DCARE: Um Modelo Computacional para Acompanhamento de Pessoas com Doença de Alzheimer baseado na Análise de Históricos de Contextos
Resumo
The aging of the population generates the incidence of diseases characteristic of advancing age, among them Alzheimer’s Disease (AD). Patients with this illness, which affects neurological functions, need support to maintain maximum independence and security during this stage of life, as the cure and reversal of symptoms have not yet been discovered. This work aims to propose a model that, based on physiological data received from external applications, makes it possible to identify possible dangerous behaviors of patients with AD. The main scientific contribution of this work is the specification of a model focusing on Alzheimer’s disease using the analysis of Context Histories and Context Prediction, which considering the state of the art, is the only one that uses analysis of Context Histories to perform predictions. The computational model used in its structure an ontology developed by this project for the treatment of contexts within Alzheimer’s. In addition, a simulator called DCARE Dataset Simulator, of Activity Daily Living (ADLs) which generates datasets were developed to perform tests of the model and an ontology has been proposed for the treatment of contexts in the subject of Alzheimer’s. DCARE is based on the experimental research method, to understand the disease and find solutions to minimize its impact on the daily monitoring of patients. Scenarios used in the construction of the model were created over interviews with five specialists in the care for patients with AD. Tests were performed with the mass of data with 1026 scenarios generated by the proposed simulator by this work. The results revealed that the predictions of the model scenarios reached the objective of the work, achieving 97.44% of the average accuracy rate.
Palavras-chave:
Alzheimer’s disease, Context Histories, Context Prediction, Patient monitoring, Internet of Things for Healthcare
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Stephan Sigg. 2008. Development a novel context prediction algorithm and analysis of context prediction schemes. Kassel University Press, Kassel, Alemanha.
E. Smets, W. De Raedt, and C. Van Hoof. 2019. Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings. IEEE Journal of Biomedical and Health Informatics 23, 2(2019), 463–473.
Julia Rosemary Thorpe, Birgitte Hysse Forchhammer, and Anja M. Maier. 2019. Development of a sensor-based behavioral monitoring solution to support dementia care. Journal of Medical Internet Research 21, 6 (2019), 1–14. https://doi.org/10.2196/12013
Lin Wan, Claudia Müller, Dave Randall, and Volker Wulf. 2016. Design of A GPS Monitoring System for Dementia Care and Its Challenges in Academia-Industry Project. ACM Trans. Comput.-Hum. Interact. 23, 5, Article 31 (Oct. 2016), 36 pages. https://doi.org/10.1145/2963095
World Health Organization WHO. 2019. Dementia: 2019 Statistical Update. https://www.who.int/news-room/fact-sheets/detail/dementia.
Francesco Amato, Pietro Crovari, Andrea Masciadri, Sara Bianchi, Maria Giulia Grillo Pasquarelli, Marie Toldo, Sara Comai, Afnan Imtiaz, and Ece Yuyar. 2018. Clone: A promising system for the remote monitoring of Alzheimer’s patients an experimentation with a wearable device in a village for Alzheimer’s care. ACM International Conference Proceeding Series June (2018), 255–260. https://doi.org/10.1145/3284869.3284906
Alzheimer’s Association. 2019. What Is Alzheimer’s Disease? https://www.alz.org/alzheimers-dementia/what-is-alzheimers.
Henry Brodaty, Michael H. Connors, Jing Xu, Michael Woodward, David Ames, and PRIME study group. 2014. Predictors of institutionalization in dementia: a three year longitudinal study. Journal of Alzheimer’s disease : JAD 40, 1 (2014), 221–226. https://doi.org/10.3233/JAD-131850
Winslow Burleson, Cecil Lozano, Vijay Ravishankar, Jisoo Lee, and Diane Mahoney. 2018. An assistive technology system that provides personalized dressing support for people living with dementia: Capability study. Journal of Medical Internet Research 20, 5 (2018), 1–20. https://doi.org/10.2196/medinform.5587
John T. Cacioppo, Louis G. Tassinary, and Gary Berntson (Eds.). 2007. Handbook of Psychophysiology(3 ed.). Cambridge University Press, Trinity, Cambridge, UK. https://doi.org/10.1017/CBO9780511546396
R. Castaldo, P. Melillo, U. Bracale, M. Caserta, M. Triassi, and L. Pecchia. 2015. Acute mental stress assessment via short term HRV analysis in healthy adults: A systematic review with meta-analysis. Biomedical Signal Processing and Control 18 (2015), 370 – 377. https://doi.org/10.1016/j.bspc.2015.02.012
John A. Chalmers, Daniel S. Quintana, Maree J.-Anne Abbott, and Andrew H. Kemp. 2014. Anxiety Disorders are Associated with Reduced Heart Rate Variability: A Meta-Analysis. Frontiers in Psychiatry 5 (2014), 80. https://doi.org/10.3389/fpsyt.2014.00080
Kwang-Ho Choi, Junbeom Kim, O. Sang Kwon, Min Ji Kim, Yeon Hee Ryu, and Ji-Eun Park. 2017. Is heart rate variability (HRV) an adequate tool for evaluating human emotions? – A focus on the use of the International Affective Picture System (IAPS). Psychiatry Research 251(2017), 192 – 196. https://doi.org/10.1016/j.psychres.2017.02.025
Jiska Cohen-Mansfield. 2008. Agitated behavior in persons with dementia: the relationship between type of behavior, its frequency, and its disruptiveness. Journal of psychiatric research 43, 1 (nov 2008), 64–69. https://doi.org/10.1016/j.jpsychires.2008.02.003
Organização Pan Americana da Saúde OPAS. 2018. Folha informativa 2018: 10 principais causas de morte no mundo. [link].
