010 14 473 487 10.1007/s00779-010-0282-z · DOI
  114. [124]Celler B.G. Ilsar E.D. Earnshaw W. Preliminary results of a pilot project on remote monitoring of functional health status in the home Proceedings of the 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Amsterdam, The Netherlands 31 October–3 November 1996 63 64
  115. [125]Chung K. Song K. Shin K. Sohn J. Cho S. Chang J.-H. Noncontact Sleep Study by Multi-Modal Sensor Fusion Sensors 2017 17 1685 10.3390/s17071685 28753994 PMC5539697 · DOI
  116. [126]Guettari T. Aguilar P.A.C. Boudy J. Medjahed H. Istrate D. Baldinger J.-L. Belfeki I. Opitz M. Maly-Persy M. Multimodal localization in the context of a medical telemonitoring system Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Buenos Aires, Argentina 31 August–4 September 2010 3835 3838 21097064
  117. [127]Kinney J.M. Striving to Provide Safety Assistance for Families of Elders: The SAFE House Project Dementia 2004 3 351 370 10.1177/1471301204045165 · DOI
  118. [128]Lotfi A. Langensiepen C. Mahmoud S.M. Akhlaghinia M.J. Smart homes for the elderly dementia sufferers: Identification and prediction of abnormal behaviour J. Ambient. Intell. Humaniz. Comput. 2011 3 205 218 10.1007/s12652-010-0043-x · DOI
  119. [129]Rantz M. Skubic M. Miller S. Krampe J. Using Technology to Enhance Aging in Place Proceedings of the International Conference on Smart Homes and Health Telematics Singapore 10–12 July 2018 169 176
  120. [130]Van Hoof J. Kort H.S.M. Rutten P.G.S. Duijnstee M.S.H. Ageing-in-place with the use of ambient intelligence technology: Perspectives of older users Int. J. Med. Inf. 2011 80 310 331 10.1016/j.ijmedinf.2011.02.010 21439898 · DOI
  121. [131]Zhou F. Jiao J.R. Chen S. Zhang D. A Case-Driven Ambient Intelligence System for Elderly in-Home Assistance Applications IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 2011 41 179 189 10.1109/TSMCC.2010.2052456 · DOI
  122. [132]Zouba N. Bremond F. Thonnat M. Anfosso A. Pascual É. Malléa P. Mailland V. Guerin O. A computer system to monitor older adults at home: Preliminary results Gerontechnology 2009 8 129 139 10.4017/gt.2009.08.03.011.00 · DOI
  123. [133]Zouba N. Bremond F. Thonnat M. Multisensor Fusion for Monitoring Elderly Activities at Home Proceedings of the Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance Genova, Italy 2–4 September 2009 98 103
  124. [134]Aghajan H. Augusto J.C. Wu C. McCullagh P. Walkden J.-A. Distributed vision-based accident management for assisted living Proceedings of the 5th International Conference on Smart Homes and Health Telematics Nara, Japan 21–23 June 2007
  125. [135]Bang S. Kim M. Song S. Park S.-J. Toward real time detection of the basic living activity in home using a wearable sensor and smart home sensors Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vancouver, BC, Canada 20–25 August 2008 5200 5203 19163889
  126. [136]Bianchi F. Redmond S.J. Narayanan M.R. Cerutti S. Celler B.G. Lovell N.H. Falls event detection using triaxial accelerometry and barometric pressure measurement Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Minneapolis, MN, USA 3–6 September 2009 6111 6114 19965262
  127. [137]Cao Y. Tao L. Xu G. An Event-driven Context Model in Elderly Health Monitoring Proceedings of the Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing Brisbane, Australia 7–9 July 2009 120 124
  128. [138]Hein A. Winkelbach S. Martens B. Wilken O. Eichelberg M. Spehr J. Gietzelt M. Wolf K.H. Busching F. Hulsken-Giesler M. Monitoring systems for the support of home care Inf. Health Soc Care 2010 35 157 176 10.3109/17538157.2010.528637 21133770 · DOI
