In this work, we investigate the influence of labeling methods on the classification of human movements on data recorded using a marker-based motion capture system. The dataset is labeled using two different approaches, one based on video data of the movements, the other based on the movement trajectories recorded using the motion capture system. The dataset is labeled using two different approaches, one based on video data of the movements, the other based on the movement trajectories recorded using the motion capture system. The data was recorded from one participant performing a stacking scenario comprising simple arm movements at three different speeds (slow, normal, fast). Machine learning algorithms that include k-Nearest Neighbor, Random Forest, Extreme Gradient Boosting classifier, Convolutional Neural networks (CNN), Long Short-Term Memory networks (LSTM), and a combination of CNN-LSTM networks are compared on their performance in recognition of these arm movements. The models were trained on actions performed on slow and normal speed movements segments and generalized on actions consisting of fast-paced human movement. It was observed that all the models trained on normal-paced data labeled using trajectories have almost 20% improvement in accuracy on test data in comparison to the models trained on data labeled using videos of the performed experiments.

本研究探讨了在使用基于标记的动作捕捉系统记录的数据中，标注方法对于人体动作分类的影响，并比较了基于视频标注和运动轨迹标注方式的监督机器学习算法在识别不同速度人体动作时的表现，结果表明，基于运动轨迹标注的模型在识别中速和正常速度人体动作时相对于基于视频标注的模型有着20％左右的准确率提高。

标签技术对不同速度的人类操纵运动分类的影响