Movement-type Classification by Using Acceleration
碩士 === 朝陽科技大學 === 資訊管理系 === 102 === Road running is getting popular in Taiwan, most of people contact this activity through their social circle. In moving procedure, rookies and trainee are hard to keep their limbs in a stable and regular oscillation when they feel weary. It is a big challenge for t...
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ndltd-TW-102CYUT03960182016-03-11T04:12:46Z http://ndltd.ncl.edu.tw/handle/30188853270470215041 Movement-type Classification by Using Acceleration 加速度感測器辨識運動型態之研究 Jyun-Han Liou 柳君翰 碩士 朝陽科技大學 資訊管理系 102 Road running is getting popular in Taiwan, most of people contact this activity through their social circle. In moving procedure, rookies and trainee are hard to keep their limbs in a stable and regular oscillation when they feel weary. It is a big challenge for them even walking and running are the human being’s innate ability. In the case of they are easily interaction their feet-steps and the movement will stop or getting slower, that make them feel more strenuous. Therefore, there are not only keep a suitable mood is training task but also do the progressive movement training. However, the moving procedure should be quantifiable and presentable that can improve any particular in the motion. In previous study, there are few research aim at observe the feature in different movement-type, but we believe to extract the feature which can distinguish different movement-type is a key to achieve the objective. Therefore, this research aims at: 1) to find any practicable features, 2) to suit the features to the classification by the accelerometer. This research propose two way to classify movement-type, one is just to judge walking and running, the other can also distinguish go up and down stairs movement-type. This research raises a way to be a basement of classify a complex movement procedure. And the feature is extract by counting local maximum and Fourier analysis. Chu-Hui Lee 李朱慧 2014 學位論文 ; thesis 42 zh-TW |
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碩士 === 朝陽科技大學 === 資訊管理系 === 102 === Road running is getting popular in Taiwan, most of people contact this activity through their social circle. In moving procedure, rookies and trainee are hard to keep their limbs in a stable and regular oscillation when they feel weary. It is a big challenge for them even walking and running are the human being’s innate ability. In the case of they are easily interaction their feet-steps and the movement will stop or getting slower, that make them feel more strenuous. Therefore, there are not only keep a suitable mood is training task but also do the progressive movement training.
However, the moving procedure should be quantifiable and presentable that can improve any particular in the motion. In previous study, there are few research aim at observe the feature in different movement-type, but we believe to extract the feature which can distinguish different movement-type is a key to achieve the objective. Therefore, this research aims at: 1) to find any practicable features, 2) to suit the features to the classification by the accelerometer. This research propose two way to classify movement-type, one is just to judge walking and running, the other can also distinguish go up and down stairs movement-type. This research raises a way to be a basement of classify a complex movement procedure. And the feature is extract by counting local maximum and Fourier analysis.
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author2 |
Chu-Hui Lee |
author_facet |
Chu-Hui Lee Jyun-Han Liou 柳君翰 |
author |
Jyun-Han Liou 柳君翰 |
spellingShingle |
Jyun-Han Liou 柳君翰 Movement-type Classification by Using Acceleration |
author_sort |
Jyun-Han Liou |
title |
Movement-type Classification by Using Acceleration |
title_short |
Movement-type Classification by Using Acceleration |
title_full |
Movement-type Classification by Using Acceleration |
title_fullStr |
Movement-type Classification by Using Acceleration |
title_full_unstemmed |
Movement-type Classification by Using Acceleration |
title_sort |
movement-type classification by using acceleration |
publishDate |
2014 |
url |
http://ndltd.ncl.edu.tw/handle/30188853270470215041 |
work_keys_str_mv |
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