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Construction and optimization of health behavior prediction model for the elderly in smart elderly care

Qian Guo, Peiyuan Chen

TL;DR

The paper tackles predicting elderly health behaviors in smart care under data heterogeneity and privacy concerns. It proposes a health-behavior prediction platform integrating multimodal data fusion, data loss handling, nonlinear prediction, emergency detection, and privacy protections such as federated learning and differential privacy. The methodology combines Healthy Aging theory, Theory of Planned Behavior, IoT and big data, with dynamic health-status models and cost-benefit optimization, exemplified by equations for dH/dt, resource allocation R_h(t), and CBR. Experiments across community, home, and hospital scenarios, plus extended population studies, show improved accuracy, robustness, and personalized service capabilities, indicating practical viability for scalable smart elderly care.

Abstract

With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and data privacy in the prediction of health behaviors of the elderly. The model achieves accurate prediction and dynamic management of health behaviors of the elderly through modules such as multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. In the experimental design, based on multi-source data sets and market research results, the model demonstrates excellent performance in health behavior prediction, emergency detection, and personalized services. The experimental results show that the model can effectively improve the accuracy and robustness of health behavior prediction and meet the actual application needs in the field of smart elderly care. In the future, with the integration of more data and further optimization of technology, the model will provide more powerful technical support for smart elderly care services.

Construction and optimization of health behavior prediction model for the elderly in smart elderly care

TL;DR

The paper tackles predicting elderly health behaviors in smart care under data heterogeneity and privacy concerns. It proposes a health-behavior prediction platform integrating multimodal data fusion, data loss handling, nonlinear prediction, emergency detection, and privacy protections such as federated learning and differential privacy. The methodology combines Healthy Aging theory, Theory of Planned Behavior, IoT and big data, with dynamic health-status models and cost-benefit optimization, exemplified by equations for dH/dt, resource allocation R_h(t), and CBR. Experiments across community, home, and hospital scenarios, plus extended population studies, show improved accuracy, robustness, and personalized service capabilities, indicating practical viability for scalable smart elderly care.

Abstract

With the intensification of global aging, health management of the elderly has become a focus of social attention. This study designs and implements a smart elderly care service model to address issues such as data diversity, health status complexity, long-term dependence and data loss, sudden changes in behavior, and data privacy in the prediction of health behaviors of the elderly. The model achieves accurate prediction and dynamic management of health behaviors of the elderly through modules such as multimodal data fusion, data loss processing, nonlinear prediction, emergency detection, and privacy protection. In the experimental design, based on multi-source data sets and market research results, the model demonstrates excellent performance in health behavior prediction, emergency detection, and personalized services. The experimental results show that the model can effectively improve the accuracy and robustness of health behavior prediction and meet the actual application needs in the field of smart elderly care. In the future, with the integration of more data and further optimization of technology, the model will provide more powerful technical support for smart elderly care services.

Paper Structure

This paper contains 21 sections, 8 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Traditional health behavior prediction methods
  4. Health monitoring system based on internet of things
  5. Prediction of health behavior in smart elderly care
  6. Methodology
  7. Related theories
  8. Smart elderly care service model
  9. Resource allocation and optimization
  10. Dynamic changes in health behaviors
  11. Interactions between environmental factors and health status
  12. Dynamic resource allocation and health management
  13. Cost-benefit analysis and optimization
  14. Experiment
  15. Difficulties in predicting health behaviors of the elderly
  16. ...and 6 more sections

Figures (6)

  • Figure 1: The overall framework structure of our model for predicting health behavior among the elderly.
  • Figure 2: Feature importance versus current usability.
  • Figure 3: Analysis of health status service coverage.
  • Figure 4: Analysis of the importance of platform functions and difficulty of implementation.
  • Figure 5: Comparison of predicted health status with actual health status.
  • ...and 1 more figures