用语音助手长期记录命令，通过大模型迭代优化检测认知衰退
Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands
- 用大模型迭代优化提示词，提取语音命令中的语言特征
- 18个月数据中识别出轻度认知障碍准确率达73.80%
- 适合关注老龄化健康监测的医疗与AI研究者
早期发现认知衰退对延缓神经退行性疾病进展至关重要。传统诊断依赖耗时的临床评估，难以实现频繁监测。本初步研究探讨语音助手系统（VAS）作为非侵入式工具，通过纵向分析语音命令中的言语模式来检测认知衰退。在18个月期间，我们收集了35名老年人的语音命令数据，其中15人每日在家中使用语音助手。为应对短句、无结构、噪声大的挑战，提出Cog-TiPRO框架：（1）利用大模型驱动的迭代提示优化进行语言特征提取，（2）基于HuBERT的声学特征提取，（3）基于transformer的时间建模。采用iTransformer，该方法在检测轻度认知障碍（MCI）上达到73.80%准确率和72.67% F1-score，优于基线27.13%。通过大模型方法，我们识别出认知衰退个体日常命令使用的独特语言特征。
原文摘要 · Abstract (English)
Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnostic approaches rely on labor-intensive clinical assessments, which are impractical for frequent monitoring. Our pilot study investigates voice assistant systems (VAS) as non-invasive tools for detecting cognitive decline through longitudinal analysis of speech patterns in voice commands. Over an 18-month period, we collected voice commands from 35 older adults, with 15 participants providing daily at-home VAS interactions. To address the challenges of analyzing these short, unstructured and noisy commands, we propose Cog-TiPRO, a framework that combines (1) LLM-driven iterative prompt refinement for linguistic feature extraction, (2) HuBERT-based acoustic feature extraction, and (3) transformer-based temporal modeling. Using iTransformer, our approach achieves 73.80% accuracy and 72.67% F1-score in detecting MCI, outperforming its baseline by 27.13%. Through our LLM approach, we identify linguistic features that uniquely characterize everyday command usage patterns in individuals experiencing cognitive decline.
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