  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.
