Mild Cognitive Impairment Identification System Based on Physiological Characteristics and Interactive Games

As the global aging population increases, the early detection and prevention of Alzheimer's disease (AD) have become important in public health.To solve the problems of subjectivity and low timeliness of traditional assessment methods, this paper proposes a multimodal dementia prevention system that combines physiological sensing, a gamification interface, and a classification model.The system includes an interactive joystick to measure pulse and blood pressure.A Chinese music game app increases the participation of the elderly and reduces their sense of rejection through gamification interaction.After the physiological data were standardized by Z-score, they were input into three small sample classifiers (Gaussian Naïve Bayes, Fisher Linear Discriminant Analysis, and Logistic Regression) for the binary classification of AD.The system performance was evaluated using the Leave-One-Out cross-validation method.Experimental results show that Logistic Regression performed best in situations with extremely small samples and class imbalance, with an F1-score of 0.700, which was higher than the other two.Dynamic features and model fusion technologies need to be integrated to further enhance the clinical application potential of the system in the early prediction of dementia.