Scout: Improving Accessibility in Local Parks

TL;DR  we designed an XR app system to improve accessibility in the great outdoors

Problem Statement

How do we empower persons with motor impairments to better plan for and engage with local parks?

Enjoying a weekend trip to the beach or hike through the local park is commonly a quick Google search away, but for persons with motor impairments, this is often not the case. Currently, the major information disconnect between outdoor parks and users causes undue hardship for those with motor needs—such as wheelchair users, elderly users, or parents with strollers—and renders users vulnerable to harsh consequences when the data is inaccurate. Our goal was to better define the needs of this community and examine methods of bridging the information gap.

Roles

This project was completed with a 4-person team. My role focused on User Research and Design Prototyping.

Tools

1. Background Research

My team and I were initially assigned to explore methods of “helping persons with a disability visit and engage with the great outdoors”. We immediately recognized the need to narrow down this problem statement both in its use case and user population. Over the course of our background research phase, we explored how people with different impairments interact with different types of outdoor spaces as well as tested current market solutions, and arrived at our focused problem statement.

Graph with "accessibility consideration" on the x-axis and "data scale" on the y-axis. Common map apps are high on Data scale and low on accessibility consideration, while accessibility-focused apps are low on data scale and high on access consideration. Our product targets an unexplored space that is high on data scale and accessibility.

2. Understanding Users

For the initial research, we focused on incorporating users from a wide spectrum of geographic, impairment, demographic, and activity backgrounds to ensure our product could be universally designed. We used the following User Research Methods:

  1. A Qualtrics survey sent to a user group in San Diego, CA
  2. Conducted 3 Unstructured Interviews with users located in various U.S. cities
  3. Conducted 1 Contextual Inquiry study in Atlanta, GA
  4. Observation studies in San Francisco, CA and in parks in Massachusetts
  5. Task Analysis based on data collected in completed research
  6. Semi-Structured Interviews with Survey Respondent User
  7. Semi-Structured Interviews with High-activity users
  8. Semi-Structured Interviews with Stakeholders, such as partners from the National Park Service and Google Maps Accessibility
  9. Affinity Diagramming to analyze data


Using what we learned from these 6 avenues of user research, we created 4 Personas with Journey Maps and Empathy Maps to drive our design implications.

3. Low-fidelity Prototyping

Using Design Implications derived from our preliminary User Research, we decided to test designs using different modalities to determine which design could offer the most inclusiveness. We produced 3 low-fidelity prototypes total: a voice-based design, a smartwatch and companion app design, and an augmented reality (AR)-based design.

Each prototype was evaluated extensively for accessibility using WCAG 2.1 and with feedback from expert evaluators in 1-on-1 feedback sessions. Additional focus was placed on increasing customizability, decreasing requirements for fine motor control, and ensuring multi-modal input options in each of the designs.

5 smartwatch watch faces in different color schemes as seen by people with different color vision

Smartwatch Prototype Colorblindness Testing

AR Prototype Sketch with user input controls, a HUD, and potential audio cues

AR Prototype Sketch

Prototyped designs of the smartwatch interface showing compass directions and warnings about hazards on the trail aheadTwo versions of the Heads-up display map in 3d

4. High-fidelity Prototyping

After analyzing findings from the low-fidelity prototype feedback sessions, we decided to move forward with only 2 of our originally prototyped ideas. We determined that the Voice-based design simply did not allow us to fulfill all critical user’s needs, and instead integrated what we learned from evaluation of the Voice-based option into the voice-input features of the AR and Smartwatch prototypes.

With applied feedback, we updated our prototypes

5. Wireframe Feedback & Updates

Feedback sessions for updated prototypes were conducted with both users and experts. Feedback data was then compiled and analyzed again as a group, which led us to the decision of combining the AR prototype and companion app of the smartwatch prototype. Annotated Wireframes were compiled to highlight these next steps and help us identify key areas of improvement.

A collection of user interface feedback for the augmented reality interface of the app, including suggestions for toggling between colored and uncolored versions of the map and removing text from road signsA collection of user feedback on specific aspects of the user interface, such as suggestions to add information about trail material, improving customizability for personalization of needs
A spreadsheet showing feedback from both users and experts interviewed during validation testing

6. Design Evaluation

The final prototype resulting from our research is a platform with two distinct touchpoints:

  1. A smartphone application focused on assisting users with the research of park and trail accessibility, and
  2. An augmented reality (AR) companion app focused on providing in-field assistance to users through location-based alerts and information

To evaluate this newest iteration, we conducted User Evaluations with prominent accessibility advocates and professional athletes, as well as with caretakers and limb-different users. We evaluated using planned evaluation activities including:

  • A Think-Aloud with 2 planned tasks
  • Screening questions evaluating for user affect and general impressions

Expert Evaluations were also conducted using:

  • A Cognitive Walkthrough and
  • Heuristic Evaluation adapted from the W3 accessibility heuristics

Data was analyzed per evaluation group using comment-counts and plans for next steps were drafted using these findings.

Concluding Thoughts

This project is ongoing, but please feel free to check out some of the newest iterations.