Yaser Mowafi
ABSTRACT
Studies have shown that cognitive function and stamina are vulnerable to aging. Susceptibility to such age-related decline can be related to many factors, including education, literacy, occupation, and engagement in leisure activities. In this paper, we present the design and development of a novel interactive application to exercise older adults’ cognitive function using state-of-the-art natural language processing (NLP). Installed on mobile devices, the app encourages cognitive health in older adults through continuously personalized, inquiry-based information acquisition. Learning tasks are designed to run multimodal pieces of media content aligned with users’ interests followed by multiple-choice questions, their answers, and related distractors dynamically generated by a series of Bidirectional Encoder Representations from Transformers (BERT) enhanced by Convolutional Neural Network (CNN) layers. A usability study was carried out on senior citizens living in the researchers’ community, aged 65-92. The evaluation consisted of sessions, roughly 30 minutes each, over three weeks. User performance measures of information recall represented by accuracy score and response time, and workload assessment measures composed of mental demand, temporal demand, user-performance, effort, and frustration were collected. Findings show the viability of attaining a positive user experience and engagement for older adults with inquiry-based information acquisition training personalized by users’ interests, interactions, and language analysis. The work lends insights and potential avenues for improving accessibility and engagement in tasks that stimulate older adults’ cognitive function using mobile devices.
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