Using Clustering Algorithms to Analyze Wearable Device Data to Support Pathways for Cultivating Medical Students’ Humanistic Care Abilities
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Abstract
This study develops an unsupervised framework using Gaussian Mixture Models (GMM) to analyze multimodal physiological data from wearable devices, aiming to support the cultivation of medical students’ humanistic care abilities. The framework integrates signals such as heart rate variability, skin conductance, and accelerometer readings obtained via textile-integrated sensors, and applies feature extraction and clustering to classify individual humanistic responsiveness. Personalized training pathways are generated based on the clustered physiological patterns, enabling targeted interventions ranging from foundational emotion awareness to advanced empathy simulation. Comparative evaluation demonstrates that GMM outperforms K-means, DBSCAN, hierarchical, and spectral clustering in terms of intra-cluster compactness, inter-cluster separation, and alignment with behavioral labels. Dynamic indicators, including low-to-high state transition frequency, provide better correlation with clinical performance than static measures, supporting flexible and adaptive training strategies. This approach offers a data-driven, end-to-end methodology for precise medical humanities education and can be integrated with antenna-enabled wearable devices, wireless physiological monitoring platforms, and edge-computing systems to facilitate real-time analysis and personalized educational interventions.
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