Comparison of two-stage FPCA and bayesian mixture models applied to physical activity accelerometer data : pattern identification and group classification, A
Collectively, society has begun to borderline obsess with ability to monitor our physical activity. A plethora of devices now exist to monitor steps, heart rate, sleep pattern, and many other metrics, giving individuals more and more information about their overall health. Research into physical activity and physical activity patterns has drastically increased as accelerometers’ ability to accurately capture information has grown. Often, physical activity data, collected as finely as every 0.25 seconds, is reduced into daily averages for individuals. This results in a huge loss of information ...
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