Pattern recognition for the detection and monitoring of verbal and non-verbal alterations in Alzheimer's disease

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Alzheimer's disease (AD) affects one in ten adults over 65 in Canada. Interventions may be more effective in the early stages of dementia. Nevertheless, it is highly common, especially in low- and middle-income countries, to diagnose AD several years after the disease begins, leading to a treatment gap for early dementia sufferers. This gap could reduce the effectiveness of treatments, prolonging patients' state of reduced independence. Early diagnosis and treatment are now recognized as a means of attenuating care costs and reducing this gap. Furthermore, an early diagnosis would allow sufferers and their families to get their affairs in order by better anticipating the future and preparing accordingly.***The current program aims to automatically characterize and monitor changes in AD. This research focuses on analyzing verbal and non-verbal behaviours in multiple modalities: speech, discourse, and facial and corporal expressions. More specifically, we are pursuing three objectives:***1. Distinguish between AD patients and ailing patients using properties extracted from transcriptions and audio sources of conversations with elderly subjects (analysis of verbal behaviours).***2. Distinguish between AD patients and ailing patients using properties extracted from video recordings of conversations with elderly subjects (analysis of non-verbal behaviours).***3. Construct a machine learning system that will use every property obtained in (1) and (2) to identify various stages of AD.***To accomplish these tasks, we are using four recognized datasets: the BBVA of the BBVA Foundation in Spain, the DementiaBank Pitt Corpus from Pittsburgh University, the Carolina Conversations Collection from the Medical University of South Carolina, and the CorpAGEst corpus from Belgium (European Union Seventh Framework Program). The first two contain conversations made during the application of cognitive tests to patients. The last two contain natural free interviews in a non-stressful environment. The languages covered include Spanish, English and French.***The impacts of this proposal are twofold. First, it contributes to building stronger facial and gesture recognition techniques adapted to the aging population. That population being on the rise everywhere in the world, this contribution will allow the development of software aimed specifically at the elderly. Second, it contributes to developing automated tools to monitor AD and other types of dementia (e.g., the effects of medications on language abilities), giving the community of researchers tools to explore specificities in an automatic fashion. The results of this proposal are not meant to diagnose or to replace specialists (e.g., clinicians, doctors, geriatricians); they are aimed at assisting these professionals in innovative and non-invasive ways, helping them to understand the disease and its progression.