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Activity Recognition for Assisting People with Dementia

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Contactless Human Activity Analysis

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 200))

Abstract

Technology can help and provide support to people with dementia to ensure their safety during daily activities. In this paper, we summarize information about activity recognition for people living with dementia. This paper aims to understand the uses and types of applications, the types of sensors/systems, methods, and data used within the scope of human activity recognition to monitor, detect symptoms, or help people with dementia. To this end, 447 abstracts were collected from a Scopus database, which yielded 127 relevant papers and 102 papers that were considered in detail based on the four categories of assessment (application, system/sensors, methods, and data). This paper shows the trend that smart environment technology is most widely used for monitoring people with dementia, wherein machine learning techniques as a method for activity recognition to achieve the results of testing or implementing the system. We conclude that combining sensor devices and the addition of smartphone devices in one system is suitable for implementation because it can be used as an identity such that it distinguishes the object under study with other objects. During the monitoring process, prevention can be achieved simultaneously by adding a warning alarm in the smartphone when people with dementia perform abnormal activities, and the results need to be further analyzed to get the best pattern of activities for people with dementia. Next, the type of application that was initially designed for monitoring can be developed into an assistant for people with dementia.

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References

  1. Abascal, J., Lafuente, A., Marco, A., M Falcó, J., Casas, R., Sevillano, J.L., Cascado, D., Lujan, C.: An architecture for assisted navigation in intelligent environments. Int. J. Commun. Netw. Distrib. Syst. 4(1), 49–69 (2010)

    Google Scholar 

  2. Adlam, T., Faulkner, R., Orpwood, R., Jones, K., Macijauskiene, J., Budraitiene, A.: The installation and support of internationally distributed equipment for people with dementia. IEEE Trans. Inform. Technol. Biomed. 8(3), 253–257 (2004)

    Article  Google Scholar 

  3. Alberdi, A., Weakley, A., Schmitter-Edgecombe, M., Cook, D.J., Aztiria, A., Basarab, A., Barrenechea, M.: Smart home-based prediction of multidomain symptoms related to Alzheimer’s disease. IEEE J. Biomed. Health Inform. 22(6), 1720–1731 (2018)

    Article  Google Scholar 

  4. Almeida, A., Azkune, G.: Predicting human behaviour with recurrent neural networks. Appl. Sci. (Switzerland) 8(2) (2018)

    Google Scholar 

  5. Alzheimer Society Canada. https://alzheimer.ca/en/home/about-dementia/dementias/lewy-body-dementia

  6. Alzheimer Society Canada. https://www.alzheimers.org.uk/about-dementia/types-dementia/symptoms-dementia#content-start

  7. Amirjavid, F., Bouzouane, A., Bouchard, B.: Activity modeling under uncertainty by trace of objects in smart homes. J. Amb. Intell. Human. Comput. 5(1), 159–167 (2014)

    Article  Google Scholar 

  8. Amirjavid, F., Bouzouane, A., Bouchard, B.: Data driven modeling of the simultaneous activities in ambient environments: case study: automatic data driven anomaly recognition for interruptive activities in smart home. J. Amb. Intell. Human. Comput. 5(5), 717–740 (2014)

    Article  Google Scholar 

  9. Arifoglu, D., Bouchachia, A.: Activity recognition and abnormal behaviour detection with recurrent neural networks. In: Procedia Computer Science, vol. 110, pp. 86–93. Elsevier B.V. (2017)

    Google Scholar 

  10. Arifoglu, D., Bouchachia, A.: Detection of abnormal behaviour for dementia sufferers using convolutional neural networks. Artif. Intell. Med. 94, 88–95 (2019)

    Article  Google Scholar 

  11. Arifoglu, D., Charif, H.N., Bouchachia, A.: Detecting indicators of cognitive impairment via graph convolutional networks. Eng. Appl. Artif. Intell. 89(February 2019), 103401 (2020)

    Google Scholar 

  12. Asghar, I., Cang, S., Hongnian, Y.: Usability evaluation of assistive technologies through qualitative research focusing on people with mild dementia. Comput. Human Behav. 79, 192–201 (2018)

    Article  Google Scholar 

  13. Augusto, J.C., McCullagh, P.: Ambient intelligence: concepts and applications. Comput. Sci. Inform. Syst. 4(1), 1–27 (2007)

