Leveraging Technology for Healthcare Transformation 716

Unique algorithm can herald a new era for smartwatches

smartwatches

Photo provided by: Hristijan Gjoreski / University of Sussex

A new algorithm has been developed by a team of scientists at the University of Sussex that enables smartwatches to detect and record your every move, including sedentary activity, which may herald a new era for smartwatches.

One of the best feature of this new algorithm is that it can even track whether you are lying or sitting down, unlike current smartwatches available in the market that need to be programmed in advance to recognise a limited number of physical activities.

“Current activity-recognition systems usually fail because they are limited to recognising a predefined set of activities, whereas of course human activities are not limited and change with time,” said Dr Hristijan Gjoreski from the University of Sussex.

This new algorithm relies on a new machine-learning approach that detects new human activities in real time. “Traditional models cluster together bursts of activity to estimate what a person has been doing, and for how long,” Dr Gjoreski added.

For an instance, a traditional smartwatch records a series of continuous steps as a walk. But these devices do not account for pauses or interruptions in the activity, and, so, a walk interrupted with two short stops would be clustered into three separate walks.

“Future smartwatches will be able to better analyse and understand our activities by automatically discovering when we engage in some new type of activity. This new method for activity discovery paints a far richer, more accurate, picture of daily human life,” said Dr Daniel Roggen, head of the Sensor Research Technology Group at the University of Sussex.

Dr Roggen is scheduled to present the new algorithm at the British Science Festival on 6th September. The research will soon be published at the International Symposium on Wearable Computers in Hawaii, US.

“As well as for fitness and lifestyle trackers, this can be used in healthcare scenarios and in fields such as consumer behaviour research,” Dr Roggen said.

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