Dynamic hand gesture recognition is one of the most significant tools for human–computer interaction. In order to improve the accuracy of the dynamic hand gesture recognition, in this paper, a two-layer Bidirectional Recurrent Neural Network for the recognition of dynamic hand gestures from a Leap Motion Controller (LMC) is proposed. In addition, based on LMC, an efficient way to capture the dynamic hand gestures is identified. Dynamic hand gestures are represented by sets of feature vectors from the LMC. The proposed system has been tested on the American Sign Language (ASL) datasets with 360 samples and 480 samples, and the Handicraft-Gesture dataset, respectively. On the ASL dataset with 360 samples, the system achieves accuracies of 100% and 96.3% on the training and testing sets. On the ASL dataset with 480 samples, the system achieves accuracies of 100% and 95.2%. On the Handicraft-Gesture dataset, the system achieves accuracies of 100% and 96.7%. In addition, 5-fold, 10-fold, and Leave-One-Out cross-validation are performed on these datasets. The accuracies are 93.33%, 94.1%, and 98.33% (360 samples), 93.75%, 93.5%, and 98.13% (480 samples), and 88.66%, 90%, and 92% on ASL and Handicraft-Gesture datasets, respectively. The developed system demonstrates similar or better performance compared to other approaches in the literature.

Recently, in the field of structural health monitoring, the detection of bolted connection looseness through percussion-based method and machine learning technology has received much attention due to the advantages of removing the requirement of sensor installation and potential for automation. However, there are few such research which are performed in the underwater environment. The paper proposes a new method, Feature-reduced Multiple Random Convolution Kernel Transform (FM-ROCKET), to identify the looseness level of the underwater bolted connections based on the percussion-induced sound (audio signal). By integrating deep learning (DL) and shallow learning, the FM-ROCKET model uses the 1D convolutional layer (a DL method) to extract features from the percussion-induced audio signal and adopts the rigid classifier (linear classifier, a shallow learning method) to classify the features. Five different preload levels of the bolted flange are considered. A hammer is utilized to tap the flange surface and the continuous percussion-induced audio signal is collected by a smartphone in an underwater environment. After the audio signal segmentation, single-hit audio signals are fed into the FM-ROCKET model. To verify the effectiveness of the proposed method, three case studies are conducted on two flanges. In case study I, the proposed method slightly outperforms other DL-based methods under different training/test splitting ratios. In case studies II and III, the proposed method is far more effective than other DL-based methods on independent and different test sets. The results demonstrate the superiority of the FM-ROCKET model in the underwater detection of bolted flange looseness. To the best of our knowledge, this article is the first attempt to address the detection of bolted flange looseness in the underwater environment by combining percussion-based method, DL, and shallow learning.