    Article  Google Scholar 

  14. Avgerinakis, K., Briassouli, A., Kompatsiaris, I.: Activity detection and recognition of daily living events. In: MIIRH 2013—Proceedings of the 1st ACM International Workshop on Multimedia Indexing and Information Retrieval for Heathcare, Co-located with ACM Multimedia 2013, pp. 3–9 (2013)

    Google Scholar 

  15. Belley, C., Gaboury, S., Bouchard, B., Bouzouane, A.: Nonintrusive system for assistance and guidance in smart homes based on electrical devices identification. Exp. Syst. Appl. 42(19), 6552–6577 (2015)

    Article  Google Scholar 

  16. Bouchard, K., Bouchard, B., Bouzouane, A.: Spatial recognition of activities for cognitive assistance: realistic scenarios using clinical data from Alzheimer’s patients. J. Amb. Intell. Human. Comput. 5(5), 759–774 (2014)

    Article  Google Scholar 

  17. Bouchard, K., Lapalu, J., Bouchard, B., Bouzouane, A.: Clustering of human activities from emerging movements: a flocking based unsupervised mining approach. J. Amb. Intell. Human, Comput (2018)

    Google Scholar 

  18. Brankaert, R., Snaphaan, L., den Ouden, E.: Stay in touch: an in context evaluation of a smartphone interface designed for people with dementia. In: International Workshop on Ambient Assisted Living, pp. 288–295. Springer (2014)

    Google Scholar 

  19. Breteler, M.M.B.: Vascular risk factors for alzheimer’s disease: an epidemiologic perspective. Neurobiol. Aging 21(2), 153–160 (2000)

    Article  Google Scholar 

  20. Bron, E., Smits, M., Van Swieten, J., Niessen, W., Klein, S.: Feature Selection Based on SVM Significance Maps for Classification of Dementia. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8679, 272–279 (2014)

    Google Scholar 

  21. Burmeister, O.K.: The development of assistive dementia technology that accounts for the values of those affected by its use. Ethics Inform. Technol. 18(3), 185–198 (2016)

    Article  Google Scholar 

  22. Cercone, N.J., Naruedomkul, K.: Computational Approaches to Assistive Technologies for People with Disabilities, vol. 253. IOS Press (2013)

    Google Scholar 

  23. Cerejeira, J., Lagarto, L., Mukaetova-Ladinska, E.: Behavioral and psychological symptoms of dementia. Front. Neurol. 3, 73 (2012)

    Article  Google Scholar 

  24. Chang, J.Y., Der Chou, L., Yung Wang, F.T., Fang Chen, S.: A kinect-based vocational task prompting system for individuals with cognitive impairments. Pers. Ubiquit. Comput. 17(2), 351–358 (2013)

    Article  Google Scholar 

  25. Cho, P.-C., Chen, W.-H., Liu, S.-I.: A comparative study of fuzzy conditional random fields for bathroom activity recognition. Adv. Sci. Lett. 13(1), 427–430 (2012)

    Article  Google Scholar 

  26. Cleland, I., Nugent, C.D., McClean, S.I., Hartin, P.J., Sanders, C., Donnelly, M., Zhang, S., Scotney, B., Smith, K., Norton, M.C., et al.: Predicting technology adoption in people with dementia; Initial results from the taut project. In: International Workshop on Ambient Assisted Living, pp. 266–274. Springer (2014)

    Google Scholar 

  27. Coronato, A.: Paragliola, Giovanni: A structured approach for the designing of safe AAL applications. Expert Syst. Appl. 85, 1–13 (2017)

    Article  Google Scholar 

  28. Craven, M.P., De Filippis, M.L., Dening, T.: Quality of Life Tools to Inform Co-design in the Development of assistive Technologies for People with Dementia and their Carers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8868, 394–397 (2014)

    Google Scholar 

  29. Das, B., Cook, D.J., Krishnan, N.C., Schmitter-Edgecombe, M.: One-class classification-based real-time activity error detection in smart homes. IEEE J. Sel. Top. Signal Process. 10(5), 914–923 (2016)

    Article  Google Scholar 

  30. De Filippis, M.L., Craven, M.P., Dening, T.: Informal Carer Role in the Personalisation of Assistive Solutions Connected to Aspirations of People with Dementia. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8868, 236–243 (2014)

    Google Scholar 

  31. Diaz-Orueta, U., Etxaniz, A., Gonzalez, M.F., Buiza, C.B., Urdaneta, E., Yanguas, J.: Role of cognitive and functional performance in the interactions between elderly people with cognitive decline and an avatar on TV. Univ. Access Inf. Soc. 13(1), 89–97 (2014)

