This project analyzed and prepared a baseline machine learning model to perform gesture recognition on data collected with MbientLab MMRL IMU rubber-banded to a two_finger ring. The final baseline model was trained with data from sessions 4, 5, and 7 consisting of 1,212 total instances of 4 gestures collected across 27 people.

When trained with a train-test split of 80:20, the model had an accuracy of 75%. The final model trained with full data (no train-test split) had reasonably robust performance in the real-time system (successfully generalized its gestures predictions to other people when integrated with the software demo app).

This report provides details on deciding on a gesture set, building and refining the gesture data collection process, and steps to integrate the model with the software iOS demo app.

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STEP (Social Technology Enterprise with Purpose) was an 400+ hour experimental course that aimed to give students real-world engineering experience within the freedoms afforded by an education-first structure. I worked on the gesture ring, designing a gesture set and building the data collection process for training a machine-learning gesture classification model.

30-second demo video:

This 20-page reflection includes criticisms and creative proposals that may be interesting to faculty developing iterations of STEP-like courses, engineering students grappling with the process of designing a process, and my future self before I set on my next AR/VR, accessibility, machine-learning, or large integrated software development project.

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