| Co-Investigator(Kenkyū-buntansha) |
坂村 健 東京大学, 大学院・情報学環, 教授 (10126071)
越塚 登 東京大学, 大学院・情報学環, 准教授 (40262266)
金久 博昭 東京大学, 大学院・総合文化研究科, 教授 (50161188)
ヘイ ディーン 東京大学, 大学院・情報学環, 特任講師 (30447453)
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| Budget Amount *help |
¥19,110,000 (Direct Cost: ¥14,700,000、Indirect Cost: ¥4,410,000)
Fiscal Year 2010: ¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2009: ¥4,290,000 (Direct Cost: ¥3,300,000、Indirect Cost: ¥990,000)
Fiscal Year 2008: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2007: ¥6,240,000 (Direct Cost: ¥4,800,000、Indirect Cost: ¥1,440,000)
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| Research Abstract |
By using the ubiquitous technique, the present study was conducted to establish which biosignal data; HR, Acc., and EMG best suited to be used as artificial neural network inputs for the prediction of energy expenditure and transient changes in activity intensity. The minimum peak torques during sit-to-stand: STS were determined by the simulation. Based on these results, the evaluation system was developed in STS task.
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