Liu Zhuofu, Shu Zihao, Cascioli Vincenzo, McCarthy Peter W
The Higher Educational Key Laboratory for Measuring and Control Technology and Instrumentations of Heilongjiang Province, Harbin University of Science and Technology, Harbin 150080, China.
Murdoch University Chiropractic Clinic, Murdoch University, Murdoch 6150, Australia.
Sensors (Basel). 2024 Dec 2;24(23):7705. doi: 10.3390/s24237705.
Sedentary behaviors, including poor postures, are significantly detrimental to health, particularly for individuals losing motion ability. This study presents a posture detection system utilizing four force-sensitive resistors (FSRs) and two triaxial accelerometers selected after rigorous assessment for consistency and linearity. We compared various machine learning algorithms based on classification accuracy and computational efficiency. The k-nearest neighbor (KNN) algorithm demonstrated superior performance over Decision Tree, Discriminant Analysis, Naive Bayes, and Support Vector Machine (SVM). Further analysis of KNN hyperparameters revealed that the city block metric with K = 3 yielded optimal classification results. Triaxial accelerometers exhibited higher accuracy in both training (99.4%) and testing (99.0%) phases compared to FSRs (96.6% and 95.4%, respectively), with slightly reduced processing times (0.83 s vs. 0.85 s for training; 0.51 s vs. 0.54 s for testing). These findings suggest that, apart from being cost-effective and compact, triaxial accelerometers are more effective than FSRs for posture detection.
久坐行为，包括不良姿势，对健康有显著危害，尤其是对失去运动能力的个体。本研究提出了一种姿势检测系统，该系统利用四个经过严格评估以确保一致性和线性度后选定的力敏电阻（FSR）和两个三轴加速度计。我们基于分类准确率和计算效率比较了各种机器学习算法。与决策树、判别分析、朴素贝叶斯和支持向量机（SVM）相比，k近邻（KNN）算法表现出卓越的性能。对KNN超参数的进一步分析表明，采用K = 3的曼哈顿距离度量可产生最佳分类结果。与FSR相比，三轴加速度计在训练阶段（分别为99.4%和96.6%）和测试阶段（分别为99.0%和95.4%）均表现出更高的准确率，且处理时间略有缩短（训练时为0.83秒对0.85秒；测试时为0.51秒对0.54秒）。这些发现表明，除了具有成本效益和紧凑性外，三轴加速度计在姿势检测方面比FSR更有效。
