This algorithm enables the technology to discover activities as they happen, not just simply when exercising, but also when brushing your teeth or cutting vegetables. It can even track sedentary activity, for instance whether you are lying or sitting down.
Dr Hristijan Gjoreski of the University of Sussex said: "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."
"Here we present a new machine-learning approach that detects new human activities as they happen in real time, and which outperforms competing approaches."
"Traditional models ' cluster' together bursts of activity to estimate what a person has been doing, and for how long."
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.