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Graph Representation Learning-Based Early Depression Detection Framework in Smart Home Environments.

Jongmo Kim1, Mye Sohn1

  • 1Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Korea.

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|February 26, 2022
PubMed
Summary

This study introduces KARE, a novel framework using knowledge graphs to integrate cyber and physical data for early depression detection in the elderly. KARE analyzes combined behavioral and sentiment changes for improved accuracy.

Keywords:
early detection of depression (EDD)elderlygraph neural networksgraph representation learningknowledge graphsmart home

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Area of Science:

  • Artificial Intelligence
  • Gerontology
  • Computer Science

Background:

  • Information and Communication Technologies (ICTs) and AI are increasingly used for early depression detection in the elderly.
  • Existing methods often analyze physical or cyber behavioral changes separately, despite simultaneous symptom manifestation.
  • A gap exists in research integrating both physical and cyber data for a holistic view of elderly depression.

Purpose of the Study:

  • To propose a novel framework, KARE (Knowledge graph-based cyber-physical view (CPV)-based activity pattern recognition for early detection of depression), for early depression detection.
  • To integrate cyber and physical world data for a comprehensive analysis of depression symptoms in the elderly.
  • To address the limitations of existing methods by simultaneously considering behavioral and sentiment changes.

Main Methods:

  • Utilized a knowledge graph (KG) to provide cross-domain knowledge and resolve heterogeneity between cyber and physical data.
  • Implemented 1D-CNN for attribute representation, connecting physical/cyber attributes with the KG.
  • Employed entity alignment with CNN/GNN embeddings, unsupervised graph extraction for CPV construction, and Gaussian Mixture Model/KL divergence for activity-pattern graph representation to train a GAT model.

Main Results:

  • The KARE framework demonstrated superior performance in early depression detection compared to state-of-the-art models.
  • Experiments using real-world datasets validated the effectiveness of the proposed knowledge graph-based approach.
  • The study successfully integrated diverse data sources for a more accurate identification of depression indicators.

Conclusions:

  • KARE offers a robust and effective solution for the early detection of depression in the elderly by integrating cyber-physical data.
  • The knowledge graph approach is crucial for bridging the gap between different data domains and enabling comprehensive analysis.
  • This research highlights the potential of AI and ICTs in improving geriatric mental healthcare through advanced data integration and pattern recognition.