Franklin, M; Lagnado, D; Min, C; Mathur, A; Kawsar, F; (2023) Enhancing Wearable Technologies for Dementia Care: A Cognitive Architecture Approach. In: Explainable and Transparent AI and Multi-Agent Systems. (pp. pp. 270-280). Springer: Cham, Switzerland.

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Abstract

Activities of Daily Living (ADLs) are often disrupted in patients suffering from dementia due to a well-known taxonomy of errors. Wearable technologies have increasingly been used to monitor, diagnose, and assist these patients. The present paper argues that the benefits current and future wearable devices provide to dementia patients could be enhanced with cognitive architectures. It proposes such an architecture, establishing connections between modalities within the architecture and common errors made by dementia patients while engaging in ADLs. The paper contends that such a model could offer continuous diagnostic monitoring for both patients and caregivers, while also facilitating a more transparent patient experience regarding their condition, potentially influencing their activities. Concurrently, such a system could predict patient errors, thus offering corrective guidance before an error occurs. This system could significantly improve the well-being of dementia patients.

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