Research Internship – Data science/Machine Learning

This post aims to describe my experiences from my three-month research internship at Toshiba Research Labs, Bristol, UK and the project I have been working on (September – December 2015).

I remember the day I first went there for my interview. The building was between a wonderful small square park and a river, and it was just 5 minutes walk from the city centre. But this was not the only thing I really liked. Working there I realised the importance of the culture in a firm. I appreciated the importance of collaboration, brainstorming and creativity. It was an academia-like environment; friendly, down-to-earth people with lots of ideas and knowledge on a variety of subjects. Everyone was approachable and you could discuss with them about anything.  I could communicate with colleagues effectively without having to worry about business formalities.

The project I worked on was intriguing. That was the main reason I applied for this research internship in the first place. It combined my academic interest on Machine Learning and my personal interest on human wellbeing.  In short, the project was about Mood Recognition ar Work using Wearable devices. In other words, understand, learn and attempt to predict someone’s mood (happiness/sadness/anger/stress/boredom/tiredness/excitrment/calmness) using just a wearable device (could be a smart wristband, a chest sensor or anything that is able to capture vital signs). Sounds impossible right? How can you predict such a complicated thing as human emotions? We, as humans, are not able to understand our mood. For example, how would you say you feel right now? Happy, Sad? Ok? This is indicative of the complexity of the problem we were facing. However, we wanted to do unscripted experiments, meaning we did not want to induce any emotions to the participants of our study. We rather wanted them to wear a smart device amd log on their mood in 2-hour intervals while they were still in work as accurate as they could. Surprisingly, at least for me, there was variation in their responses in general. Some higher, some lower but all of them varied. That was encouraging.

We had to study the literature, do some research to answer the following question: How could we extract meaningful features from vital signs and accelerometer signals that will have predictive capabilities in terms of emotions? After some digging around, we found the relevant literature. It was not new concept. There were studies both in Medical literature and in Computer Science, associating heart rate with stress and skin temperature with fatigue. We wanted to take this further. We wanted to check whether a combination of all these could have a more powerful predictive ability. Intuitively, think about the times you felt stressed. Your heart might pumps faster, but sometimes your foot or hand might be shaking as well. These could be captured by the accelerometer and together could be used as an additional indicator stressful situation.

We ended up with hundreds of features, and tested a number of basic machine learning techniques, such as Decision Trees and SVMs.

Our results were good enough, comparable to those in literature. Thus, we decided to publish our findings in the PerCom 2016 conference proceedings (WristSense Workshop)(

Further, a number of ideas for patents were discussed and exciting new venues for potential work was drawn.

Overall, I would recommend an internship during a PhD programme as it is a very rewarding experience.

I would like to take this opportunity to thank all of the employees, managers, directors there for the unique experience and their confidence in me.

2 thoughts on “Research Internship – Data science/Machine Learning

  1. Good question. The biggest challenge for me was to take initiative. In particular, how to prepare and plan a user study, how to collect the data, what data and what features to use. But in the end, i really enjoyed the process. I felt valued, especially when everything glued together, producing an international-level academic paper.


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