When researchers describe data from their study, there is no accepted rule that defines the best way to represent results. Therefore, collecting and explaining results from personal study, or understanding data from the literature, is not always straightforward. These issues are even worse in the biomedical engineering field in which people with different backgrounds, usually engineers and clinicians, need to interact and exchange information.
By definition, graphical displays complement verbal discourse in written documents and in oral presentations as a more powerful form of effective redundancy. Through spatial relationships and potential richness of detail, they provide insights in ways that text cannot hope to match (Doumont 2009).
Graphs are mainly used to show comparisons between data sets, overlapping of data, compositions of data, correlations among variables, or evolutions of a variable (Best 2005).
The graph is a visually reproduced concept that is aimed to bridge the gap between meaning across languages and different research fields.
Designing good charts presents more challenges than tabular display as it draws also on the artistic talent of the scientist; in fact, the complete knowledge and understanding of own data is necessary together with a good sense of how the audience will visualize and understand the chart’s graphical elements independently from the structure of the dataset as, often, the structure of the data set largely suggests the type of graph to be selected (Coles 1997, Few 2004). Poor structure ruins otherwise effective graphs by accidentally distorting the data, making them hard to read, or distracting the readers if purposefully misleading (Doumont 2009).
Unfortunately, it could happen that researchers have a straightforward overview of their represented results while the reader can not immediately understand the meaning of the results section of these papers, especially when there are mainly graphs and images to show the outputs.
As there is no standard method to be used to show data, authors can decide which is the best method for presenting their work, but the chosen method may not be fully understandable for the readers.
Sometimes, a compromise between details and accuracy is made in showing results, especially when a big amount of data has been collected.
In literature, papers usually present data through tables, pie charts, and histograms to show all the results, but often confuse the reader or overflow him with information (Innocenti et al. 2011, Pianigiani et al. 2012).
Looking at literature about biomechanics of knee joint, a large number of studies has been published. These studies are based on different techniques such as experimental tests and in vitro tests (Arnout et al. 2014, Delport et al. 2013, Heesterbeek et al. 2014), in vivo measurements (Battaglia et al. 2014, Fregly et al. 2012, Kutzner et al. 2011), numerical analyses (Fitzpatrick and Rullkoetter 2014, Innocenti et al. 2014, Zelle et al. 2011), imaging or biologic tests (Victor et al. 2009, Worsley et al. 2011, Zdero et al. 2001). In these works, knee movements and forces are expressed in function of several motor tasks that can be performed both during daily activities and in some extreme situations, such as sport activities, and comparing healthy or pre-operative knee conditions with post-operative configurations. The method of representation is often typical for that specific area in which the analysis has been performed, but less common in other fields and sometimes also dependent on the technique in use.
Aiming to find an innovative graphical method to show datasets representing knee biomechanics, a new technique called “KneePrints” has been proposed. Thanks to its customizable nature, the KneePrints graph can be adopted for several situations and also to represent data from sensitivity analyses.
Introducing the KneePrints, the first aim is to define a new methodology to bridge the gap between making presentable and explainable data, about knee biomechanics, for the writer and so, making the readers able to manage the full flow of represented biomechanics data and to improve its comprehension.
To demonstrate the efficacy of this new method, it has been tested on an already published set of data (Innocenti et al. 2011). To evaluate the perception of different audiences in the biomedical field, a survey was proposed to surgeons and researches. In the survey, this new technique has been compared with more conventional presentation methods for the same data set. In the survey, distributed by hand and on line to international operators in the biomechanical field, a detailed description of the proposed method was also provided.