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%.

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