The second prototype of the cap

A healthy lifestyle should include a daily hydration routine. But, let’s be honest, in a hyper-connected world dominated by notifications, work and family life, how good are we actually at keeping track of how much water we’ve had to drink during the day? It’s a natural reflex to drink water only when thirsty, but this is not enough. In fact, doctors have shown that forgetting to stay hydrated during the day can result in reduced concentration, dizziness and fatigue, with more serious consequences of dehydration following.

Recognizing these symptoms ourselves during busy days in College, a few of my friends and I began Wells. We developed the wellsCap, a ‘smart cap’ that would fit on a regular PET bottle, transforming it into a smart device that measures daily water intake. It securely synchronizes with an application that records and subsequently determines the ideal daily water intake based on a number of personal factors, tailoring intake specifically to an individual.

I constructed a unique, thermoelectric heat pump that was able to recycle the energy in the warm air escaping from a building into fresh air being drawn in. The device consisted of passive and active components, controlled by an embedded system built upon the Arduino platform. I also developed the connectivity aspects and successfully deployed the system.

My role in the team was as the hardware engineer, responsible for product prototyping and the measurement system. I was also in charge of driving the team forward, driving our idea forward from beyond just paper. We were recognized for our efforts, coming first place at the CISCO Switch-Up Challenge in London in 2016. After a series of individual projects during high school, this experience taught me how to work in a mixed discipline team, in order to achieve the best results.

Martin Ferianc
Martin Ferianc
PhD student at University College London

I am a final-year PhD student passionate about machine learning and Bayesian neural networks at UCL with hands-on experience from industrial/academic placements in different countries. My main expertise has revolved around convolutional neural networks and their hardware acceleration applied to computer vision tasks. I am a practical researcher with an engineering background ready to apply my diverse skillset in industry.