We study the task of gesture recognition from electromyography (EMG), with
the goal of enabling expressive human-computer interaction at high accuracy,
while minimizing the time required for new subjects to provide calibration
data. To fulfill these goals, we define combination gestures consisting of a
direction component and a modifier component. New subjects only demonstrate the
single component gestures and we seek to extrapolate from these to all possible
single or combination gestures. We extrapolate to unseen combination gestures
by combining the feature vectors of real single gestures to produce synthetic
training data. This strategy allows us to provide a large and flexible gesture
vocabulary, while not requiring new subjects to demonstrate combinatorially
many example gestures. We pre-train an encoder and a combination operator using
self-supervision, so that we can produce useful synthetic training data for
unseen test subjects. To evaluate the proposed method, we collect a real-world
EMG dataset, and measure the effect of augmented supervision against two
baselines: a partially-supervised model trained with only single gesture data
from the unseen subject, and a fully-supervised model trained with real single
and real combination gesture data from the unseen subject. We find that the
proposed method provides a dramatic improvement over the partially-supervised
model, and achieves a useful classification accuracy that in some cases
approaches the performance of the fully-supervised model.

通过使用依据动作方向和修改因素定义的综合手势，从而快速实现高准确度的表情人机交互，且最小化了新受试者提供校准数据的时间。通过将真实的单一手势特征向量结合生成合成训练数据，我们从未见过的组合手势进行外推，这样的策略构建了一个大而灵活的手势词汇，而无需新受试者演示大量的示例手势。通过自我监督，我们预先训练了一个编码器和组合操作符，为未见受试者提供有用的合成训练数据。通过收集真实世界的电肌图数据集，并与两个基线方法进行对比评估：仅使用未见受试者的真实单一手势数据进行部分监督训练的模型，以及使用未见受试者的真实单一手势和真实组合手势数据进行完全监督训练的模型。研究结果表明，所提出的方法明显优于部分监督模型，并且在某些情况下达到了接近完全监督模型性能的有用分类准确度。