| dc.description.abstract | Abstract
This study addresses the challenges of continuous Human Action Recognition (HAR) in
precision assembly environments, focusing on the limitations of camera-based systems in
recognizing small actions, transition instability, and overall accuracy. To this end, a novel
multi-module system is proposed, including a camera that recognizes body movements, a
bracelet that recognizes precise hand movements, and a secondary camera that confirms
whether hand movement recognition meets the startup conditions. The sensing signals are input
to the body and hand action recognition modules, and finally the decision-making module
integrates their outputs to form better recognition results. This research employed an
experimental approach using LEGO car assembly and electronic connector assembly tasks to
evaluate the performance of the system. Three deep learning models, AE + LSTM, LSTM +
Attention, and LSTM, were compared. The results show that the LSTM + Attention model
demonstrated superior performance in both hand and body action recognition. Also, significant
improvements in recognizing both large-scale body movements and small hand actions, and
here the wearable sensor outperforming the camera-based system in fine-action recognition.
Finally, the decision-making model effectively managed transition instability and enhanced the
overall reliability of the HAR system. This research contributes to the field of HAR by
proposing a robust solution for precision assembly environments, potentially improving safety,
efficiency, and human-robot collaboration in industrial settings. Future work should focus on
refining the algorithms to better handle noise. Additionally, emphasis should be placed on
ensuring that the HAR system is user friendly and effective in dynamic industrial settings. | en_US |