Alexsandro Souza Filippetto, Robson Lima, and Jorge Luis Victória Barbosa. 2021. A risk prediction model for software project management based on similarity analysis of context histories. Information and Software Technology 131 (2021), 106497. https://doi.org/10.1016/j.infsof.2020.106497
S. A. Khoo, T. Y. Chen, Y. H. Ang, and P. Yap. 2013. The impact of neuropsychiatric symptoms on caregiver distress and quality of life in persons with dementia in an Asian tertiary hospital memory clinic. International Psychogeriatrics 25, 12 (2013), 1991–1999. https://doi.org/10.1017/S1041610213001518
Sylvia D. Kreibig. 2010. Autonomic nervous system activity in emotion: A review. Biological Psychology 84, 3 (2010), 394 – 421. https://doi.org/10.1016/j.biopsycho.2010.03.010 The biopsychology of emotion: Current theoretical and empirical perspectives.
Chelsey Lai Kwan, Yacine Mahdid, Rossio Motta Ochoa, Keven Lee, Melissa Park, and Stefanie Blain-Moraes. 2019. Wearable technology for detecting significant moments in individuals with dementia. BioMed Research International 2019 (2019), 2314–6133. https://doi.org/10.1155/2019/6515813
Savanna Denega Machado and Jorge Luis Victória Barbosa. 2020. (to appear). Technologies Applied in the Care of Patients with Alzheimer’s Disease: A Systematic Review. In Proceedings of the Brazilian Symposium on Multimedia and the Web (São Luís, Brazil) (WebMedia ’20). Association for Computing Machinery, New York, NY, USA, 29–32. https://doi.org/10.1145/3428658.3431753
D.L. Murman, Q. Chen, M.C. Powell, S.B. Kuo, C.J. Bradley, and C.C. Colenda. 2002. The incremental direct costs associated with behavioral symptoms in AD. Neurology 59, 11 (2002), 1721–1729. https://doi.org/10.1212/01.WNL.0000036904.73393.E4 arXiv: https://n.neurology.org/content/59/11/1721.full.pdf
Christianne Nesbitt, Ajay Gupta, Shubham Jain, Kurt Maly, and Hamid Reza Okhravi. 2018. Reliability of Wearable Sensors to Detect Agitation in Patients with Dementia: A Pilot Study. In Proceedings of the 2018 10th International Conference on Bioinformatics and Biomedical Technology (Amsterdam, Netherlands) (ICBBT ’18). Association for Computing Machinery, New York, NY, USA, 73–77. https://doi.org/10.1145/3232059.3232067
Elisa Antônia Ribeiro. 2008. A perspectiva da entrevista na investigação qualitativa, In A perspectiva da entrevista na investigação qualitativa. Evidência: olhares e pesquisa em saberes educacionais 4, 5, 129–148.
Lin Shu, Jinyan Xie, Mingyue Yang, Ziyi Li, Zhenqi Li, Dan Liao, Xiangmin Xu, and Xinyi Yang. 2018. A Review of Emotion Recognition Using Physiological Signals. Sensors 18, 7 (Jun 2018), 2074. https://doi.org/10.3390/s18072074
Stephan Sigg. 2008. Development a novel context prediction algorithm and analysis of context prediction schemes. Kassel University Press, Kassel, Alemanha.
E. Smets, W. De Raedt, and C. Van Hoof. 2019. Into the Wild: The Challenges of Physiological Stress Detection in Laboratory and Ambulatory Settings. IEEE Journal of Biomedical and Health Informatics 23, 2(2019), 463–473.
Julia Rosemary Thorpe, Birgitte Hysse Forchhammer, and Anja M. Maier. 2019. Development of a sensor-based behavioral monitoring solution to support dementia care. Journal of Medical Internet Research 21, 6 (2019), 1–14. https://doi.org/10.2196/12013
Lin Wan, Claudia Müller, Dave Randall, and Volker Wulf. 2016. Design of A GPS Monitoring System for Dementia Care and Its Challenges in Academia-Industry Project. ACM Trans. Comput.-Hum. Interact. 23, 5, Article 31 (Oct. 2016), 36 pages. https://doi.org/10.1145/2963095
World Health Organization WHO. 2019. Dementia: 2019 Statistical Update. https://www.who.int/news-room/fact-sheets/detail/dementia.
Publicado
07/06/2021
Como Citar
MACHADO, Savanna Denega; BARBOSA, Jorge Luis Victória; TAVARES, João da Rosa; MARTINS, Márcio Garcia.
DCARE: Um Modelo Computacional para Acompanhamento de Pessoas com Doença de Alzheimer baseado na Análise de Históricos de Contextos. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 17. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
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