Deep Learning CNN-LSTM Approach for Identifying Welder’s Hand Motion Gestures Using Wearable Sensors
A welder plays a crucial role in the construction of a new ship. Monitoring the performance of welders involved in the construction of a ship is particularly important for controlling dimensions. he competency and consistency of welders play a vital role, with specific hand movement patterns contrib...
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| Veröffentlicht in: | IOP conference series. Earth and environmental science 2024-12, Vol.1423 (1), p.12036 |
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| creator | Virmansyah, Vialdo Muhammad Pribadi, Triwilaswandio Wuruk Baihaqi, Imam |
| description | A welder plays a crucial role in the construction of a new ship. Monitoring the performance of welders involved in the construction of a ship is particularly important for controlling dimensions. he competency and consistency of welders play a vital role, with specific hand movement patterns contributing to welding quality. This study aims to identify individual welders’ hand movement patterns using Deep Learning with CNN-LSTM configurations. Data was collected through IMU motion sensors attached to welders’ wrists, capturing acceleration, angular speed, magnetic force, and electric current changes. The data was classified using CNN-LSTM, which showed higher accuracy than SVM methods. Experimental results indicated an overall classification accuracy of 99.73% for identifying individual welders and 97.07% for determining welding positions. |
| doi_str_mv | 10.1088/1755-1315/1423/1/012036 |
| format | Article |
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| subjects | Acceleration Angular speed Convolutional Neural Network-Long Short Term Memory Deep Learning Hand (anatomy) Machine learning Magnetic fields Motion sensors Movement Sensors Wearable Device Welding Welding machines |
| title | Deep Learning CNN-LSTM Approach for Identifying Welder’s Hand Motion Gestures Using Wearable Sensors |
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