Presentation 2010-03-16
Exercise Stress Estimation in Unconstrained Measurement Environment
Daisuke OKUYA, Kurato MAENO,
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Abstract(in English) In activity recognition with acceleration sensors, it is also important to recognize the pace or exercise stress of the action in addition to recognizing the type of the action. To measure a musclar stress, there are techniques measuring a electric potential and microvibration of muscle. They are unsuitable for the stress estimate in the activity recognition because the movement of an examinee are limited to the measurement environment. In this paper, we acquired acceleration elements including microvibration of muscle using an acceleration sensor. It is used for general activety recognition usage and does not restrain the body to the measurement environment. Then we extracted the feature quantities that depend on the exercise stress. And we found that the recognition rates by machine learning that uses extracted feature amount for input is higher than that uses mean power frequency of acceleration.
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Keyword(in English) activity recognition / exercise stress estimation / accelerometer
Paper # PRMU2009-293,HIP2009-178
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Conference Date 2010/3/8(1days)
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Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Exercise Stress Estimation in Unconstrained Measurement Environment
Sub Title (in English)
Keyword(1) activity recognition
Keyword(2) exercise stress estimation
Keyword(3) accelerometer
1st Author's Name Daisuke OKUYA
1st Author's Affiliation Corporate R&D Center, Oki Electric Industry Co., Ltd.()
2nd Author's Name Kurato MAENO
2nd Author's Affiliation Corporate R&D Center, Oki Electric Industry Co., Ltd.
Date 2010-03-16
Paper # PRMU2009-293,HIP2009-178
Volume (vol) vol.109
Number (no) 471
Page pp.pp.-
#Pages 6
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