Paper Abstract and Keywords |
Presentation |
2006-05-26 11:10
Evaluation of data collection parameters on the daily activity classification with accelerometers Katsuhiro Tabuchi (ATR(Osaka Univ.)), Futoshi Naya, Ren Ohmura, Haruo Noma, Kiyoshi Kogure (ATR), Fumio Kishino (Osaka Univ.) |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In order to improve the daily activity classification with accelerometers, we have evaluated how the daily activity classification performance depends on 1) the number of sensors and their positions and 2) the sampling frequency. We have obtained the result that the classification performance of 10 kinds of daily activity is 89.8% when the accelerometers are installed in four places (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 88.9% for the same accelerometer data except that they have been re-sampled from 50 Hz to 12.5 Hz. These results can be reflected to the wearable sensor design with less user load and longer working time. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Daily activity classification / Accelerometer / Evaluation of classification performance / Sensor position / Sampling rate / / / |
Reference Info. |
IEICE Tech. Rep., vol. 106, no. 73, PRMU2006-27, pp. 43-48, May 2006. |
Paper # |
PRMU2006-27 |
Date of Issue |
2006-05-19 (PRMU, MI) |
ISSN |
Print edition: ISSN 0913-5685 |
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