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研究生:王崇豪
研究生(外文):Chong-Hao Wang
論文名稱:運用分類方法於智慧椅坐姿之辨識
論文名稱(外文):Application of classification methods to sitting posture recognition for intelligent chair
指導教授:郭人介郭人介引用關係
指導教授(外文):Ren-Jieh Kuo
口試委員:林希偉歐陽超
口試委員(外文):Shi-Woei LinChao Ou-Yang
口試日期:2019-01-09
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:52
中文關鍵詞:智慧椅坐姿特徵擷取隨機森林支持向量機貝式分類類神經網路
外文關鍵詞:Intelligent chairSitting posturesFeature extractionRandom forestSupport vector machineBayes classifiersNeural network
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生活當中,坐姿幾乎佔據了人們所活動的時間,近幾年來健康意識的抬頭,一些研究顯示,有些疾病與長期久坐是有影響的,因為長期久坐將會產生各種不同的坐姿,有些坐姿將會影響身體的健康,因此使用智慧椅的感應器,通過分類模型監控您的坐姿,是一種非常有效的方法。為了資料的準確度,我們將分為男女個別進行資料的收集,然而在資料分析的同時將採取特徵擷取的方式,總共有8顆感應器,是否需要刪除不必要的感應器,達到智慧椅成本的節省。分析的過程本研究將進行隨機森林(Random Forest)、支持向量機(Support Vector Machine,SVM)、貝式分類(Bayes Classifiers)及類神經網路。由實驗結果顯示,本實驗較適合應用於隨機森林(Random Forest)的分類方法,並且在特徵擷取後也可以得知,確實有感應器可以刪除,節省智慧椅的成本,準確度也在特徵擷取後從96.5%提升到98.96%。
In daily life, sitting position occupies most of people’s activity time. In recent years, health has become more and more important to us. Some studies have shown that some diseases are influenced by long-term sedentary. The reason is that long-term sedentary will lead to variety of sitting postures. Some sitting postures will affect the health of the body. Therefore, using intelligent chair with sensors to monitor your siting posture by classification model is a very effective way. Thus, this study will use intelligent chair to collect sitting data. For the accuracy of the data, this study divided the data collection to men and women respectively. However, during data analysis, feature extraction is adopted. Totally, there are eight sensors used. It is necessary to delete unnecessary sensors to achieve the cost savings of the intelligent chair. In the process of recognition, this study uses random forest, support vector machine, Bayes classifiers and neural networks for comparison. The results show that random forest model can obtain better results for the current problem. In addition, after the feature extraction, it can be known that there is indeed a sensor that can be deleted. Besides, the accuracy is also improved from 96.5% to 98.96%.
摘要............................I
ABSTRACT........................II
誌謝............................III
目錄............................IV
圖目錄...........................VI
表目錄...........................VII
第一章、緒論......................1
1.1研究背景與動機.................1
1.2研究目的.......................2
1.3研究範圍與假設..................3
1.4研究流程.......................3
第二章、文獻探討...................6
2.1坐姿...........................6
2.1.1坐姿影響與分類................7
2.2 智慧椅........................11
2.3分類方法.......................13
2.3.4 貝式分類....................13
2.3.1 決策樹......................15
2.3.2 隨機森林(random forests)....19
2.3.3 支持向量機(SVM)..............20
第三章、研究方法....................22
3.1研究流程與架構...................22
3.2坐姿與智慧椅.....................23
3.3收集資料.........................26
3.4資料信度分析.....................28
3.5資料分析.........................29
3.6評估指標.........................31
第四章、結果與分析...................32
4.1實驗設計.........................32
4.2結果分析.........................33
4.2.1 男女資料比較...................34
4.2.2分類模型評估比較................35
4.2.3特徵擷取.......................39
4.2.4特徵擷取後分類模型評估比較.......40
4.2.5 混淆矩陣(confusion matrix)....41
第五章、結論與建議...................42
5.1結論.........................42
5.2貢獻.........................43
5.3未來建議......................44
參考文獻.............................45
附錄................................49
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