    Article  Google Scholar 

  32. Donnelly, M., Nugent, C., McClean, S., Scotney, B., Mason, S., Passmore, P., Craig, D.: A mobile multimedia technology to aid those with Alzheimer’s disease. IEEE Multimedia 17(2), 42–51 (2010)

    Article  Google Scholar 

  33. Duchesne, S., Caroli, A., Geroldi, C., Barillot, C., Frisoni, G.B., Louis Collins, D.: MRI-based automated computer classification of probable AD versus normal controls. IEEE Trans. Med. Imag. 27(4), 509–520 (2008)

    Article  Google Scholar 

  34. Duong, S., Patel, T., Chang, F.: Dementia: What pharmacists need to know. Canad. Pharmacists J./Revue des Pharmaciens du Canada 150(2), 118–129 (2017)

    Google Scholar 

  35. Dziak, D., Jachimczyk, B., Kulesza, W.J.: IoT-based information system for healthcare application: design methodology approach. Appl. Sci. (Switzerland) 7(6) (2017)

    Google Scholar 

  36. Feki, M.A., Biswas, J., Tolstikov, A.: Model and algorithmic framework for detection and correction of cognitive errors. Technol. Health Care 17(3), 203–219 (2009)

    Article  Google Scholar 

  37. Feuz, K.D., Cook, D.J., Rosasco, C., Robertson, K., Schmitter-Edgecombe, M.: Automated detection of activity transitions for prompting. IEEE Trans. Hum.-Mach. Syst. 45(5), 575–585 (2015)

    Google Scholar 

  38. Fortin-Simard, D., Sebastien Bilodeau, J., Bouchard, K., Gaboury, S., Bouchard, B., Bouzouane, A.: Exploiting passive RFID technology for activity recognition in smart homes. IEEE Intell. Syst. 30(4), 7–15 (2015)

    Article  Google Scholar 

  39. Gayathri, K.S., Elias, S., Ravindran, B.: Hierarchical activity recognition for dementia care using Markov logic network. Pers. Ubiquit. Comput. 19(2), 271–285 (2015)

    Article  Google Scholar 

  40. Gheorghe Moshnyaga, V., Ryu, T., Tanaka, O., Hashimoto, K.: Low-cost assistive technologies for in-home caregivers of people with dementia. ICIC Express Lett. Part B: Appl. 7(3), 641–647 (2016)

    Google Scholar 

  41. Gottfried, B., Aghajan, H., Wong, K., Augusto, J., Guesgen, H., Kirste, T., Lawo, M.: Spatial health systems—When humans move around, vol. 8700, pp. 41–69 (2015)

    Google Scholar 

  42. Griol, D., Callejas, Z.: Mobile conversational agents for context-aware care applications. Cogn. Comput. 8(2), 336–356 (2016)

    Article  Google Scholar 

  43. Hoey, J., Poupart, P., von Bertoldi, A., Craig, T., Boutilier, C., Mihailidis, A.: Automated handwashing assistance for persons with dementia using video and a partially observable Markov decision process. Comput. Vis. Image Underst. 114(5), 503–519 (2010)

    Article  Google Scholar 

  44. Hoey, J., Schröder, T., Alhothali, A.: Affect control processes: intelligent affective interaction using a partially observable Markov decision process. Artif. Intell. 230, 134–172 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  45. Hu, C., Hua, X., Thompson, P., El Fakhri, G., Thompson, P.M., Li, Q.: Inferring sources of dementia progression with network diffusion model addiction and the brain view project diffusion MRI and connectomics view project inferring sources of dementia progression with network diffusion model (2014)

    Google Scholar 

  46. Hussain, R.G., Ghazanfar, M.A., Azam, M.A., Naeem, U., Rehman, S.U.: A performance comparison of machine learning classification approaches for robust activity of daily living recognition. Artif. Intell. Rev. 52(1), 357–379 (2019)

    Article  Google Scholar 

  47. Inoue, S., Lago, P., Hossain, T., Mairittha, T., Mairittha, N.: Integrating activity recognition and nursing care records: the system, deployment, and a verification study. Proc. ACM Interact. Mob. Wearable Ubiquit. Technol. 3(3), 1–24 (2019)

