Deep Insights into Cognitive Decline: A Survey of Leveraging Non-Intrusive Modalities with Deep Learning Techniques

TL;DR

This survey reviews non-intrusive deep learning methods for early detection of cognitive decline using audio, text, and visual data, emphasizing the effectiveness of multimodal approaches and state-of-the-art architectures.

Contribution

It provides a comprehensive overview of recent non-intrusive deep learning techniques, including multimodal models and advanced architectures like Transformers, for cognitive decline detection.

Findings

Text-based approaches outperform other modalities.

Multimodal models improve detection accuracy.

State-of-the-art architectures enhance performance.

Abstract

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.