Sitting posture recognition for smart chair

Open Access
Conference Proceedings
Authors: Ren-Jieh KuoChih-Wen ShihChong-Hao Wang

Abstract: In recent years, the relationship between sitting posture and health has been paid attention to by researchers, since a person spends about 90% of a day sitting except for sleeping time, and the prolonged sitting is one of the important causes of musculoskeletal diseases. Basically, the different sitting postures caused by sitting for a long time will cause different pressure problems on the spine. Thus, this study intends to accurately predict sitting posture to reduce the damage caused by sitting posture using random forest. A smart chair with eight pressure sensors provided by a case company in Taiwan is applied to collect pressure data of various sitting postures in order to develop a prediction model to predict the sitting posture. Since random forest also owns the capability of feature extraction, it is also employed to find unnecessary sensors to reduce the cost of smart chair and further achieve higher prediction accuracy. The results showed that random forest can yield better results for the current problem compared with other methods. In addition, after the feature extraction via random forest, it can be known that there is indeed a sensor that can be eliminated. The accuracy can be enhanced from 90.70% to 91.36%.

Keywords: Smart chair, Sitting postures, Feature extraction, Random forest.

DOI: 10.54941/ahfe1003458

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