HOPES

What it does

HOPES is a wearable biomedical device for pain-free, low cost, at-home intraocular pressure (IOP) testing. Powered by patent pending sensor technology and artificial intelligence (AI), HOPES is a convenient platform for users to self-monitor IOP.

Your inspiration

My father was diagnosed with glaucoma in 2019 and suffered from constant eye pain and headache. This personal experience motivated me to delve deeper into the disease and treatments. It turns out that IOP is the clinician’s single metric to assess glaucoma. Regular monitoring of IOP fluctuation is critical to collect a comprehensive IOP profile to determine long-term treatment goals. Yet, at-home self-tonometry is inaccurate while the more accurate standard Goldmann applanation tonometry remains a clinical practice. Therefore, the field of glaucoma has lagged far behind in developing a safe, accurate, low cost, at-home eye pressure sensor.

How it works

HOPES allows users to test their IOP regularly and conveniently at home. After creating a profile in the HOPES App, the user just needs to wear the HOPES glove with sensor placed at fingertip. Click the ‘start’ button on the smart watch and then press the fingertip upon the centre of the eyelid until hearing ‘test complete’ notification. The sensor on the fingertip employs a unique sensor architecture that can capture dynamic pressure information of the user's eye with sub-millisecond precision. The captured signals are processed by our machine learning algorithms to continuously and accurately compute users’ IOP. Real time IOP will be presented to users on their smart watch. Data can also be transmitted via Bluetooth to paired devices or uploaded to cloud to be accessed remotely by clinicians. The app can prompt users with easy-to-read measurement history and direct links to healthcare system, allowing them to seek medical help to minimise future symptoms.

Design process

Our design process ran in parallel with research, seeking to combine E-skin sensor with artificial intelligence to solve real-world problems. When my father was diagnosed with glaucoma, our team performed a literature review to look at the current glaucoma monitoring methods. We also consulted clinicians and patients about challenges they faced. After interviewing them, our team identified several limitations of current tonometry: specialised equipment and personnel, high monitoring cost, pain and discomfort from anaesthesia and corneal contact, inaccurate measurements, and regular hospital visits. These drove the ideation of a pain-free, low cost and reliable eye pressure sensor. Our initial prototype consisted of a 3D-printed sensor holder with bulky electronics. After feedback from clinicians and patients, we decided to integrate the sensor onto a glove. We then designed a wearable single finger glove and incorporated electronics into a smart watch design. We also added Bluetooth communication to transmit collected data to the paired devices for real-time viewing. We continued iterating to improve the accuracy and usability of HOPES. User and clinician feedback are imperative to us throughout the design process to optimise features and user experience.

How it is different

Most clinical trials in glaucoma have relied on Goldmann applanation tonometry (GAT) as the gold standard. GAT requires specialised training, the use of fluorescein dye, and topical anaesthesia. Though home tonometry helps to avoid frequent hospital visit and minimise disruption of normal day routine, it is still costly and inaccurate. HOPES aims to address these drawbacks. Unlike current tonometry that causes patients’ discomfort, HOPES is tested on eyelid, ideally free from cornea abrasion and applicable for irregular cornea. HOPES uses a high density pressure sensor array to capture a high-resolution pressure gradient map of cornea. Through machine learning algorithm, it promises an easy, fast, and accurate IOP measurement. Computed by our pre-trained AI model, HOPES is anticipated to be independent of pressure applied and effect of eyelid. Bluetooth and user-friendly features aim to encourage users to share IOP data with clinicians easily and regularly.

Future plans

We intend to bring HOPES to market. We are currently cooperating with clinicians at the National University Hospital in Singapore to collect patients’ eye pressure data to train our machine learning model, while optimising performance and improving on the design and form factor of HOPES. Our next goals would be the following: first, to collect larger datasets through clinical trials, design and synchronise with our tele-health app interface. Next, to work with hospitals to run pilot programs with our tele-health IOP monitoring platform. Lastly, to apply for required funding to further facilitate the product commercialisation globally.

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