Digital Measures of Drawing Process to Predict Multiple Cognitive and Gait Measures in Older Adults

As the global population ages, assessment of cognitive and gait impairments is crucial for early identification and intervention for age-related disorders such as dementia and frailty; yet this quantitative, multi-faceted measurement requires trained professionals and specialized equipment. Drawing tests are widely used in clinical practice or as a self-administered tool for screening cognitive and motor dysfunctions mainly in particular domains such as visuospatial cognition and hand dexterity. We developed models for extending the applications of drawing tests to predict a wide range of cognitive and gait measures by analyzing the digitally captured drawing process. Specifically, we collected drawing data with a digital tablet from 189 older adults, along with neuropsychological examination-based cognitive measures relevant to memory, executive function, and global cognition and optical motion capture-based gait measures including gait speed, step length, and step time. We then evaluated regression models to predict multiple cognitive and gait measures solely from drawing data by combining global statistical drawing features with time-series embeddings obtained using self-supervised representation learning with deep neural networks. As a result, our models predicted both cognitive and gait measures with standardized mean absolute errors of 0.54 to 0.75. Our findings showed, for the first time, the feasibility of the use of drawing analysis for predicting multidimensional cognitive and gait measures, suggesting the potential of digital-drawing measures as a proxy marker for common aging-relevant clinical outcomes under multiple neurological and geriatric conditions.