Classification of Children’s Sitting Postures Using Machine Learning Algorithms

机器学习 人工智能 计算机科学 支持向量机 算法 卷积神经网络 朴素贝叶斯分类器 逻辑回归 物理医学与康复 医学 病理
作者
Yong Jin Kim,Youngdoo Son,Wonjoon Kim,Byungki Jin,Myung Hwan Yun
出处
期刊:Applied sciences [MDPI AG]
卷期号:8 (8): 1280-1280 被引量:37
标识
DOI:10.3390/app8081280
摘要

Sitting on a chair in an awkward posture or sitting for a long period of time is a risk factor for musculoskeletal disorders. A postural habit that has been formed cannot be changed easily. It is important to form a proper postural habit from childhood as the lumbar disease during childhood caused by their improper posture is most likely to recur. Thus, there is a need for a monitoring system that classifies children’s sitting postures. The purpose of this paper is to develop a system for classifying sitting postures for children using machine learning algorithms. The convolutional neural network (CNN) algorithm was used in addition to the conventional algorithms: Naïve Bayes classifier (NB), decision tree (DT), neural network (NN), multinomial logistic regression (MLR), and support vector machine (SVM). To collect data for classifying sitting postures, a sensing cushion was developed by mounting a pressure sensor mat (8 × 8) inside children’s chair seat cushion. Ten children participated, and sensor data was collected by taking a static posture for the five prescribed postures. The accuracy of CNN was found to be the highest as compared with those of the other algorithms. It is expected that the comprehensive posture monitoring system would be established through future research on enhancing the classification algorithm and providing an effective feedback system.
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