智能座椅的坐姿识别

Ren-jieh Kuo, Chih-Wen Shih, Chong-Hao Wang
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引用次数: 0

摘要

近年来,坐姿与健康的关系受到研究人员的关注,因为一个人一天中除了睡觉时间外,约有90%的时间是坐着的,而长时间的坐着是导致肌肉骨骼疾病的重要原因之一。基本上,久坐造成的不同坐姿会对脊柱造成不同的压力问题。因此,本研究旨在利用随机森林对坐姿进行准确预测,以减少坐姿对人体造成的损害。采用台湾case公司提供的带有8个压力传感器的智能椅子,收集各种坐姿的压力数据,建立预测模型,预测坐姿。由于随机森林还具有特征提取的能力,因此还可以利用随机森林来寻找不需要的传感器,从而降低智能椅子的成本,进一步达到更高的预测精度。结果表明,与其他方法相比,随机森林方法对当前问题的求解效果更好。另外,通过随机森林进行特征提取后,可以知道确实存在一个可以被淘汰的传感器。准确度由90.70%提高到91.36%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sitting posture recognition for smart chair
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%.
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