Introduction: statistical charts are key to data literacy and essential in the process of communicating abstract science concepts and relationships that are difficult to understand through other representations. These types of visualization can present numerous challenges for users with low vision, an often-overlooked group despite its enormous prevalence. To evaluate MS Excel's as an accessible authoring tool analyzing ATAG 2.0 compliance, and its capability to create accessible charts according to a set of heuristic indicators proposed by the authors. Methodology: Evaluate a) the compliance of Microsoft Excel software as an authoring tool with ATAG 2.0 guidelines; and b) the compliance of MS Excel-generated charts with a domain heuristic set. For the heuristic evaluation, apart from the original MS Excel chart (XSLX), three exported versions (DOCX, HTML and SVG) of the same chart were created using all the accessibility features available in the software. Results: Regarding ATAG compliance, 48 desired accessibility features applying to MS Excel have been identified and the software meet just 26 of them (54.17%). Regarding heuristic evaluation, the four versions of the chart present a percentage of compliance with the heuristics equal to or greater than 66.66%. The versions that have obtained the best scores are MS Excel original and MS Word exported charts with 72.2% of indicators achieved, followed by SVG and HTML exported charts, both with 66.66%. Even though MS Excel does not meet a good part of the ATAG 2.0 success criteria, it has been possible to create charts with a good level of accessibility following the suggested heuristic principles.
Cervical range of motion (ROM) is a crucial aspect of assessment following a neck injury and prior to cervical rehabilitation. We explored using an exergame with a head-tracker to predict the degree of cervical ROM. Using head movement, users moved a cursor over a picture-reveal puzzle to remove tiles and reveal an underlying picture. In a within-subjects user study, we controlled mobility restriction by fitting participants with either a rigid cervical collar (severe restriction), a soft cervical collar (moderate restriction), or no collar (no restriction). We also controlled task difficulty through two levels each of number of tiles (13x10, 7x5) and gain (high, low). Selection rate by mobility restriction ranged from approximate to 30% for severe to approximate to 95% with none, and approximate to 50% for moderate. Results suggest the following ascending ranks for difficulty based on number of tiles and gain: (1) 7x5, high gain, (2) 7x5, low gain, (3) 13x10, high gain, and (4) 13x10, low gain. This ascending difficulty order is recommended for presenting the puzzles to people with cervical conditions to avoid overexertion. The collected data were also used in machine learning with a Random Forest model. Mobility restriction category (severe, moderate, none) was correctly predicted in 80.6% of 36 samples. The results are a first step in using an exergame and machine learning to automatically categorize patients according to their cervical ROM.
