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Non-Intrusive Physical Activity Prediction for Exergames
Computer Science and Engineering
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In recent years, exercise games have been criticized for not being able to engage their players into levels of physical activity that are high enough to yield health benefits. A major challenge in the design of exergames, however, is that it is difficult to assess the amount of physical activity an exergame yields due to limitations of existing techniques to assess energy expenditure of exergaming activities. With recent advances in commercial depth sensing technology to accurately track players' motions in 3D, we present a technique called Vizical that uses state-of the art regression algorithms to accurately predict energy expenditure in real time. Vizical may allow for creating exergames that are more vigorous to play and whose intensity can be adjusted during runtime to stimulate larger health benefits.