    Google Scholar 

  48. Iqbal, S., Ahsan, K., Hussain, M.A., Nadeem, A., Saeed, M., Ahmed, S.: Managing disorientation of time and place in dementia patients using bluetooth sensors. Proc. Pakist. Acad. Sci.: A. Phys. Comput. Sci. 55(2), 41–53 (2018)

    Google Scholar 

  49. Järvelin, A.: Juhola, Martti: Comparison of machine learning methods for classifying aphasic and non-aphasic speakers. Comput. Methods Prog. Biomed. 104(3), 349–357 (2011)

    Article  Google Scholar 

  50. Kańtoch, E.: Human activity recognition for physical rehabilitation using wearable sensors fusion and artificial neural networks. In: Computing in Cardiology, vol. 44, pp. 1–4. IEEE Computer Society (2017)

    Google Scholar 

  51. Karaman, S., Benois-Pineau, J., Dovgalecs, V., Mégret, R., Pinquier, J., André-Obrecht, R., Gaëstel, Y., Dartigues, J.F.: Hierarchical hidden Markov model in detecting activities of daily living in wearable videos for studies of dementia. Multimedia Tools Appl. 69(3), 743–771 (2014)

    Article  Google Scholar 

  52. Khosla, R., Nguyen, K., Chu, M.-T.: Human robot engagement and acceptability in residential aged care. Int. J.f Hum.-Comput. Interact. 33(6), 510–522 (2017)

    Google Scholar 

  53. König, A., Satt, A., Sorin, A., Hoory, R., Toledo-Ronen, O., Derreumaux, A., Manera, V., Verhey, F., Aalten, P., Robert, P.H., et al.: Automatic speech analysis for the assessment of patients with predementia and Alzheimer’s disease. Alzheimer’s Dementia: Diag., Assess. Disease Monitor. 1(1), 112–124 (2015)

    Google Scholar 

  54. Lahmiri, S.: Shmuel, A.: Performance of machine learning methods applied to structural MRI and ADAS cognitive scores in diagnosing Alzheimer’s disease. Biomed. Signal Process. Control 52, 414–419 (2019)

    Google Scholar 

  55. Lam, K.Y., Hung Tsang, N.W., Han, S., Zhang, W., Yin Ng, J.K., Nath, A.: Activity tracking and monitoring of patients with Alzheimer’s disease. Multimedia Tools Appl. 76(1), 489–521 (2017)

    Article  Google Scholar 

  56. Land, W.H., David Schaffer, J.: A Machine intelligence designed bayesian network applied to alzheimer’s detection using demographics and speech data. In: Procedia Computer Science, vol. 95, pp. 168–174. Elsevier B.V. (2016)

    Google Scholar 

  57. Land, W.H., David Schaffer, J.: The uncertainty area metric: a method for comparing learning machines on what they don’t know. In: Procedia Computer Science, vol. 114, pp. 192–199. Elsevier B.V. (2017)

    Google Scholar 

  58. Lang, L., Clifford, A., Wei, L., Zhang, D., Leung, D., Augustine, G., Danat, I.M., Zhou, W., Copeland, J.R., Anstey, K.J., et al.: Prevalence and determinants of undetected dementia in the community: a systematic literature review and a meta-analysis. BMJ Open 7(2), e011146 (2017)

    Article  Google Scholar 

  59. Lapalu, J., Bouchard, K., Bouzouane, A., Bouchard, B., Giroux, S.: Unsupervised mining of activities for smart home prediction. In: Procedia Computer Science, vol. 19, pp. 503–510. Elsevier B.V. (2013)

    Google Scholar 

  60. Li, R., Hapfelmeier, A., Schmidt, J., Perneczky, R., Drzezga, A., Kurz, A., Kramer, S.: A case study of stacked multi-view learning in dementia research. Technical report

    Google Scholar 

  61. Lightner, M.R.: Erdogmus, Deniz: Signal processing challenges in cognitive assistive technology. IEEE Signal Processing Magazine 25(5), 103–108 (2008)

    Article  Google Scholar 

  62. Lin, L., Czarnuch, S., Malhotra, A., Yu, L., Schröder, T., Hoey, J.: Affectively aligned cognitive assistance using bayesian affect control theory. In: International Workshop on Ambient Assisted Living, pp. 279–287. Springer (2014)

