To increase the efficiency of VR exposure therapies.
What it does
Currently, Optimized Exposure walks users through three use-cases of VR exposure therapy—namely acrophobia (fear of heights), thalassophobia (fear of the ocean), and arachnophobia (fear of spiders)—while logging heart rate (HR) data using Fitbit Ionic.
After the experience, HR changes are pulled from Fitbit's servers & visualized as a way to optimize the intensity of subsequent exposures. For instance, if patients experienced a large spike in HR during the experience, we would hold the intensity at its current level until HR decreases to baseline and patients are "desensitized" to this particular scenario. After desensitization, intensity would begin to ramp up until a similar spike occurred at a higher intensity level—thus efficiently targeting the patient's phobia over a single session.
Additionally, researchers may visualize how sound therapy impacts HR during such experiences. For instance, audio beats-per-minute (BPM) may act as an attractor for patient HR, thus having the potential to bias patients toward lower stress during high-intensity exposure therapy.
How we built it
- Fitbit Ionic
- python-fitbit: Fitbit API Python Client Implementation
Challenges we ran into
- Pulling user data from Fitbit databases using Fitbit API
- Fitbit synch times
Accomplishments that we're proud of
- Translating data gathered by Fitbit API into meaningful visualizations.
What we learned
- How to pull user data from Fitbit databases using Fitbit API
What's next for OptimizedExposure
- Analyze heart rate data to establish subsequent exposure intensity
- Make Unity objects responsive to such intensity values
- Use different wearables (that allow for real-time syncing)