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- W4313476089 abstract "The development of robotic machines has shown the potential to promote automation in construction. One of the critical enablers of human–robot collaboration is a user-friendly interface to support their interactions. Compared with other interfaces, hand gesture is an effective communication channel on construction sites. This article proposes a system for recognition of construction workers’ hand gestures using wearable sensors on fingers. The system starts with synchronizing, normalizing, and smoothing finger motions. Then, the motion data are extracted through a sliding window and fed into an enhanced fully convolutional neural network (FCN) for the hand gesture recognition. The system was tested through a system validation test and achieved the precision and recall of 85.7% and 93.8%, respectively. A pilot study demonstrated the use of the proposed system to interact with a robotic dump truck. The system was further compared with vision-based recognition methods to quantitatively and qualitatively assess their relative benefits and limitations." @default.
- W4313476089 created "2023-01-06" @default.
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- W4313476089 date "2023-01-01" @default.
- W4313476089 modified "2023-09-23" @default.
- W4313476089 title "Wearable Sensors-Based Hand Gesture Recognition for Human–Robot Collaboration in Construction" @default.
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- W4313476089 doi "https://doi.org/10.1109/jsen.2022.3222801" @default.
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