Using Wearable Eye-Tracking Sensors to Monitor Postoperative VisualRehabilitation in Cataract Patients
DOI:
https://doi.org/10.69996/fmep.2025033Keywords:
Eye-Tracking, Internet of Things (IoT), Visual Rehabilitation, Postoperative Monitoring, Machine LearningAbstract
This paper explores the application of wearable sensor technology in healthcare, specifically emphasizing the use of eye-tracking devices in postoperative visual rehabilitation for cataract patients. By integrating Internet of Things (IoT) platforms, the study enables real-time collection of ocular movement data, blink rates, and visual attention metrics critical for assessing visual recovery. Wearable eye-tracking sensors provide continuous feedback on gaze behavior and visual focus, offering clinicians valuable insights into patient progress outside clinical settings. Data analytics models, including Support Vector Machines (SVM), Random Forest, Deep Neural Networks (DNN), and Logistic Regression, are employed to classify rehabilitation stages and detect anomalies. DNN models demonstrate the highest classification accuracy at 93.1%, with precision and recall both above 91%. SVM and Random Forest yield accuracies of 91.4% and 90.8% respectively, confirming the reliability of sensor-derived data in postoperative monitoring. The findings support the potential of IoT-enabled eye-tracking systems in enhancing patientspecific rehabilitation strategies and clinical decision-making.
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