1. RERC on ICT Access From Cloud to Smartphone: Empowering & Accessible ICT University of Pittsburgh & CMU Bambang Parmanto http://rercict.pitt.edu 1

2. ICT Access: Vision and Goals (1) To mitigate accessibility barriers to ICT for PwD (2) To increase access to ICT by improving its affordability and availability for underserved populations, (3) To empower PwDs by developing innovative ICT that opens access to health and vocational services. 2

3. WebAnywhere: A Cloud-Based Screen Reader No installation needed, it runs from anywhere and any computer Originally developed in 2008, updating new technologies: HTML5 and text to speech browser, expand to low vision & motor impairments 3

4. Tele-Wellness for Individuals with SCI and CP Supporting Self Management Adherence to self-care tasks 50 participants are currently enrolled in RCT 1 year trial, 2 arms Measures: health outcomes, self-management skills, social participation 4

5. mHealth for TBI Interventions 5

6. Speech-Language Training for Children in Rural Areas Speech-Language Education at home Reverse tele-therapy: – Parents can observe the session while the child is at DePaul in SLP therapy – Parent learn the ‘how-to’ by observing clinician Length: 12 months, approximately twice a month, 40-60 mins. 15 participants enrolled, 2 completed 6

7. Adaptive Accessible mHealth 7

8. Large Scale Longitudinal Web Accessibility Study Aims: – Gauge the state of the Web accessibility, monitoring progress/problems – Work on comparing categories of websites, tracking accessibility over time, Involving millions of webpages – Web Accessibility Barrier Metric & Measuring Tool Accessibility to web sites become a ‘hot topic’ in healthcare CMS’ Meaningful Use Stage 3: accessibility technology compatibility and accessibility- centered design Early adopter should enter Stage 3 starting 2016 Aims: – improve the metric (update to WCAG 2.0, incorporate other relevant work) – conduct longitudinal studies to identify accessibility trends, identify problems, & inform stakeholders 8

9. Demo of the Technologies @ RESNA Workshop Wednesday, July 13, 5 – 6 pm, “Improving QOL of PwD through Mobile Health” Thursday, July 14, 8 – 9:15 pm, “Emerging and Innovative Technologies Show and Tell” 9

10. Appendix 10

11. Keyboard Surface Interaction Turn any keyboard into a spatial touch- sensitive surface 11

12. Background New ways of delivering content in interactive way may increase accessibility barrier W3C published new guidelines to account for accessibility at very early design process, called WCAG version 2 Accessibility to web sites become a ‘hot topic’ in healthcare – CMS’ Meaningful Use Stage 3: accessibility technology compatibility and accessibility-centered design – Early adopter should enter Stage 3 starting 2016 The need to measure current accessibility – WCAG 2.0 introduces new and more detailed accessibility recommendations (does not replace WCAG v1, but extend it with more accessibility considerations) – Recommendations =/= method to evaluate accessibility – Need a strategy to evaluate and aggregate accessibility of web sites Researching better strategy and developing engine to aggregate – Most recommendations are geared for designers, difficult to identify implementation in websites without human intervention – Need advanced algorithm and strategy to mimic human judgment toward potential barriers – Need to identify which barriers would be the most appropriate to capture as measurement to indicate accessibility – Advanced machine learning algorithm combined with simple tag rules may allow crawler engine to identify potential and real accessibility barrier in a web site and aggregate the number to produce approximate accessibility barrier score (based on previous study in Web Accessibility Barrier) 12

13. Current Progress Identification of websites to sample, made into a list. We currently have two lists: US-based websites and International websites; will focus on healthcare first Completed in-depth analysis of WCAG to be used as an evaluation guideline; also includes approaches developed by popular accessibility groups Highlighted AAA recommendations for initial work with machine learning/context identification 13

14. Process 14 Approximation, Aggregation and Analysis Potential Barrier vs Actual Barrier Single Page vs Entire Website Single Website vs Website Category Website Samples Sampling the Web By Popularity By Function By Importance Identification of Parts Markup Parser ‘Pagelet’ identification, Based on WCAG recommendation Context extraction using sample website and machine learning ‘Pagelet’ identification, Based on WCAG recommendation Context extraction using sample website and machine learning Evaluation of Parts Simple Rule Machine-learning based Rule Perceivable Identification of structure and relationship between structure in a layout Perceivable Identification of structure and relationship between structure in a layout Operable Pagelet with keyboard ‘trap’ Structural purpose analysis Operable Pagelet with keyboard ‘trap’ Structural purpose analysis Understandable Language analysis Automatic pagelet content/context change analysis Error prevention/recovery mechanism analysis Understandable Language analysis Automatic pagelet content/context change analysis Error prevention/recovery mechanism analysis