Portfolio Category

Health Tech

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NeuroSpin (Mind-Controlled Wheelchair)

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NeuroSpin consists of a prototype for a wheelchair that can be controlled via electroencephalogram (EEG) sensing. The main application of this wheelchair is to enable paralyzed individuals to have control over their transportation by using their concentration levels.   

I worked in a team with Alana Crognale and Tomi Kalejaiye to build NeuroSpin at Cornell Tech, NYC. We created a prototype for a wheel that starts rotating when your concentration goes above a certain threshold. The prototype also includes a green LED that lights up when the wheel is rotating and a red LED that lights up when the wheel is static, together with a screen to provide user feedback and make the device interactive. 

Some of the components used are a Force Trainer headset that we modified, a Metro Mini, wheels, motors, a transistor, a diode, LEDs, and small physical wheelchair prototype. All of this for just $30!

The next steps are to make the wheel rotate only when the user concentrates on the LED, and replicate the device to create the four wheels of a chair.  Stay tuned!

Check out the demo video! We also presented this prototype at the Dec 2019 Cornell Tech Open Studio event. For more information on the hardware and software specifics of the project send me a message.

Multichannel Light Sensor

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The project consists of a six channel light sensor that measures the intensity of light at different wavelengths within the visible spectrum. It includes an Android mobile application that retrieves the data from the sensor and stores it both locally and in a database within a secure server. The sensor was used as the prototype for a commercial product to perform studies at the Surrey Sleep Research Centre (SSRC) aimed at analyzing the influence of light on human overall health and sleep quality. I completed this project as my dissertation at the University of Surrey, UK.

Check out the video! For more information on the hardware and software specifics of the project click on the link to the full project repository.

Project Repository

EEG Meditation Tracker

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This is an ongoing project that I started in the COVID-19 lockdown period. During quarantine I meditated often to improve my mental health. But I often struggled to know whether I was making progress and increasing my ability to stay “in the zone” for longer periods of time. So I thought… why not build myself a tracker?

This Arduino-based prototype measures my brain waves and calculates how long my mental relaxation streaks are, allowing me to see my highest score of the day. The success metric I use to determine whether my meditation session is successful is how quiet and focused my mind is the rest of the day. I found that the longer, deeper and more consistently I meditate in the morning, the better my mind feels the rest of the time. Hence, I decided to use the length of time I manage to stay in a meditative state as a measure of my performance. This direct correlation might not be true for other people though as there are a lot of factors that come into play such as how long it takes one to “get in the zone” or the type of meditation.

My goal is to be able to meditate for 45 minutes straight. My plan is to find out what happens to myself and other people when using this device to reach my time goals and iterate further to adjust it. Stay tuned for updates!

Front Image Source: mindful.org

HealViz (Health Literacy App)

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Every year many people get at least one blood test done, but most of them don’t understand what the results mean. In fact, 14% of adults in the U.S have below basic health literacy. These adults are more likely to report their health as poor and lack health insurance (https://health.gov/communication/literacy/quickguide/factsbasic.htm).

To solve this problem, I worked in a team with Lucie Bolé, Simi Rajpal, and Ian Mujkherjee to build HealViz at Cornell Tech, NYC. HealViz is an iOS mobile application that provides users with graphical representations of their blood test result data, as well as definitions of medical terms and actionable insights to use this information to improve their own health.

Check out the video! For more information on the project click on the link to the repository.

Project Repository

Fall Detection Wearable

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This project consists of a low-cost wearable device specifically designed for the elderly population to detect falls and alert an emergency contact via SMS messaging. The accelerometer-based device does this by connecting to a mobile app via Bluetooth, in which the user previously input an emergency contact number, and alerting this number should a fall be detected.

I worked on a team at the University of Surrey, UK to complete this project.

Check out a video of the first prototype below! For more information on the final project send me a message to get access to the repository!