Evaluation of Data Collection Parameters on the Daily Activity Classification Using Wireless Accelerometers
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- Tabuchi Katsuhiro
- Graduate School of Information Science and Technology, Osaka University:ATR Knowledge Science Laboratories
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- Naya Futoshi
- ATR Knowledge Science Laboratories
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- Ohmura Ren
- ATR Knowledge Science Laboratories
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- Noma Haruo
- ATR Knowledge Science Laboratories
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- Kogure Kiyoshi
- ATR Knowledge Science Laboratories
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- Kishino Fumio
- Graduate School of Information Science and Technology, Osaka University
Bibliographic Information
- Other Title
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- 無線加速度センサを用いた人の日常行動識別におけるデータ収集条件の影響評価(「BMI/BCI時代の心理学とVR」特集)
- 無線加速度センサを用いた人の日常行動識別におけるデータ収集条件の影響評価
- ムセン カソクド センサ オ モチイタ ヒト ノ ニチジョウ コウドウ シキベツ ニ オケル データ シュウシュウ ジョウケン ノ エイキョウ ヒョウカ
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Abstract
In order to improve the daily activity classification with wireless accelerometers worn by people, we have evaluated how the daily activity classification performance depends on 1) the number of sensors and their bodily positions and 2) the sampling frequency. We have obtained the result that the highest classification performance of 10 kinds of daily activity is 89.9% when the accelerometers are installed in four positions (the both hands neck and both ankles) and SVM (Support Vector Machine) is used as a classifier. We have also obtained the result that the classification performance is 88.6% for the same accelerometer data except that they have been re-sampled from 50 Hz to 6.25 Hz. These results can be reflected to the wearable sensor design with less user load and longer working time.
Journal
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- Transactions of the Virtual Reality Society of Japan
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Transactions of the Virtual Reality Society of Japan 12 (1), 25-35, 2007
THE VIRTUAL REALITY SOCIETY OF JAPAN
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Keywords
Details 詳細情報について
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- CRID
- 1390001204463632000
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- NII Article ID
- 110008729007
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- NII Book ID
- AA11448578
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- ISSN
- 24239593
- 1344011X
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- NDL BIB ID
- 8777724
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- Text Lang
- ja
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- Data Source
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- JaLC
- NDL
- CiNii Articles
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- Abstract License Flag
- Disallowed