Submovements are ballistic components of human motion constituting a large part of motor interaction and arising from the cyclical and overlapping cognitive processes of perception, motor planning, and motor execution. Extracting submovements is challenging as the motions tend to overlap, or start before the previous ends. We propose and evaluate use of a wavelet-inspired technique to accurately locate and parameterize submovements from one-dimensional speed time series. Our method employs a self-weighted loss refinement step to identify and improve regions of poor quality of fit, a challenge for simpler wavelet transforms. We demonstrate the accuracy of our method by presenting analysis of ~6,400 1-2s trials of synthetic egocentric camera (first-person shooter) aim data for which we know ground truth, modeled from a similarly sized real data set of 13 users. We compare our method to dual-threshold and the persistence 1D segmentation techniques and note challenges and opportunities for future improvements.

翻译：子运动是人类运动中的弹道成分，构成运动交互的很大一部分，源于感知、运动规划和运动执行的周期性重叠认知过程。提取子运动具有挑战性，因为运动往往相互重叠，或在前一个运动结束前开始。我们提出并评估了一种利用小波启发技术，从一维速度时间序列中准确定位和参数化子运动的方法。我们的方法采用自加权损失细化步骤，以识别和改善拟合质量较差的区域，这是简单小波变换面临的挑战。通过分析约6400个1-2秒的合成第一人称射击游戏视角（自我中心相机）瞄准数据试次（已知真实值，并从类似规模的13名用户真实数据集建模而来），我们展示了该方法的准确性。我们将方法与双阈值和一维持久性分割技术进行了比较，并指出了未来改进的挑战与机遇。