不同采样密度下体压分布特征
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摘要
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.
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 to24×24, 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 32×32sampling 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.
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 to24×24, 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 32×32sampling 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.
引文
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[2]CILOGLU H,ALZIADEH M,MOHANY A,et al.Assessment of the whole body vibration exposure and the dynamic seat comfort in passenger aircraft[J].International Journal of Industrial Ergonomics,2015,45(7):116-123.
[3]RICHARDS L G,JACOBSON I D,KUHLTHAU AR.What the passenger contributes to passenger comfort[J].Applied Ergonomics,1978,9(3):137-142.
[4]VINK P.Aircraft interior comfort and design[M].Boca Raton:CRC Press,2011.
[5]铃木浩明,管永利.列车舒适度的评价[J].国外铁道车辆,1999(2):26-32.LINGMU hao-ming,GUAN Yong-li.The evaluation of trian comfort[J].Foreign Rolling Stock,1999(2):26-32.
[6]马佳,柯艺杰,苏强,等.汽车座椅舒适度人工智能评价方法研究[J].机械科学与技术,2011,30(3):419-422.MA Jia,KE Yi-jie,SU qiang,et al.An automobile seat comfort evalution method based on artificial intelligence[J].Mechanical Science and Technology for Aerospace Engineering,2011,30(3):419-422.
[7]LAI Y H,SUE M W,GUO L Y.A novel evaluation platform for the evaluation of anti-ulcers mattress[J].Gerontechnology,2014,13(2):232.
[8]ANDRADE Y N.An ergonomic evaluation of aircraft pilot seats[D].Daytona Beach:Embry Riddle Aeronautical University,2013.
[9]CIACCIA F R D A S,SZNELWAR L I.An approach to aircraft seat comfort using interface pressure mapping[J].Work,2012,41(Suppl.1):240-245.
[10]LI W,YU S,YANG H,et al.Effects of long-duration sitting with limited space on discomfort,body flexibility,and surface pressure[J].International Journal of Industrial Ergonomics,2017,58:12-24.
[11]STINSON M D,PORTER-ARMSTRONG A,EAKINP.Seat-interface pressure:a pilot study of the relationship to gender,body mass index,and seating position[J].Archives of Physical Medicine and Rehabilitation,2003,84(3):405-409.
[12]VOS G A,CONGLETON J J,MOORE J S,et al.Postural versus chair design impacts upon interface pressure[J].Applied Ergonomics,2006,37(5):619-628.
[13]KOLICH M,SEAL N,TABOUN S.Automobile seat comfort prediction:statistical model vs artificial neural network[J].Applied Ergonomics,2004,35(3):275-284.
[14]ZHAO C,YU S,MILLER C,et al.Predicting aircraft seat comfort using an artificial neural network[J/OL].Human Factors and Ergonomics in Manufacturing and Service Industries.https://onlinelibrary.wiley.com/doi/full/10.1002/hf m.20767.
[15]TIMM M,SAMUELSSON K.Wheelchair seating:a study on the healthy elderly[J].Scandinavian Journal of Occupational Therapy,2016,23(6):458-466.
[16]VERBUNT M,BARTNECK C.Sensing senses:tactile feedback for the prevention of decubitus ulcers[J].Applied Psychophysiology and Biofeedback,2010,35(3):243-250.
[17]WININGER M,CRANE B A.Assessment of the minimally sufficient spatial sampling in pressure mapping the wheelchair seating interface[J].Technology and Disability,2015,27(4):119-125.
[18]HEFFERNAN C,FREIVALDS A.Optimum pinch grips in the handling of dies[J].Applied Ergonomics,2000,31(4):409-414.
[19]KREMSER F,GUENZKOFER F,SEDLMEIER C,et al.Aircraft seating comfort:the influence of seat pitch on passengers’well-being[J].Work,2012,41(Suppl.1):4936-4942.
[20]ZEMP R,TAYLOR W R,LORENZETTI S.Seat pan and backrest pressure distribution while sitting in office chairs[J].Applied Ergonomics,2016,53:1-9.