基于机器学习的预测认知评估的游戏化方法

Alexander Simpson, Yongjie An, Jacob Estep, Abhijeet Saraf, J. Raiti
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引用次数: 0

摘要

认知评估是识别个体认知障碍和诊断阿尔茨海默病等疾病的重要方法。然而,它通常使用基于纸张的评估来执行,这可能会让患者感到沮丧和不参与。低接触可能导致不准确和异常的结果。本文旨在通过采用游戏化方法进行认知评估来解决这一问题。我们使用一个受“打地鼠”游戏启发的物理原型,使用SVR机器学习模型准确地预测了玩家的认知能力。该模型使用了参与游戏的参与者的输入,包括反应时间、游戏内得分和心率,r平方值为0.689 (3sf)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Gamified Approach to Cognitive Assessment with Machine Learning Based Predictions
Cognitive Assessment is an important method for identifying cognitive impairment in individuals, and diagnosing diseases such as Alzheimer’s disease. However, it is usually performed using paper-based assessment, which can be frustrating and unengaging for patients. Low engagement can lead to inaccuracies and anomalous results. This paper aims to address this issue by taking a gamified approach to cognitive assessment. Using a physical prototype of a wack-a-mole inspired game, we accurately predicted cognitive ability of players using an SVR Machine Learning model. This model used inputs from participants playing the game including reaction times, in-game scores and heart rate, achieving an R-squared of 0.689 (3sf).
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