Authors: David Ortiz-Perez, Manuel Benavent-Lledo, Jose Garcia-Rodriguez, David Tomás, M. Flores Vizcaya-Moreno
Abstract: Cognitive decline is a natural part of aging, often resulting in reduced
cognitive abilities. In some cases, however, this decline is more pronounced,
typically due to disorders such as Alzheimer’s disease. Early detection of
anomalous cognitive decline is crucial, as it can facilitate timely
professional intervention. While medical data can help in this detection, it
often involves invasive procedures. An alternative approach is to employ
non-intrusive techniques such as speech or handwriting analysis, which do not
necessarily affect daily activities. This survey reviews the most relevant
methodologies that use deep learning techniques to automate the cognitive
decline estimation 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 works 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, the textual modality
achieves the best results and is the most relevant for detecting cognitive
decline. Moreover, combining various approaches from individual modalities into
a multimodal model consistently enhances performance across nearly all
scenarios.
Source: http://arxiv.org/abs/2410.18972v1
