Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore 54000, Pakistan.
Department of Information Technology, Assosa University, Assosa 5220, Ethiopia.
Comput Intell Neurosci. 2022 Aug 18;2022:7957148. doi: 10.1155/2022/7957148. eCollection 2022.
In this research, an intelligent and cost-efficient system has been proposed to detect the improper sitting posture of a person working at a desk, mostly in offices, using machine learning classification techniques. The current era demands to avoid the harms of an improper posture as it, when prolonged, is very painful and can be fatal sometimes. This study also includes a comparison of two arrangements. Arrangement 01 includes six force-sensitive resistor (FSR) sensors alone, and it is less expensive. Arrangement 02 consists of two FSR sensors and one ultrasonic sensor embedded in the back seat of a chair. The K-nearest neighbor (KNN), Naive Bayes, logistic regression, and random forest algorithms are used to augment the gain and enhanced accuracy for posture detection. The improper postures recognized in this study are backward-leaning, forward-leaning, left-leaning, and right-leaning. The presented results validate the proposed system as the accuracy of 99.8% is achieved using a smaller number of sensors that make the proposed prototype cost-efficient with improved accuracy and lower execution time. The proposed model is of a dire need for employees working in offices or even at the residential level to make it convenient to work for hours without having severe effects of improper posture and prolonged sitting.
在这项研究中，提出了一种智能且经济高效的系统，使用机器学习分类技术来检测在办公桌前工作的人的不当坐姿，主要是在办公室中。当前时代要求避免不当姿势的危害，因为长时间保持不当姿势非常痛苦，有时甚至是致命的。本研究还包括两种方案的比较。方案 01 仅包含六个力敏电阻（FSR）传感器，价格较低。方案 02 由安装在椅子靠背中的两个 FSR 传感器和一个超声波传感器组成。使用 K-最近邻（KNN）、朴素贝叶斯、逻辑回归和随机森林算法来提高增益并提高姿势检测的准确性。本研究中识别的不当姿势包括向后倾斜、向前倾斜、向左倾斜和向右倾斜。所提出的结果验证了所提出的系统，因为使用数量较少的传感器即可达到 99.8%的准确率，从而使所提出的原型具有成本效益，并且具有更高的准确性和更短的执行时间。对于在办公室甚至在住宅环境中工作的员工来说，这种模型非常有必要，因为它可以方便员工长时间工作，而不会受到不当姿势和长时间坐姿的严重影响。
