Skin tribology: Science friction?

The application of tribological knowledge is not just restricted to optimizing mechanical and chemical engineering problems. In fact, effective solutions to friction and wear related questions can be found in our everyday life. An important part is related to skin tribology, as the human skin is frequently one of the interacting surfaces in relative motion. People seem to solve these problems related to skin friction based upon a trial-and-error strategy and based upon on our sense for touch. The question of course rises whether or not a trained tribologist would make different choices based upon a science based strategy? In other words: Is skin friction part of the larger knowledge base that has been generated during the last decades by tribology research groups and which could be referred to as Science Friction? This paper discusses the specific nature of tribological systems that include the human skin and argues that the living nature of skin limits the use of conventional methods. Skin tribology requires in vivo, subject and anatomical location specific test methods. Current predictive friction models can only partially be applied to predict in vivo skin friction. The reason for this is found in limited understanding of the contact mechanics at the asperity level of product-skin interactions. A recently developed model gives the building blocks for enhanced understanding of friction at the micro scale. Only largely simplified power law based equations are currently available as general engineering tools. Finally, the need for friction control is illustrated by elaborating on the role of skin friction on discomfort and comfort. Surface texturing and polymer brush coatings are promising directions as they provide way and means to tailor friction in sliding contacts without the need of major changes to the produc

A multivariable model for predicting the frictional behaviour and hydration of the human skin

Background The frictional characteristics of skin-object interactions are important when handling objects, in the assessment of perception and comfort of products and materials and in the origins and prevention of skin injuries. In this study, based on statistical methods, a quantitative model is developed that describes the friction behaviour of human skin as a function of the subject characteristics, contact conditions, the properties of the counter material as well as environmental conditions. Aims Although the frictional behaviour of human skin is a multivariable problem, in literature the variables that are associated with skin friction have been studied using univariable methods. In this work, multivariable models for the static and dynamic coefficients of friction as well as for the hydration of the skin are presented. Materials & Methods A total of 634 skin-friction measurements were performed using a recently developed tribometer. Using a statistical analysis, previously defined potential influential variables were linked to the static and dynamic coefficient of friction and to the hydration of the skin, resulting in three predictive quantitative models that descibe the friction behaviour and the hydration of human skin respectively. Results and Discussion Increased dynamic coefficients of friction were obtained from older subjects, on the index finger, with materials with a higher surface energy at higher room temperatures, whereas lower dynamic coefficients of friction were obtained at lower skin temperatures, on the temple with rougher contact materials. The static coefficient of friction increased with higher skin hydration, increasing age, on the index finger, with materials with a higher surface energy and at higher ambient temperatures. The hydration of the skin was associated with the skin temperature, anatomical location, presence of hair on the skin and the relative air humidity. Conclusion Predictive models have been derived for the static and dynamic coefficient of friction using a multivariable approach. These two coefficients of friction show a strong correlation. Consequently the two multivariable models resemble, with the static coefficient of friction being on average 18% lower than the dynamic coefficient of friction. The multivariable models in this study can be used to describe the data set that was the basis for this study. Care should be taken when generalising these results