Action detection

Photo of escalator

Using signatures and computer vision to classify physical human actions from real time data is an important challenge, with a range of potentially interested parties. In particular, through the Turing, the research group have linked to the Health and Safety Executive (HSE). The annual cost of work-related injuries in the UK is estimated at £4.6bn, and these injuries are often due to poor practice and training in manual handling. As the HSE currently does a lot of manual frame-by-frame video analysis at a substantial cost, real-time analysis would be of great value.

Human computer interfaces


This relates to some Chinese handwriting research. Further advances in this area are to be able to detect who has written the handwriting and also improved accelerometer analysis. As much of the computation of signatures for this must be done on simple, lightweight devices this of interest to the research group’s industrial partner ARM. Through this partnership, and with support from the Turing, the group aims to engage with other potentially parties that could be interested in this area such as Apple, Fitbit, Google etc.



low angle view of sky 256112

Radio telescopes are used to receive and study huge complex astronomical radio-wave data. Non-linearity is a crucial aspect of the way these telescopes collect and analyse their data. Rough path models have potential to help the development of measurement instruments and processing techniques used in new telescopes such as SKA in Cambridge. Our aim, in collaboration with SKA, is to improve detection sensitivity and make new observations of fast transients.

Mental health

Photo of facial expressions

Using speech and self-reported mood data from a prior clinical trial, a tool has been developed which looks in an automated way at self-reported data from individuals. When trained on the data provided across the participants in the trial, the tool captures the order in which individuals become more or less angry, anxious, elated, irritable, and sad.

The tool has already shown that, over a relatively short window of measurement, it’s possible to meaningfully position an individual on the bipolar spectrum. The sensitivity of this methodology offers the potential for its use in providing feedback for clinicians and clients.