Researchers at Liverpool John Moores University has developed methods for how to use information from energy use, based on household smart meter devices, to understand paterns of behaviour among elders. By using AI algoritms for interpretations of the information check on elderly people staying at home, to determine if there are changes in their behaviours. The use of different types of home appliances, kettles etc., gives rise to different patterns of energy use, and by extension could be used to determine if life style changes are emerging.


Link to article at Liverpool John Hopkins Uni.

The use of existing information, as a smart meter could be coming to every ones home soon, makes things easy. As this would be information that would come most homes, would give rise to very large data sets, and research would have a substantiell material to work with, to evaluate this approach. Feeding back information both to health care systems and the individual could give very practical information for how care is given and how to the individual monitor and change behaviour. However the feedback on the personal level would require further systems that lets the individual interact with the data. This fits very well into to the FRONT-VL approach of sensor integration and integration of information for end users.

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As usual this kind of application turns in to privacy vs. personal benefit discussion. Control over personal information always limits the reach of such systems, and in the case of mental health, good decision making over data use and deteriorating conditions might be at odds.