Real-time human sitting posture detection using mobile devices

Jheanel E. Estrada, L. Vea
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引用次数: 22

Abstract

This study developed models to detect proper/ improper sitting postures using gyroscope readings from some human spinal points (thoracic, thoraco-lumbar and lumbar) through mobile devices attached at those points. It also established relationships of human body frames and proper sitting posture. The models were developed by training some well-known classifiers such as KNN, SVM, MLP, and Decision Tree using the data collected from 49 students of different body frames. Decision Tree classifier demonstrated the most promising model performance with an accuracy of 96.13% and a kappa of 0.921. Results also showed that there were relationships between body frame and posture.
利用移动设备进行实时人体坐姿检测
本研究开发了一些模型,通过连接在人体脊柱点(胸椎、胸腰椎和腰椎)的移动设备,使用陀螺仪读数来检测正确/不正确的坐姿。它还建立了人体骨架与正确坐姿的关系。该模型是通过训练KNN、SVM、MLP和Decision Tree等分类器来开发的,这些分类器来自49名不同身体框架的学生。决策树分类器显示出最有希望的模型性能,准确率为96.13%,kappa为0.921。结果还表明,身体结构和姿势之间存在关系。
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