Home-based Dry Eye Assessment via Blink Kinematics Using mmWave and Clinical Knowledge Distillation

Tear Film Break-Up Time (TBUT) is a critical clinical parameter in the management of dry eye disease (DED). However, traditional TBUT assessments rely on costly and time-consuming clinical procedures, while existing home-based solutions fail to provide precise TBUT values. In this work, we present Blinic, a contactless system leveraging commercial millimeter-wave (mmWave) radar to predict precise TBUT values and assess DED severity grades at home. Blinic incorporates detailed blink kinematics that are closely linked to TBUT. To address the challenge of predicting TBUT directly from radar data, we propose a teacher-student learning framework. The teacher model, trained on electronic health records (EHRs) including image-based diagnostic tests, transfers medical insights to the student model, which uses radar-captured blink dynamics. This knowledge transfer is further enhanced by a fine-tuned large language model, DryEye-LLM, which is based on clinical diagnostic reports and employs unsupervised domain adaptation to align EHRs with radar data. To ensure accurate blink motion capture, Blinic employs an antenna-coded MIMO mmWave radar design. Additionally, a query-based multitask learning module simultaneously predicts TBUT and DED severity grades, addressing potential conflicts in feature representation. Evaluated on 192 participants in collaboration with an eye clinic, Blinic demonstrates achieving a mean absolute error of 2.73 seconds for TBUT with an average accuracy of 90.54% for DED grading in real-world settings, providing a practical solution for home-based DED management.