A recursive model of residual life prediction for human beings with health information from activities of daily living and memory. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Gao, Kaiye; Wang, Tianshi; Peng, Kaixiang; Wang, Ziwen; He, Qiong; Peng, Rui
• Is Part Of: Systems science & control engineering. Volume 9:Issue 1(2021)
• Page Start: 529
• Page End: 541
• Publication Date: 2021-01-01
• Subjects: Stochastic filtering -- recursive model -- health information -- activities of daily living -- memory; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2021.1943724
• Languages: English
• ISSNs: 2164-2583

Abstract

Abstract : As the ageing population increases, it is essential to manage human resources with residual life prediction, especially for the ageing population. Thus, this study aims to predict residual life using the available information of health statuses, such as the performances in activities of daily living (ADL) and memory. In this study, the principal components of ADL and memory information are extracted for prediction. The relationship between the principal components and residual life is established based on the concept of the proportional residual, which states that the residual life may be proportional to the changes in ADL and memory performance. A recursive model for residual life prediction is formulated and fitted for the Chinese Health and Nutrition Survey data. Finally, a goodness-of-fit test is conducted, and an example case is presented. The results show that the fitted model in this study is more accurate and precise than the original, unfitted model to estimate the residual life of human beings. This study is advantageous over existing studies in two aspects: (1) the model is formulated using a recursive method based on stochastic filtering; (2) the data include both physical status and mental status.