  We present a framework for gesture customization requiring minimal examples
from users, all without degrading the performance of existing gesture sets. To
achieve this, we first deployed a large-scale study (N=500+) to collect data
and train an accelerometer-gyroscope recognition model with a cross-user
accuracy of 95.7% and a false-positive rate of 0.6 per hour when tested on
everyday non-gesture data. Next, we design a few-shot learning framework which
derives a lightweight model from our pre-trained model, enabling knowledge
transfer without performance degradation. We validate our approach through a
user study (N=20) examining on-device customization from 12 new gestures,
resulting in an average accuracy of 55.3%, 83.1%, and 87.2% on using one,
three, or five shots when adding a new gesture, while maintaining the same
recognition accuracy and false-positive rate from the pre-existing gesture set.
We further evaluate the usability of our real-time implementation with a user
experience study (N=20). Our results highlight the effectiveness, learnability,
and usability of our customization framework. Our approach paves the way for a
future where users are no longer bound to pre-existing gestures, freeing them
to creatively introduce new gestures tailored to their preferences and
abilities.
