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Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands

Created by
  • Haebom

Author

Kristin Qi, Youxiang Zhu, Caroline Summerour, John A. Batsis, Xiaohui Liang

Outline

This paper presents an innovative approach that leverages voice assistant systems (VAS) for the early diagnosis of cognitive decline. We developed Cog-TiPRO, a novel framework for detecting cognitive decline, by analyzing voice command data collected from 35 older adults (15 of whom interacted with the VAS daily) over 18 months. Cog-TiPRO combines linguistic feature extraction using LLM-based repeated prompt refinement, HuBERT-based acoustic feature extraction, and Transformer-based temporal modeling. Using iTransformer, we achieved 73.80% accuracy and 72.67% F1 score for Mild Cognitive Impairment (MCI) detection, a 27.13% improvement over existing methods. The LLM approach identified linguistic features that characterize the daily command usage patterns of individuals experiencing cognitive decline.

Takeaways, Limitations

Takeaways:
Early diagnosis of cognitive decline using a voice assistant system is possible.
Providing a non-invasive and convenient method for monitoring cognitive decline.
Achieving MCI detection performance with improved accuracy compared to existing methods.
LLM-based approach uncovers novel linguistic features associated with cognitive decline.
Limitations:
This is a small pilot study (35 participants), and further research is needed to determine generalizability.
Only a small portion of participants (15) provided daily VAS interaction data.
Generalizability to diverse populations needs to be verified.
Long-term follow-up and larger-scale studies are needed to verify this.
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