Mechanical Engineering |
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Body pressure distribution characteristics in different sampling densities |
Chuan ZHAO1( ),Sui-huai YU1,*( ),Lei WANG2,Wen-hua LI1 |
1. Shaanxi Engineering Laboratory for Industrial Design, Northwestern Polytechnical University, Xi'an 710072, China 2. China Aviation Industry First Aircraft Design Institute, Xi'an 710089, China |
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Abstract The raw data with sampling density of 32×32 was spatially filtered to eliminate noise, in order to enhance the continuity of data distribution between independent sensors. Then sampling density of data was decreased to 24×24, 16×16, and 8×8, respectively. Four common features (mean pressure, maximum pressure, mean pressure gradient, and maximum pressure gradient) were calculated at each sampling density. The one-way ANOVA analysis showed that the differences of mean values between 32×32 sampling density and 24×24 as well as 16×16 sampling densities were small (1.1 mmHg, 2.6 mmHg), but the difference of mean value between 32×32 and 8×8 sampling densities was big (9.0 mmHg). Spearman correlation analysis revealed that the four common features of 32×32 sampling density had high correlation with that of 24×24, 16×16, and 8×8 sampling densities (P<0.05). The highest was the peak pressure correlation (0.99,P<0.05) between the 32 ×32 and 24 ×24 sampling densities, and the lowest was the mean pressure gradient correlation (0.55,P<0.05) between the 32×32 and 8×8 sampling densities. The test results showed that the pressure mat with the sampling density of 24×24 and 16×16 can provide accurate body pressure distribution characteristics.
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Received: 24 January 2018
Published: 21 February 2019
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Corresponding Authors:
Sui-huai YU
E-mail: zhaochuancow@gmail.com;ysuihuai@vip.sina.com
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不同采样密度下体压分布特征
对压力座垫原始数据(采样密度为32×32)进行空域滤波处理剔除噪声,增强独立传感器之间数据分布的连续性. 将体压分布采样密度降至24×24、16×16、8×8,提取不同采样密度下的平均压力、峰值压力、平均压力梯度和峰值压力梯度进行分析. 单因素方差分析结果表明,32×32采样密度与24×24、16×16采样密度下的特征均值之间的差异较小(1.1、2.6 mmHg),与8×8采样密度下的特征均值之间的差异较大(9.0 mmHg). 斯皮尔曼相关性分析结果表明,32×32与24×24、16×16、8×8采样密度下的平均压力、峰值压力、平均压力梯度和峰值压力梯度具有较高的相关性(P<0.05). 其中相关性最高的为与24×24采样密度下的峰值压力(0.99,P<0.05),相关性最低的为与8×8采样密度下的平均压力梯度(0.55,P<0.05). 试验结果表明采样密度为24×24和16×16的压力座垫可以提供精确的体压分布特征.
关键词:
压力座垫,
乘坐舒适性,
体压分布,
飞机座椅,
数据简化
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