| 引用本文: | 赵川,余隋怀,陈晨,寸文哲,王龙.面向站立姿态的操纵舒适性评价模型研究[J].哈尔滨工业大学学报,2020,52(5):194.DOI:10.11918/201901045 |
| ZHAO Chuan,YU Suihuai,CHEN Chen,CUN Wenzhe,WANG Long.Research on the control handing comfort evaluation model for standing posture[J].Journal of Harbin Institute of Technology,2020,52(5):194.DOI:10.11918/201901045 |
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| 摘要: |
| 良好的操纵舒适性不仅减少工作中的疲劳,而且提高工作效率.针对站立姿态下操纵舒适性评价的不确定性和模糊性,构建基于T-S模糊神经网络 (Takagi-Sugeno Fuzzy Neural Network, T-S FNN)的站立姿态操纵舒适性评价模型.通过实验,收集模型的训练和测试数据.选取20名被试者参与本次实验,实验要求每个被试者完成100项目操纵任务,共有4个面板位置,每个面板上有25个圆形贴纸,代表不同操纵位置.在实验过程中分别记录被试者的关节角度、脚底压力、人体尺寸、操纵目标位置及主观舒适性数据.选取90%的实验数据对模型进行训练,10%的实验数据对所提出的方法进行验证,并与BP神经网络模型预测的主观舒适性进行比较,结果表明:T-S FNN模型具有较小的均方根误差（1.2 VS 4.5）.最后随机选取15组不同操纵任务进行检验,结果表明:该方法的预测值和实际值相关性系数为0.962（P<0.01）,与快速上肢评估（RULA）计算结果的相关性系数为0.833（P<0.01）,与工作体位分析系统（OWAS）计算结果的相关性系数为0.694（P<0.01）,说明该方法能够良好的反应真实结果. |
| 关键词: 操纵舒适性 站立姿态 T-S模糊神经网络 人机工效 |
| DOI：10.11918/201901045 |
| 分类号:TB47 |
| 文献标识码:A |
| 基金项目:工信部民机专项(MJ-2015-F-018)；高等学校学科创新引智计划B13044 |
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| Research on the control handing comfort evaluation model for standing posture |
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ZHAO Chuan,YU Suihuai,CHEN Chen,CUN Wenzhe,WANG Long
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(Shaanxi Engineering Laboratory for Industrial Design (Northwestern Polytechnical University), Xi’an 710021, China)
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| Abstract: |
| Good control handing comfort not only reduces fatigue but also improves efficiency. Aiming at the uncertainty and fuzziness of control handing comfort evaluation, this study builds up a control handing comfort evaluation model for standing posture based on Takagi-Sugeno Fuzzy Neural Network (T-S FNN). Training and testing data were collected during the experiment. Twenty adult test subjects were asked to complete 100 different operation tasks. Test subjects’ joint angle, foot pressure distribution, anthropometric dimensions, target position, and subjective comfort rating were collected during the experiment. The proposed model was trained using 90% of the data obtained from the experiment and was verified by the remaining 10% experiment data. It was then compared with the subjective comfort rating estimated by BP Neural Network. Results show that the proposed model had smaller root mean square error than BP Neural Network (1.2 vs. 4.5). Subsequently, 15 groups of different tasks were randomly selected to further test this model. Results show that the correlation coefficients between the value obtained by this model and the actual value, and those obtained by the Rapid Upper Limb Assessment (RULA) and the Ovako Working Posture Analysing System (OWAS) were 0.962 (P<0.01), 0.833 (P<0.01), and 0.694 (P<0.01), respectively. This study demonstrates that the proposed model is effective in estimating control handing comfort. |
| Key words: control handing comfort standing posture T-S Fuzzy Neural Network (T-S FNN) ergonomics |
