September 18, 2010 by Post Team 

Kinesiology, Researchers at Michigan State University departments of electrical engineering and kinesiology have partnered to create a new network of wearable sensors to assess physical activity of a person and well-being.

Using technology developed by Prof. Subir Biswas Engineering, participants will wear three small sensors wireless – over their wrist, arm and leg – than in any physical activity will not only measure the frequency, intensity and time, but now the type of activity, provides valuable information. Data is then wirelessly transmitted to computer suppliers of medical services for remote evaluation and management of well being.

“This adds another dimension to how we measure physical activity,” said Karin Pfeiffer, Department of Kinesiology at MSU. “If we can not accurately measure physical activity, we can not know what is effective and what is not in the fight against the obesity and other risk factors of health. ”

The project is funded by a two-year, and 411,000 grant from the National Institutes of Health.

Although accelerometers – portable devices used to measure the movement – have become a popular tool for measuring physical activity, there are several limitations to the extent of upward movement and activities performed while standing still, among others. Biswas and Pfeiffer are the development of wireless network system to better detect various physical activities and energy expenditure measurement.

“With this technology we can now measure acceleration, inclination, posture, proximity of members to each other, all in conjunction with each other,” said Biswas.

He worked on the technology of mobile sensor networks for several years and has successfully applied to projects funded by NASA, the National Science Foundation and the U.S. Department of Agriculture.

“With the traditional approach based on accelerometry, we followed the only activity by measuring the individual movements of the body, not by their distance to the other,” said Biswas. “While an approach based on accelerometry can be used to differentiate between postures such as walking and running, it is not very effective to identify and differentiate between low-level postures such as sitting and standing. ”

Biswas has created a working prototype, and Pfeiffer soon begin to test it with students of his department. The results of the current project will allow the team to begin to explore the advanced features, such as body-statistical data processing in real time and feedback to participants.

“By detecting more information about physical activity, we can begin to fit the effective exercise programs,” said Pfeiffer. “This will help us immensely as we try to reverse some alarming trends seen in the health of children.”

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