Dr Tom Diethe (http://tomdiethe.com), Research Fellow for the £15 million SPHERE (http://www.irc-sphere.ac.uk) Interdisciplinary Research Collaboration (IRC) at the University of Bristol, will introduce the SPHERE project, which is designing a platform for eHealth in a smart-home context. This platform is currently being deployed into homes throughout Bristol. You may have seen the SPHERE House (http://theinstitute.ieee.org/technology-topics/smart-technology/the-sphere-house-can-monitor-its-residents-health) featured on the BBC's 'Joy of Data' documentary or in the wonderful Aardman animated overview of the project (http://www.aardman.com/work/sphere-project/).
This talk will focus on the Data Science and Machine Learning challenges and opportunities of SPHERE. Tom will discuss the implications for such an eHealth system in terms of the quantification and management of uncertainty for automated decision making in health care, gathering the necessary data to train Machine Learning models, and the importance of calibration in such systems, particularly in light of the differing operational contexts that will be encountered.
Due to well-known demographic challenges, traditional regimes of health-care are in need of re-examination. Many countries are experiencing the effects of an ageing population, which coupled with a rise in chronic health conditions is expediting a shift towards the management of a wide variety of health related issues in the home. In this context, advances in Ambient Assisted Living are providing resources to improve the experience of patients, as well as informing necessary interventions from relatives, carers and health-care professionals.
SPHERE has developed a number of different sensors that will combine to build a picture of how we live in our homes. This information can then be used to spot issues that might indicate a medical or well-being problem. The technology could help in the following ways:
• Characterise the sedentary behaviour that is linked to so many conditions.
• Detect correlations between factors such as diet and sleep.
• Measure changes in movement, posture and patterns of movement over months.
• Analyse eating behaviour - including whether people are taking prescribed medication.
• Detect periods of depression or anxiety and intervene using a computer based therapy.
• Predict falls and detect strokes so that help may be summoned.
Further details of SPHERE can be found at http://www.irc-sphere.ac.uk
Dr. Diethe is a Research Fellow on the SPHERE project at the University of Bristol, where he specialises in probabilistic methods for machine learning and data fusion, including time-series modelling of human behaviour, unsupervised, active, and transfer learning approaches. He has a Ph.D. in Machine Learning applied to multivariate signal processing from UCL, and was employed by Microsoft Research Cambridge where he co-authored a book titled “Model-Based Machine Learning”, an early access online version of which is available at http://www.mbmlbook.com. He also has significant industrial experience, with positions at QinetiQ and the British Medical Journal, during which time he has performed application-driven research, and has pioneered large-scale production-ready software engineering projects. He is a fellow of the Royal Statistical Society and a member of the IEEE Signal Processing Society. Contact him at [masked]
Note: the venue seats 50. To ensure all seats are taken on the day, we are setting a RSPV limit of 75, but admission will be strictly on a first come basis. Sorry for this arrangement but we've had waiting lists for recent events where people have not taken up their reserved place, which seems both unfair and leaving wasted seats.