Data fusion and mining in SPHERE


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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.