Industrial Engineering, Seoul National University, Seoul, 151-744, South Korea.
Industrial Engineering, Seoul National University, Seoul, 151-744, South Korea; Institute for Industrial Systems Innovation, Seoul National University, Seoul, 151-744, South Korea.
Appl Ergon. 2024 Jul;118:104282. doi: 10.1016/j.apergo.2024.104282. Epub 2024 Apr 3.
The objective of the current study was to explore the utilization of the decision tree (DT) algorithm to model posture-discomfort relationships at the individual level. The DT algorithm has the advantage that it makes no assumptions about the distribution of data, is robust in representing non-linear data with noise, and produces white-box models that are interpretable. Individual-level modelling is essential for examining individual-specific postural discomfort perception processes and understanding the inter-individual variability. It also has practical applications, including the development of individual-specific digital human models and more precise and informative population accommodation analysis. Individual-specific DT models were generated using postural discomfort rating data for various seated upper body postures to predict discomfort based on postural and task variables. The individual-specific DT models accurately predicted postural discomfort and revealed large inter-individual variability in the modelling results. DT modelling is expected to greatly facilitate investigating the human discomfort perception process.
本研究旨在探索决策树 (DT) 算法在个体水平上建模姿势不适关系的应用。DT 算法的优势在于它不对数据分布做出假设，能够稳健地表示具有噪声的非线性数据，并生成可解释的白盒模型。个体水平建模对于研究个体特定的姿势不适感知过程和理解个体间变异性至关重要。它还具有实际应用价值，包括开发特定于个体的数字人体模型和更精确、更具信息量的人群适应分析。使用各种坐姿上半身姿势的姿势不适评分数据生成个体特定的 DT 模型，以根据姿势和任务变量预测不适。个体特定的 DT 模型准确地预测了姿势不适，并揭示了建模结果中的个体间变异性很大。DT 建模有望极大地促进对人体不适感知过程的研究。