    Google Scholar 

  63. Lobo, A., Launer, L.J., Fratiglioni, L., Andersen, K., Di Carlo, A., Breteler, M.M.B., Copeland, J.R.M., Dartigues, J.F., Jagger, C., Martinez-Lage, J., et al.: Prevalence of dementia and major subtypes in europe: a collaborative study of population-based cohorts. Neurology 54(5), S4 (2000)

    Google Scholar 

  64. López-de Ipiña, K., Alonso, J.B., Solé-Casals, J., Barroso, N., Henriquez, P., Faundez-Zanuy, M., Travieso, C.M., Ecay-Torres, M., Martínez-Lage, P., Eguiraun, H.: On automatic diagnosis of Alzheimer’s disease based on spontaneous speech analysis and emotional temperature. Cogn. Comput. 7(1), 44–55 (2013)

    Article  Google Scholar 

  65. Lotfi, A., Langensiepen, C., Mahmoud, S.M., Akhlaghinia, M.J.: Smart homes for the elderly dementia sufferers: identification and prediction of abnormal behaviour. J. Amb. Intell. Humanized Comput. 3(3), 205–218 (2012)

    Article  Google Scholar 

  66. Lou, R.C., Giuliano, A., Mulvenna, M.D.: State of the art in electronic assistive technologies for people with dementia. In: Supporting People with Dementia Using Pervasive Health Technologies, pp. 23–35. Springer (2010)

    Google Scholar 

  67. Maclin, J.M.A., Wang, T., Xiao, S.: Biomarkers for the diagnosis of Alzheimer’s disease, dementia lewy body, frontotemporal dementia and vascular dementia. General Psychiatry 32(1), (2019)

    Google Scholar 

  68. Maitre, J., Rendu, C., Bouchard, K., Bouchard, B., Gaboury, S.: Basic daily activity recognition with a data glove. In: Procedia Computer Science, vol. 151, pp. 108–115. Elsevier B.V. (2019)

    Google Scholar 

  69. Mancioppi, G., Fiorini, L., Sportiello, M.T., Cavallo, F.: Novel technological solutions for assessment, treatment, and assistance in mild cognitive impairment. Front. Neuroinf. 13, (2019)

    Google Scholar 

  70. Mani, S., Shankle, W.R., Dick, M.B., Pazzani, M.J.: Two-stage machine learning model for guideline development. Artif. Intell. Med. 16(1), 51–71 (1999)

    Article  Google Scholar 

  71. Martin, S., Bengtsson, J.E., Dröes, R.-M.: Assistive technologies and issues relating to privacy, ethics and security. In: Supporting People with Dementia Using Pervasive Health Technologies, pp. 63–76. Springer (2010)

    Google Scholar 

  72. Martins, B., Rei, J., Braga, M., Abelha, A., Vicente, H., Neves, J., Neves, J.: Kidney care–a personal assistant assessment. In: Intelligent Systems Reference Library, vol. 132, pp. 37–54. Springer Science and Business Media, Deutschland GmbH (2018)

    Google Scholar 

  73. Meditskos, G., Dasiopoulou, S., Kompatsiaris, I.: MetaQ: a knowledge-driven framework for context-aware activity recognition combining SPARQL and OWL 2 activity patterns. Pervasive Mob. Comput. 25, 104–124 (2016)

    Article  Google Scholar 

  74. Meditskos, G., Kompatsiaris, I.: iKnow: ontology-driven situational awareness for the recognition of activities of daily living. Pervasive Mob. Comput. 40, 17–41 (2017)

    Article  Google Scholar 

  75. Meditskos, G., Plans, P.M., Stavropoulos, T.G., Benois-Pineau, J., Buso, V., Kompatsiaris, I.: Multi-modal activity recognition from egocentric vision, semantic enrichment and lifelogging applications for the care of dementia. J. Vis. Commun. Image Represent. 51(September 2017), 169–190 (2018)

    Google Scholar 

  76. Meiland, F., Dröes, R.-M., Sävenstedt, S.: Measuring the impact of cognitive prosthetics on the daily life of people with dementia and their carers. In: Supporting People with Dementia Using Pervasive Health Technologies, pp. 207–220. Springer (2010)

    Google Scholar 

  77. Miao, F., Cheng, Y., He, Y., He, Q., Li, Y.: A wearable context-aware ECG monitoring system integrated with built-in kinematic sensors of the smartphone. Sensors (Switzerland) 15(5), 11465–11484 (2015)

    Article  Google Scholar 

  78. Miesenberger, K., Petz, A.: “Easy-to-read on the web”: state of the art and needed research. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 8547, pp. 161–168 (2014)

