Towards Open-World Gesture Recognition
Junxiao Shen Matthias De Lange Xuhai "Orson" Xu Enmin Zhou Ran Tan Naveen Suda Maciej Lazarewicz Per Ola Kristensson Amy Karlson Evan Strasnick
Static machine learning methods in gesture recognition assume that training
and test data come from the same underlying distribution. However, in
real-world applications involving gesture recognition on wrist-worn devices,
data distribution may change over time. We formulate this problem of adapting
recognition models to new tasks, where new data patterns emerge, as open-world
gesture recognition (OWGR). We propose leveraging continual learning to make
machine learning models adaptive to new tasks without degrading performance on
previously learned tasks. However, the exploration of parameters for questions
around when and how to train and deploy recognition models requires
time-consuming user studies and is sometimes impractical. To address this
challenge, we propose a design engineering approach that enables offline
analysis on a collected large-scale dataset with various parameters and
compares different continual learning methods. Finally, design guidelines are
provided to enhance the development of an open-world wrist-worn gesture
recognition process.