  129. [139]Medjahed H. Istrate D. Boudy J. Dorizzi B. Human activities of daily living recognition using fuzzy logic for elderly home monitoring Proceedings of the IEEE International Conference on Fuzzy Systems Jeju Island, Korea 20–24 August 2009 2001 2006
  130. [140]Nyan M.N. Tay F.E.H. Tan A.W.Y. Seah K.H.W. Distinguishing fall activities from normal activities by angular rate characteristics and high-speed camera characterization Med. Eng. Phys. 2006 28 842 849 10.1016/j.medengphy.2005.11.008 16406739 · DOI
  131. [141]Roy P.C. Bouzouane A. Giroux S. Bouchard B. Possibilistic Activity Recognition in Smart Homes for Cognitively Impaired People Appl. Artif. Intell. 2011 25 883 926 10.1080/08839514.2011.617248 · DOI
  132. [142]Sim K. Phua C. Yap G. Biswas J. Mokhtari M. Activity recognition using correlated pattern mining for people with dementia Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 7593 7597 22256096
  133. [143]Srinivasan S. Han J. Lal D. Gacic A. Towards automatic detection of falls using wireless sensors Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Lyon, France 22–26 August 2007 1379 1382 18002221
  134. [144]Tolkiehn M. Atallah L. Lo B. Yang G.-Z. Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Boston, MA, USA 30 August–3 September 2011 369 372 22254325
  135. [145]Fukai H. Nishie Y. Abiko K. Mitsukura Y. Fukumi M. Tanaka M. An age estimation system on the AIBO Proceedings of the International Conference on Control, Automation and Systems Seoul, Korea 14–17 October 2008 2551 2554
  136. [146]Noury N. AILISA: Experimental platforms to evaluate remote care and assistive technologies in gerontology Proceedings of the 7th International Workshop on Enterprise networking and Computing in Healthcare Industry Busan, Korea 23–25 June 2005 67 72
  137. [147]Chul H. Ryul J.H. Stonier K.D. The study of robot platform for orchestrating and reusing services Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts Seoul, Korea 26–28 October 2010 162 164
  138. [148]Graf B. Reiser U. Hagele M. Mauz K. Klein P. Robotic home assistant Care-O-bot 3 and innovation platform Proceedings of the IEEE Workshop on Advanced Robotics and its Social Impacts Tokyo, Japan 23–25 November 2009 139 144
  139. [151]Leite I. Pereira A. Martinho C. Paiva A. Are emotional robots more fun to play with? Proceedings of the RO-MAN—The 17th IEEE International Symposium on Robot and Human Interactive Communication Munich, Germany 1–3 August 2008 77 82
  140. [152]Stiehl W.D. Lieberman J. Breazeal C. Basel L. Lalla L. Wolf M. Design of a therapeutic robotic companion for relational, affective touch Proceedings of the ROMAN IEEE International Workshop on Robot and Human Interactive Communication Nashville, TN, USA 13–15 August 2005 408 415
  141. [153]Aminuddin R. Sharkey A. Levita L. Interaction with the Paro robot may reduce psychophysiological stress responses Proceedings of the 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) Christchurch, New Zealand 7–10 March 2016 593 594
  142. [154]Pollack M. Engberg S. Matthews J. Thrun S. Brown L. Colbry D. Orosz C. Peintner B. Ramakrishnan S. Dunbar-Jacob J. Pearl: A mobile robotic assistant for the elderly Proceedings of the Workshop on Automation as Caregiver: The Role of Intelligent Technology in Elder Care (AAAI) Menlo Park, CA, USA 28–29 July 2002 85 92
  143. [155]Onishi M. Luo Z. Odashima T. Hirano S. Tahara K. Mukai T. Generation of Human Care Behaviors by Human-Interactive Robot RI-MAN Proceedings of the IEEE International Conference on Robotics and Automation Roma, Italy 10–14 April 2007 3128 3129