    Google Scholar 

  79. Moutacalli, M.T., Bouzouane, A., Bouchard, B.: The behavioral profiling based on times series forecasting for smart homes assistance. J. Amb. Intell. Humanized Comput. 6(5), 647–659 (2015)

    Article  Google Scholar 

  80. Muangpaisan, W.: Clinical differences among four common dementia syndromes. Geriatr Aging 10(425), 9 (2007)

    Google Scholar 

  81. Naeem, U., Bashroush, R., Anthony, R., Azam, M.A., Tawil, A.R., Lee, S.W., Mou-Ling, D.: Activities of daily life recognition using process representation modelling to support intention analysis. Int. J. Pervasive Comput. Commun. 11(3), 347–371 (2015)

    Article  Google Scholar 

  82. Naeem, U., Bigham, J.: Activity recognition in the home using a hierarchal framework with object usage data. J. Amb. Intell. Smart Environ. 1(4), 335–350 (2009a)

    Google Scholar 

  83. Naeem, U., Bigham, J.: Recognising activities of daily life through the usage of everyday objects around the home. In: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare-Pervasive Health 2009, PCTHealth 2009 (2009b)

    Google Scholar 

  84. Nakahara, K., Yamaguchi, H., Higashino, T.: In-home activity and micro-motion logging using mobile robot with kinect. In: Adjunct Proceedings of the 13th International Conference on Mobile and Ubiquitous Systems: Computing Networking and Services, pp. 106–111 (2016)

    Google Scholar 

  85. Nunes, C., Silva, D., Guerreiro, M., De Mendonça, A., Carvalho, A.M., Madeira, S.C.: Class imbalance in the prediction of dementia from neuropsychological data. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 8154, pp. 138–151 (2013)

    Google Scholar 

  86. Oltu, B., Akşahin, M.F., Kibaroğlu, S.: Investigation of EEG signal for diagnosis of mild cognitive impairment and alzheimer’s disease. In: 2019 Medical Technologies Congress (TIPTEKNO), pp. 1–4. IEEE (2019)

    Google Scholar 

  87. Orimaye, S.O., Wong, J.S.M., Golden, K.J., Wong, C.P., Soyiri, I.N.: Predicting probable Alzheimer’s disease using linguistic deficits and biomarkers. BMC Bioinf. 18(1) (2017)

    Google Scholar 

  88. Orpwood, R., Chadd, J., Howcroft, D., Sixsmith, A., Torrington, J., Gibson, G., Chalfont, G.: Designing technology to improve quality of life for people with dementia: user-led approaches. Univ. Access Inf. Soc. 9(3), 249–259 (2010)

    Article  Google Scholar 

  89. Orpwood, R., Gibbs, C., Adlam, T., Faulkner, R., Meegahawatte, D.: The design of smart homes for people with dementia-user-interface aspects. Univ. Access Inf. Soc. 4(2), 156–164 (2005)

    Article  Google Scholar 

  90. Osmani, V., Daqing, Z., Balasubramaniam, S.: Human activity recognition supporting context-appropriate reminders for elderly. In: 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare-Pervasive Health 2009, PCTHealth 2009 (2009)

    Google Scholar 

  91. Parlak, S., Sarcevic, A., Marsic, I., Burd, R.S.: Introducing RFID technology in dynamic and time-critical medical settings: requirements and challenges. J. Biomed. Inf. 45(5), 958–974 (2012)

    Article  Google Scholar 

  92. Pasquier, F.: Early diagnosis of dementia: neuropsychology. J. Neurol. 246(1), 6–15 (1999)

    Article  Google Scholar 

  93. Peters, C., Hermann, T., Wachsmuth, S.: Tebra: automatic task assistance for persons with cognitive disabilities in brushing teeth. In: In: International Joint Conference on Biomedical Engineering Systems and Technologies, pp. 333–353. Springer (2013)

    Google Scholar 

  94. Phua, C., Roy, P.C., Aloulou, H., Biswas, J., Tolstikov, A., Foo, V.S.F., Aung, A.P.W., Huang, W., Feki, M.A., Maniyeri, J., Chu, A.K.W., Xu, D.: State-of-the-art assistive technology for people with dementia. Number January (2013)

    Google Scholar 

  95. Prince, M., Comas-Herrera, A., Knapp, M., Guerchet, M., Karagiannidou, M.: World alzheimer report 2016: improving healthcare for people living with dementia: coverage, quality and costs now and in the future (2016)

