Survey of neurocognitive disorder detection methods based on speech, visual, and virtual reality technologies

Abstract

The global trend of population aging poses significant challenges to society and healthcare systems, particularly because of neurocognitive disorders (NCDs) such as Parkinson's disease (PD) and Alzheimer's disease (AD). In this context, artificial intelligence techniques have demonstrated promising potential for the objective assessment and detection of NCDs. Multimodal contactless screening technologies, such as speech-language processing, computer vision, and virtual reality, offer efficient and convenient methods for disease diagnosis and progression tracking. This paper systematically reviews the specific methods and applications of these technologies in the detection of NCDs using data collection paradigms, feature extraction, and modeling approaches. Additionally, the potential applications and future prospects of these technologies for the detection of cognitive and motor disorders are explored. By providing a comprehensive summary and refinement of the extant theories, methodologies, and applications, this study aims to facilitate an in-depth understanding of these technologies for researchers, both within and outside the field. To the best of our knowledge, this is the first survey to cover the use of speech-language processing, computer vision, and virtual reality technologies for the detection of NSDs.