  144. [156]Gupta P. Verma P. Gupta R. Verma B. MovAid—A novel device for advanced rehabilitation monitoring Proceedings of the 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Milan, Italy 25–29 August 2015 4655 4658 26737332
  145. [157]Rodriguez-Losada D. Matia F. Jimenez A. Galan R. Lacey G. Implementing Map Based Navigation in Guido, the Robotic Smart Walker Proceedings of the 2005 IEEE International Conference on Robotics and Automation Barcelona, Spain 18–22 April 2005
  146. [158]Kriglstein S. Wallner G. HOMIE: An artificial companion for elderly people Proceedings of the CHI ’05 extended abstracts on Human factors in computing systems—CHI’05 Portland, OR, USA 2–7 April 2005
  147. [159]Onishi K. ‘wakamaru’, the Robot for Your Home J. Soc. Mech. Eng. 2006 109 448 449 10.1299/jsmemag.109.1051_448 · DOI
  148. [160]Han J. Lee S. Kang B. Park S. Kim J. Kim M. Kim M. A trial English class with a teaching assistant robot in elementary school Proceedings of the 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI) Osaka, Japan 2–5 March 2010
  149. [161]Kato S. Ohshiro S. Itoh H. Kimura K. Development of a communication robot Ifbot Proceedings of the ICRA’04, 2004 IEEE International Conference on Robotics and Automation New Orleans, LA, USA 26 April–1 May 2004 697 702
  150. [163]Stiehl W.D. Lieberman J. Breazeal C. Basel L. Cooper R. Knight H. Lalla L. Maymin A. Purchase S. The huggable: A therapeutic robotic companion for relational, affective touch Proceedings of the 2006 3rd IEEE Consumer Communications and Networking Conference (CCNC 2006) Boston, MA, USA 30 July–3 August 2006
  151. [166]Liu X. Cao J. Tang S. Wen J. Guo P. Contactless Respiration Monitoring Via Off-the-Shelf WiFi Devices IEEE Trans. Mob. Comput. 2016 15 2466 2479 10.1109/TMC.2015.2504935 · DOI
  152. [167]Vimarlund V. Wass S. Big Data, Smart Homes and Ambient Assisted Living IMIA Yearb. 2014 9 143 149 10.15265/IY-2014-0011 25123734 PMC4287073 · DOI
  153. [168]Linskell J. Smart Home Technology and Special Needs: Reporting UK activity and Sharing Implemention Experiences from Scotland Proceedings of the 5th International ICST Conference on Pervasive Computing Technologies for Healthcare Dublin, Ireland 23–26 May 2011 287 291

Cited by

Sorted by citations

Authors
Uddin, M. Z., Khaksar, W., & Torresen, J.
Affiliations
  • University of Oslo (NO)
  • Department of Informatics, University of Oslo, 0316 Oslo, Norway
Corresponding author
Uddin, Md. Zia
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2018-06-25
Volume
18
Issue
7
Pages
2027
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s18072027
PMC
PMC6068532
OpenAlex
W2811312112
Cited by
166
References
1
Citations by year
2026: 6 · 2025: 14 · 2024: 23 · 2023: 20 · 2022: 31 · 2021: 30 · 2020: 27 · 2019: 13 · 2018: 2
FWCI
2.11
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • NORA - Norwegian Open Research Archives (submittedVersion, cc-by)
Funders
Norges Forskningsråd (247697)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
  • Social Sciences › Demography › Technology Use by Older Adults
  • Physical Sciences › Computer Science › Computer Networks and Communications › Wireless Sensor Networks for Data Analysis
Keywords
ambient sensors, elderly care, independent living, elderly monitoring, activities of daily living, fall detection, non-contact sensors, motion sensors, pressure sensors, video cameras, sound sensor, wearable sensors, long-term care, home monitoring
MeSH terms
Accidental Falls, Activities of Daily Living, Aged, Humans, Monitoring, Ambulatory, Independent Living

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