Inspiration
Our inspiration stemmed from a shared passion for technology and a desire to make a positive impact on the lives of individuals with limited mobility. Witnessing the uncertainty that many Canadians have towards technology, particularly AI, as highlighted in recent surveys, motivated us to bridge this gap by creating a seemingly difficult solution as high-school students. This will spread the message that technology isn’t so complex as it may seem.
What it does
The Hand Tracking System employs advanced computer vision techniques to detect and precisely locate key points or joints on a human hand. These joint positions are tracked, allowing us to monitor the user’s hand movements along the Y-axis. By analyzing the relative positions of these joints, particularly when the top joint of a finger overlaps or passes under the bottom joint of the same finger, we infer that the user has likely curled or closed their fingers or hand.
The system translates this real-time hand gesture information into a text document as a series of binary signals (1s and 0s) in the form of a continuously updating text document, all managed within a Python environment. These binary signals serve as a dynamic representation of the user’s hand and joint positions.
Furthermore, the system interfaces with an Arduino microcontroller, where the updated text document is uploaded. The Arduino processes these binary values to control servo motors. The behavior of these motors is contingent upon the values within the text document. Specifically, when the document indicates a value of “O”, it signifies that the motors should maintain their current state unless the previous value was “1” (indicating a hand closure), in which case they open. Conversely, when the document reads “1”, it commands the motors to close.
In essence, the Hand Tracking System transforms complex hand movements into a streamlined and interpretable format, enabling us to correctly guide and control the servo motors and thus, the hand.
How we built it
we built it using the cv2 and mediapipe imports on in collaboration with Arduino
Challenges we ran into
connecting two completely different systems together
Accomplishments that we're proud of
What we learned
The power of collaboration and innovation among young minds. The potential for technology to drive positive societal change and shift perceptions of complexity.
What's next for Finger movement mimic
Continuously refining and optimizing the hand tracking algorithms to enhance accuracy and responsiveness. Allowing users to exhibit a wide range of hand movements and gestures, and accommodating this variability in a reliable manner could have been challenging. The system may need to adapt to different user preferences and hand shapes. Include more precise control of servo motors to mimic hand movements accurately.
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