Case Study: SPRING Analysis
Furthering Autism research through a quantitative approach to research and analysis.
Client: MIT Media Lab
Project: Spring Analysis (Autism Resource Group)
Involvement: Design Sprint Facilitator | UX | Visual Design | HTML/CSS Templates
Project Timeline: 4 weeks
The SPRING research group at the MIT Media Lab is focused on furthering Autism research through a quantitative approach to research and analysis.
They developed a smart learning device/toy (SPRING) that is focused on optimizing learning and cognitive development in children with Autism.
In addition, the smart SPRING device acts as a research platform when it paired with other sensors and videos of the research sessions.
SPRING Analysis Project
Following the development of the SPRING devise, the next step was to build a SPRING analysis application to: 1) collect the multiple time-stamped streams of data during the child’s interactions with SPRING (video of play session, child heart rate, EDA data, etc) and 2) arrange the data to allow researchers to analyze findings and correlations in the data.
We kicked off the project with a one week Design Sprint (based on the Google Venture Sprint model). I facilitated the Sprint and was joined by the client and 3 thoughtbot software engineers.
We started the Sprint by gathering information, knowledge, and history of the project; defining the problem statement; identifying key features and functionality; and mapping the primary user journey through the app.
How might we visually synchronize video, physiological data, and physical data from SPRING so that we can observe physiological, behavioral, and cognitive phenomenon in children with Autism.
With the problem identified, we talked through the current workflow of data management and the data sources (accelerometer, electrodermal activity, heart rate, videos of sessions, etc), and requested features.
Several questions and challenges the emerged early in the discussion, including, syncing start times across each data sources, plotting results, data security and speed, and the challenge of syncing and analyzing a video of the session along with up to ten streams of additional data sources.
Additionally, because of the size of the grant funding this project, we only had 4 weeks to design and build the platform.
Identify Critical Paths
Once we determined the problem-to-solve and exposed challenges and risks, we defined the Critical Path — the step-by-step map of the user’s most critical experience from start to finish.
We established two key Critical Paths:
- The Creation Experience — the researcher creates a project and uploads video and data that was collected during the session.
- The Exploration Experience — the research team analyses the session data, scans for points of interests, and exports portions of the video/data for further research.
Diverge & Converge
Day 2 and 3 of the Design Sprint were focused on generating many ideas on how to solve the problem (diverge) and then narrowing down those ideas to a single approach (converge).
This phase involved sketching and ideation exercises (e.g., “Mind Mapping”, “Crazy Eights” and “Storyboarding”). Finally, as a group, we narrowed the ideas down to one Final Story Board and identified the views to be prototyped.
For our final story board, we had to eliminate many nice-to-haves and fiercely prioritize and focus on the MVP functionality in order to complete the project within timeline and budget.
Following the Sprint group exercises, I prototyping the main user flow using Sketch and importing it into Invision app for testing.
The client reviewed and tested the prototype in Invision. Since the MIT research group were the primary users, we opted not to do additional user testing. After making a few small changes, I moved to high-fidelity designs.
Visual Design & HTML/CSS
Because of time constraints, branding and visual design exploration was minimal. As child-focused of the project, we opted for a brand direction using primary colors and a simple and friendly aesthetic.
We successfully design and launched the initial product in 5 weeks, with the hope of further grants to continue work and add additional functionality.
The initial release was piloted by the MIT Autism Resource group and allowed the research team to more quickly analyze video (interactions between child and researcher) and session data (accelerometer data, electrodermal activity data, heart rate) in order to further qualitative Autism research and optimize learning and social, cognitive, and motor development in children.