Variables influencing the frictional behaviour of in vivo human skin

TLDR: The coefficient of friction increased significantly with increasing age, increasing ambient temperature and increasing relative air humidity, and a significant inversely proportional relationship was found between friction and both the amount of hair present on the skin and the height of the subject.

Abstract: In the past decades, skin friction research has focused on determining which variables are important to affect the frictional behaviour of in vivo human skin. Until now, there is still limited knowledge on these variables.This study has used a large dataset to identify the effect of variables on the human skin, subject characteristics and environmental conditions on skin friction. The data are obtained on 50 subjects (34 males and 16 females). Friction measurements represent the friction between in vivo human skin and an aluminium sample, assessed on three anatomical locations.The coefficient of friction increased significantly (p<0.05) with increasing age, increasing ambient temperature and increasing relative air humidity. A significant inversely proportional relationship was found between friction and both the amount of hair present on the skin and the height of the subject. Other outcome variables in this study were the hydration of the skin and the skin temperature. © 2013 Elsevier Ltd.

Figures

Figure 7 The differences in skin hydration between men and women, for all assessed anatomical locations.

Table 3: the median, interquartile range and range for the measured variables.

Table 8: The level of fitness influences the skin temperature.

Frequently asked questions

Q1. What are the contributions in "Variables influencing the frictional behaviour of in vivo human skin" ?

Widmaier et al. this paper identified the factors that influence the skin friction behavior, such as age, eating and drinking habits, skin layer thickness, Young 's modulus and shear strength. 

Q2. What is the nature of the relationship between the significantly related variables?

Curve fitting based on regression analysis is used to determine the nature of the relationship between the significantly related variables. 

Q3. What was used to obtain the coefficients of friction for each measurement?

Matlab (version R2011b; The MathWorks Inc., Massachusetts, USA) was used to obtain the coefficients of friction for each measurement. 

Q4. What is the nonparametric alternative for the better known t-test?

The Mann-Witney U test is the nonparametric alternative for the better known t-test and the Kruskall-Wallis test is a nonparametric ANOVA. 

Q5. What is the correlation coefficient between the coefficient of friction and the temperature of the body?

A curve fitting model indicates that only a small part of the variation in the coefficient of friction can be described with a model (R2 = 0.05 for the dynamic coefficient of friction and R2 = 0.03 for the static coefficient of friction), although the correlation coefficients indicate that increasing height is coupled to a decrease in the coefficient of friction. 

Q6. What is the importance of a large set of data for an analysis?

For such an analysis, a large set of data is essential, because with small datasets, the actual effect of variables on the coefficient of friction cannot be determined with sufficient confidence. 

Q7. What is the relationship between the coefficient of friction and the hair on the surface of the body?

It must be noted that the relationship between the coefficient of friction and the hair present on the surface of the skin is also related to the body location: the index finger pad, which has already been associated with higher coefficients of friction, is hairless.