用深度学习分析语音笔迹等非侵入数据，提前发现认知衰退迹象。
Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques
- 利用语音、文本、视觉等非侵入方式，结合Transformer与基础模型检测认知衰退。
- 文本分析在多数情况下表现最优，多模态融合能显著提升检测效果。
- 适合医疗健康、智能养老领域研究者参考，尤其关注早期干预技术。
认知衰退是衰老的自然过程，但在阿尔茨海默病等疾病影响下可能加速。早期识别异常衰退至关重要，有助于及时干预。尽管医学数据有效，但常需侵入性检查。替代方案是采用语音、手写等非侵入技术，不干扰日常活动。本综述系统梳理了基于深度学习的非侵入式认知衰退检测方法，涵盖音频、文本与视觉处理。讨论各模态的特点与优势，包括Transformer架构和基础模型等前沿技术。同时介绍多模态融合研究，整合不同信号提升性能。重点列举主要数据集及量化结果。结论显示：文本方法普遍优于其他模态；多模态模型在几乎所有场景中均实现性能提升。
原文摘要 · Abstract (English)
Cognitive decline is a natural part of aging. However, under some circumstances, this decline is more pronounced than expected, typically due to disorders such as Alzheimer's disease. Early detection of an anomalous decline is crucial, as it can facilitate timely professional intervention. While medical data can help, it often involves invasive procedures. An alternative approach is to employ non-intrusive techniques such as speech or handwriting analysis, which do not disturb daily activities. This survey reviews the most relevant non-intrusive methodologies that use deep learning techniques to automate the cognitive decline detection task, including audio, text, and visual processing. We discuss the key features and advantages of each modality and methodology, including state-of-the-art approaches like Transformer architecture and foundation models. In addition, we present studies that integrate different modalities to develop multimodal models. We also highlight the most significant datasets and the quantitative results from studies using these resources. From this review, several conclusions emerge. In most cases, text-based approaches consistently outperform other modalities. Furthermore, combining various approaches from individual modalities into a multimodal model consistently enhances performance across nearly all scenarios.
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