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.

通过设计工程方法，我们提出了一种离线分析方法，用于研究并比较利用大规模数据集进行参数探索和持续学习方法的开放式手势识别模型，以提升腕式装置上的手势识别过程的开发。