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Our First Prototype with Leap Motion Controller 1
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Sensor Visualization
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Second Prototype with Sensor Visualization
Inspiration
As working from home, office settings, and daily typing has become more prominent in daily lives, the importance for technological adaptations towards ergonomic needs increases. Many companies are emphasizing the importance of proper ergonomic practices by incorporating AI analysis or adjusting everyday items to correct user’s postures for long-term optimal comfort. Our project specializes in preventing wrist damage from excessive typing with improper form. With our website, users are able to adopt proper natural posture to prevent future consequences.
What it does
Het.AI utilizes Leap Motion Controller 1, Wolfram Alpha AI, and OpenAI. The Leap Motion Controller 1 is used for users to monitor their wrists' angle measurements with animations and instant feedback. User angle measurement data is defined as vector points for Wolfram Alpha AI and Open Ai to utilize. Wolfram Alpha AI computes the vector's magnitude of the user's hand movement to the optimal position for typing, also known as the origin. OpenAI produces feedback as directional output for users to follow along for adjustments in their typing position if needed.
How we built it
Het.AI uses Firebase, Leap, and Numpy Documentation. Firebase documentation is incorporated for data collection, Leap documentation is implemented to utilize the Leap Motion Controller 1, and Numpy documentation is for computing user's angle measurements. Het.AI would use Vercel however is in the prototyping phase and being hosted locally.
Challenges we ran into
One of our teammates was brand new to GitHub, and this paired with the fact that the documentation for the LeapC sensor was highly dependent on the fact that all calls had to be ran on a virtual environment, our team ran into a large issue when it came to using the sensor data. Overall, issues with familiarity rose, and provided obstacles which slowed down the production stages.
Accomplishments that we're proud of
We are proud that despite the shortcomings of this being our team's first hackathon ever, we were able to accomplish what we set out for, somewhat.
What we learned
We learned how to record and output data in real-time by using the Leap Motion Controller 1 along with the integration of several programming languages. Additionally, we learned how to incorporate Firebase documentation, AI integration, and the importance of virtual environments.
What's next for Het.Ai
In the future, Het.AI strives to utilize more modern and compact sensors to efficiently incorporate our ergonomic sensor into innovative systems. Het.AI looks forward to shrinking its current limitations with data collection and sensor sensitivity capabilities. By making these advancements, the target audience of Het.AI will widen which allows larger audiences of people to utilize a convenient way of implementing healthy ergonomic practices into their daily lives.
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