论文标题
标签技术在分类人类操纵运动的影响不同的影响
The influence of labeling techniques in classifying human manipulation movement of different speed
论文作者
论文摘要
在这项工作中，我们研究了标签方法对使用基于标记的运动捕获系统记录的数据分类对人类运动的分类的影响。该数据集使用两种不同的方法标记，一种基于运动的视频数据，另一个基于使用运动捕获系统记录的运动轨迹。该数据集使用两种不同的方法标记，一种基于运动的视频数据，另一个基于使用运动捕获系统记录的运动轨迹。数据记录在一个执行堆叠场景的参与者中，该场景包括三种不同的速度（缓慢，正常，快速）的简单手臂运动。包括K-Nearest邻居，随机森林，极端梯度增强分类器，卷积神经网络（CNN），长期短期记忆网络（LSTM）以及CNN-LSTM网络的组合，在包括K-Near的森林，极端梯度增强分类器（CNN），卷积神经网络（CNN），卷积神经网络（CNN），卷积神经网络（CNN），卷积神经网络（CNN）（CNN），在识别这些手臂运动方面的性能中比较。对模型进行了对缓慢和正常速度移动段的动作进行培训，并在由快节奏的人类运动组成的动作上进行了概括。据观察，与使用执行实验的视频进行训练的模型相比，使用轨迹标记的正常速度数据训练的模型的准确性将近20％。
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.
