Research Projects

India has a high prevalence of ocular inflammatory diseases which is increasing at an alarming rate due to ageing population and increasing digitalization. Clinically approved ocular health monitoring include retinal imaging, coherence tomography, fluorescence staining which are expensive and requires frequent hospital visits. With the ever-increasing demand for accessible and affordable healthcare, the world has seen a rise in the wearable devices which has revolutionized healthcare. Taking advantage of IoT’s holistic approach for data collection and transfer, a seamless network between the patients and healthcare professionals is established for remote monitoring. Despite significant advancements in the field of wearable devices, their successful market translation into a standalone module is hindered due to a major bottleneck of power supply. Hence, we propose to develop a self-powered wearable ocular device for real-time non-invasive monitoring of tear biomarkers to assess the progress of chronic ocular diseases. The sensing principle will rely on the triboelectric effect due to the electrical cues generated at the device-tear fluid interface by the tiny mechanical movements of the tear droplet over the device surface, thus making it self-powered in nature. Designed as a tear-fluid sensor, the device will compose of ultrathin, transparent and flexible biopolymer carrageenan film as the solid triboelectric layer which will be modified with sensing probes (ion-selective membranes/biomolecules) for binding of target analytes (Ca2+/MMP-9) in tears. As a liquid triboelectric layer, when the tear fluid will come in contact with the solid sensing film, binding of analytes will take place resulting in triboelectric signal change owing to the solid-liquid contact electrification. The triboelectric voltage signal will be collected by the Magnesium electrodes sputtered on the back side of the carrageenan film. It is expected that the self-powered ocular device will display a concentration-dependent voltage profile. A microcontroller chip and wireless communication unit will be embedded with the triboelectric sensor for data collection and transmission by collaborating with Bridge Healthcare, Chennai to develop a fully integrated platform for real-time monitoring in smartphones. The integrated device will be deployed for testing of tear biomarkers in in-vivo models to be conducted at Centre Animal Facility, IIT Madras as well as in healthy volunteers by attaching it as an undereye patch. The sensing datasets will be analysed with CNN-based AI model for concentration prediction, allowing precise identification of abnormal physiological patterns. This concept can be extended to detect other biomarkers in biofluids by simply changing the composition of the solid-triboelectric layer. Such AI integrated wearable ocular device will enable transformation of monitoring data into useful diagnostic information enhancing clinician’s decision-making timeline.