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A Multi-Sensor Data Fusion Based Fitness Service Under Grid Environment

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People pay more attention to fitness to keep health in modern society. However customized exercise is difficult to spread because of lacking of resources. We designed a fitness experiment to gather multi source muscle fatigue information during fitness. In order to reduce multidisciplinary uncertainty in data process and fuse these multi-source data, a hidden markov model was put forward in this study, the observation probability was calculated as an index to coordinate conflict among the data gathered from different sensors and different disciplines. A grid service for fitness based on multidisciplinary multi-source data fusion was developed in this study. The fatigue knowledge extracted from these multi sensors was used to build grid middleware, and the middleware was implemented with multi agent supported. By being presented as fitness service under the grid environment, exerciser can be guided with customized plan according to his physical status. And finally a prototype of fitness service was developed with the methodology of evaluation of fatigue during low limb exercise in bicycle ergometer.


Document Type: Research Article


Publication date: 2011-10-01

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  • The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.
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