The influence of labeling techniques in classifying human manipulation movement of different speed.
In International Conference on Pattern Recognition Applications and Methods, (ICPRAM-2022), 03.2.-05.2.2022, n.n., pages 338-345, Feb/2022. ISBN: 978-989-758-549-4.
Human action recognition aims to understand and identify different human behaviors and designate appropriate
labels for each movement’s action. 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 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.
Movement Recognition, Human Movement Analysis, k-Nearest Neighbor, Convolutional Neural Networks, Extreme Gradient Boosting, Random Forest, Long Short-term Memory Networks, CNN-LSTM Network.