Walking path images from real-time location data predict degree of cognitive impairment.

Background: We propose a novel approach that uses spatial walking patterns produced by real-time location systems to classify the severity of cognitive impairment (CI) among residents of a memory care unit.

Methods: Each participant was classified as "No-CI", "Mild-Moderate CI" or "Severe CI" based on their Mini-Mental State Examination scores. The location data was distributed into windows of various durations (5, 10, 15 and 30 min) and transformed into images used to train a custom convolutional neural network (CNN) at each window size. Class Activation Mapping was applied to the top-performing models to determine the features of images associated with each class.

Results: The best performing model achieved an accuracy of 87.38 % (30-min window length) with an overall pattern that larger window sizes perform better. The class activation maps were effectively consolidated into a Cognitive Impairment Classification Value (CICV) score that distinguishes between No-CI, Mild-Moderate CI, and Severe CI.

Conclusion: The class activation maps show that the CNN made relevant and intuitive distinctions for paths corresponding to each class. Future work should validate the proposed techniques with participants who are well-characterized clinically, over larger and diversified settings, and towards classification of neuropsychiatric symptoms such as motor agitation, mood, or apathy.