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A smartwatch that tracks your every move

Future smartwatches will be able to better analyse and understand
 our activities by automatically discovering when we engage
in some new type of activity.
London Scientists have created a new algorithm that enables smartwatches to not only record your exercise session but also detect when you are brushing your teeth or cooking, an advance that will provide a richer and more accurate picture of your daily life.

Current smartwatches can recognise a limited number of particular activities, including yoga and running, but these are programmed in advance.

The new method, developed by researchers from University of Sussex in the UK, enables the technology to discover activities as they happen, not just simply when exercising, but also when brushing your teeth or cutting vegetables.

Traditional models "cluster" together bursts of activity to estimate what a person has been doing, and for how long, researchers said.

For example, a series of continuous steps may be clustered into a walk. Where they falter is that they 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.

The new algorithm tracks ongoing activity, paying close attention to transitioning, as well as the activity itself. In the example above, it assumes that the walk will continue following the short pauses, and therefore holds the data while it waits.

"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 Hristijan Gjoreski of the University of Sussex.

"Here we present a new machine-learning approach that detects new human activities as they happen in real time, and which outperforms competing approaches," Gjoreski said.

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 Daniel Roggen of University of Sussex.

"As well as for fitness and lifestyle trackers, this can be used in health care scenarios and in fields such as consumer behaviour research," he added.

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