Gesture recognition based on sEMG using multi-attention mechanism for remote control

Author(s):  
Xiaodong Lv ◽  
Chuankai Dai ◽  
Haijie Liu ◽  
Ye Tian ◽  
Luyao Chen ◽  
...  
Author(s):  
Alexandru Pasarica ◽  
Casian Miron ◽  
Dragos Arotaritei ◽  
Gladiola Andruseac ◽  
Hariton Costin ◽  
...  

2011 ◽  
Vol 268-270 ◽  
pp. 1607-1612
Author(s):  
Hung Ming Chen ◽  
Po Hung Chen ◽  
Yong Zan Liou ◽  
Zhi Xiong Xu ◽  
Yeni Ouyang

This study presents a smart remote controller (SRC) framework for the Android. The Android mobile device acts as the client side of the proposed SRC software. The software uses intuitive dynamic user operation modes to send remote control commands to the controlled side by leveraging the multi-touch events, gesture recognition and hand gesture features of the Android device. The remote controlled server side is based on a Java framework. This facilities portability to PCs or networked information appliances such as Internet TVs, thus, allowing users to establish connections and translate events to control corresponding programs or actions. In this design of the proposed SRC, advanced features are categorized into various modes that can be applied to the scenarios offices and digital homes.


2021 ◽  
Vol 11 (24) ◽  
pp. 11951
Author(s):  
Kang Liu ◽  
Ying Zheng ◽  
Junyi Yang ◽  
Hong Bao ◽  
Haoming Zeng

For an automated driving system to be robust, it needs to recognize not only fixed signals such as traffic signs and traffic lights, but also gestures used by traffic police. With the aim to achieve this requirement, this paper proposes a new gesture recognition technology based on a graph convolutional network (GCN) according to an analysis of the characteristics of gestures used by Chinese traffic police. To begin, we used a spatial–temporal graph convolutional network (ST-GCN) as a base network while introducing the attention mechanism, which enhanced the effective features of gestures used by traffic police and balanced the information distribution of skeleton joints in the spatial dimension. Next, to solve the problem of the former graph structure only representing the physical structure of the human body, which cannot capture the potential effective features, this paper proposes an adaptive graph structure (AGS) model to explore the hidden feature between traffic police gesture nodes and a temporal attention mechanism (TAS) to extract features in the temporal dimension. In this paper, we established a traffic police gesture dataset, which contained 20,480 videos in total, and an ablation study was carried out to verify the effectiveness of the method we proposed. The experiment results show that the proposed method improves the accuracy of traffic police gesture recognition to a certain degree; the top-1 is 87.72%, and the top-3 is 95.26%. In addition, to validate the method’s generalization ability, we also carried out an experiment on the Kinetics–Skeleton dataset in this paper; the results show that the proposed method is better than some of the existing action-recognition algorithms.


2021 ◽  
Vol 11 (21) ◽  
pp. 9982
Author(s):  
Hongchao Zhuang ◽  
Yilu Xia ◽  
Ning Wang ◽  
Lei Dong

The combination of gesture recognition and aerospace exploration robots can realize the efficient non-contact control of the robots. In the harsh aerospace environment, the captured gesture images are usually blurred and damaged inevitably. The motion blurred images not only cause part of the transmitted information to be lost, but also affect the effect of neural network training in the later stage. To improve the speed and accuracy of motion blurred gestures recognition, the algorithm of YOLOv4 (You Only Look Once, vision 4) is studied from the two aspects of motion blurred image processing and model optimization. The DeblurGanv2 is employed to remove the motion blur of the gestures in YOLOv4 network input pictures. In terms of model structure, the K-means++ algorithm is used to cluster the priori boxes for obtaining the more appropriate size parameters of the priori boxes. The CBAM attention mechanism and SPP (spatial pyramid pooling layer) structure are added to YOLOv4 model to improve the efficiency of network learning. The dataset for network training is designed for the human–computer interaction in the aerospace space. To reduce the redundant features of the captured images and enhance the effect of model training, the Wiener filter and bilateral filter are superimposed on the blurred images in the dataset to simply remove the motion blur. The augmentation of the model is executed by imitating different environments. A YOLOv4-gesture model is built, which collaborates with K-means++ algorithm, the CBAM and SPP mechanism. A DeblurGanv2 model is built to process the input images of the YOLOv4 target recognition. The YOLOv4-motion-blur-gesture model is composed of the YOLOv4-gesture and the DeblurGanv2. The augmented and enhanced gesture data set is used to simulate the model training. The experimental results demonstrate that the YOLOv4-motion-blur-gesture model has relatively better performance. The proposed model has the high inclusiveness and accuracy recognition effect in the real-time interaction of motion blur gestures, it improves the network training speed by 30%, the target detection accuracy by 10%, and the value of mAP by about 10%. The constructed YOLOv4-motion-blur-gesture model has a stable performance. It can not only meet the real-time human–computer interaction in aerospace space under real-time complex conditions, but also can be applied to other application environments under complex backgrounds requiring real-time detection.


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