    Google Scholar 

  96. Raeper, R., Lisowska, A., Rekik, I.: Cooperative correlational and discriminative ensemble classifier learning for early dementia diagnosis using morphological brain multiplexes. IEEE Access 6, 43830–43839 (2018)

    Article  Google Scholar 

  97. Ramos, J., Anacleto, R., Costa, Paulo Novais, Â., Figueiredo, L., Almeida, A.: Orientation system for people with cognitive disabilities. In: Advances in Intelligent and Soft Computing, vol. 153, AISC, pp. 43–50 (2012)

    Google Scholar 

  98. Ramzan, M., Abid, A., Khan, H.U., Awan, S.M., Ismail, A., Ahmed, M., Ilyas, M., Mahmood, A.: A review on state-of-the-art violence detection techniques. IEEE Access 7, 107560–107575 (2019)

    Article  Google Scholar 

  99. Riboni, D., Bettini, C., Civitarese, G., Janjua, Z.H., Helaoui, R.: SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment. Artif. Intell. Med. 67, 57–74 (2016)

    Article  Google Scholar 

  100. Roy, P.C., Bouzouane, A., Giroux, S., Bouchard, B.: Possibilistic activity recognition in smart homes for cognitively impaired people. Appl. Artif. Intell. 25(10), 883–926 (2011)

    Article  Google Scholar 

  101. Sarraf, S., Desouza, D.D., Anderson, J.A.E., Saverino, C.: MCADNNet: recognizing stages of cognitive impairment through efficient convolutional fMRI and MRI neural network topology models. IEEE Access 7, 155584–155600 (2019)

    Article  Google Scholar 

  102. Seixas, F.L., Zadrozny, B., Laks, J., Conci, A., Muchaluat Saade, D.C.: A Bayesian network decision model for supporting the diagnosis of dementia, Alzheimer’s disease and mild cognitive impairment. Comput. Biol. Med. 51, 140–158 (2014)

    Article  Google Scholar 

  103. Shahrestani, S.: Internet of Things and smart environments: assistive technologies for disability, dementia, and aging. Springer International Publishing (2017)

    Google Scholar 

  104. Singh, N.P.T.F., Samuel Preston, J., King, R.D., Marron, J.S., Weiner, M.W., Joshi, S.: Quantifying anatomical shape variations in neurological disorders. Med. Image Anal. 18(3), 616–633 (2014)

    Google Scholar 

  105. Souillard-Mandar, W., Davis, R., Rudin, C., Rhoda, A., Libon, D.J., Swenson, R., Price, C.C., Lamar, M., Penney, D.L.: Learning classification models of cognitive conditions from subtle behaviors in the digital clock drawing test. Mach. Learn. 102(3), 393–441 (2016)

    Google Scholar 

  106. Span, M., Smits, C., De Ven Groen-Van, L.M., Cremers, A., Jukema, J., Vernooij-Dassen, M., Hettinga, M.: Towards an interactive web tool that supports shared decision making in dementia. Int. J. Adv. Life Sci 6, 338–349 (2014)

    Google Scholar 

  107. Staudinger, T., Polikar, R.: Analysis of complexity based EEG features for the diagnosis of Alzheimer’s disease. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, pp. 2033–2036 (2011)

    Google Scholar 

  108. Stavropoulos, T.G., Meditskos, G., Andreadis, S., Avgerinakis, K., Adam, K., Kompatsiaris. I.: Semantic event fusion of computer vision and ambient sensor data for activity recognition to support dementia care. J. Amb. Intell. Humanized Comput. pp. 1–16, December 2016

    Google Scholar 

  109. Stefanova, E., Pavlovic, A., Jovanovic, Z., Veselinovic, N., Despotovic, I., Stojkovic, T., Sternic, N., Kostic, V.: Vascular risk factors in Alzheimer’s disease-preliminary report. J. Neurol. Sci. 322(1–2), 166–169 (2012)

    Article  Google Scholar 

  110. Suijkerbuijk, S., Brankaert, R., De Kort, Y.A.W., Snaphaan, L.J.A.E., Ouden, E.D.: Seeing the first-person perspective in dementia: a qualitative personal evaluation game to evaluate assistive technology for people affected by dementia in the home context. Interact. Comput. 27(1), 47–59 (2015)

    Article  Google Scholar 

  111. Tapus, A., Maria Vieru, A.-M.: Robot cognitive stimulation for the elderly. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNCS, vol. 7930, pp. 94–102 (2013)

    Google Scholar 

  112. Tung, J., Snyder, H., Hoey, J., Mihailidis, A., Carrillo, M., Favela, J.: Everyday patient-care technologies for Alzheimer’s disease. IEEE Pervasive Comput. 12(4), 80–83 (2013)

    Article  Google Scholar 

  113. Van der Flier, W.M., Scheltens, P.: Epidemiology and risk factors of dementia. J. Neurol. Neurosurg. Psychiatry 76(suppl 5), v2–v7 (2005)

    Article  Google Scholar 

  114. van Hoof, J., Verboor, J., Oude Weernink, C.E., Sponselee, A.A.G., Sturm, J.A., Kazak, J.K., Govers, G.M.J., van Zaalen, Y.: Real-time location systems for asset management in nursing homes: an explorative study of ethical aspects. Information (Switzerland) 9(4) (2018)

    Google Scholar 

  115. Van Hoof, J., Wouters, E.J.M., Marston, H.R., Vanrumste, B., Overdiep, R.A.: Ambient assisted living and care in the Netherlands: the voice of the user. Int. J. Amb. Comput. Intell. (IJACI) 3(4), 25–40 (2011)

    Article  Google Scholar 

  116. Wan, J., Byrne, C., O’Hare, G.M.P., O’Grady, M.J.: OutCare: supporting dementia patients in outdoor scenarios. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), LNAI, vol. 6279, pp. 365–374 (2010)

    Google Scholar 

  117. Wang, P., Sun, L., Smeaton, A.F., Gurrin, C., Yang, S.: Computer vision for lifelogging: characterizing everyday activities based on visual semantics. In: Computer Vision for Assistive Healthcare, pp. 249–282. Elsevier (2018)

    Google Scholar 

  118. Wherton, J.P., Monk, A.F.: Technological opportunities for supporting people with dementia who are living at home. Int. J. Hum. Comput. Stud. 66(8), 571–586 (2008)

    Article  Google Scholar 

  119. Wherton, J.P., Monk, A.F.: Problems people with dementia have with kitchen tasks: the challenge for pervasive computing. Interact. Comput. 22(4), 253–266 (2010)

    Article  Google Scholar 

  120. World Health Organization: Global action plan on the public health response to dementia (2017–2025)

    Google Scholar 

  121. Yao, L., Sheng, Q.Z., Li, X., Gu, T., Tan, M., Wang, X., Wang, S., Ruan, W.: Compressive representation for device-free activity recognition with passive RFID signal strength. IEEE Trans. Mob. Comput. 17(2), 293–306 (2017)

    Article  Google Scholar 

  122. Yefimova, M., Woods, D.L.: Using sensor technology to monitor disruptive behavior of persons with dementia. In: AAAI Fall Symposium-Technical Report, vol. FS-12-01, pp. 51–54 (2012)

    Google Scholar 

  123. Zafeiris, D., Rutella, S., Ball, G.R.: An artificial neural network integrated pipeline for biomarker discovery using Alzheimer’s disease as a case study (2018)

    Google Scholar 

  124. Zaffalon, M., Wesnes, K., Petrini, O.: Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data. In: Artificial Intelligence in Medicine, vol. 29, pp. 61–79. Elsevier (2003)

    Google Scholar 

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Authors and Affiliations

  1. Kyushu Institute of Technology, 2-4 Hibikino Wakamatsu, Kitakyushu, Fukuoka, 808-0196, Japan

    Muhammad Fikry, Defry Hamdhana, Paula Lago & Sozo Inoue

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  1. Muhammad Fikry
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  2. Defry Hamdhana
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  3. Paula Lago
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  4. Sozo Inoue
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Correspondence to Muhammad Fikry .

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  1. Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  2. Multimedia R&D and Standards, Qualcomm Technologies Inc., San Diego, CA, USA

    Upal Mahbub

  3. College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

    Tauhidur Rahman

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Fikry, M., Hamdhana, D., Lago, P., Inoue, S. (2021). Activity Recognition for Assisting People with Dementia. In: Ahad, M.A.R., Mahbub, U., Rahman, T. (eds) Contactless Human Activity Analysis. Intelligent Systems Reference Library, vol 200. Springer, Cham. https://doi.org/10.1007/978-3-030-68590-4_10

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