Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

Sun, Ying; Weng, Yaoqing; Luo, Bin; Li, Gongfa; Tao, Bo; Du, Jiang; Chen, Disi

doi:10.1049/iet-ipr.2020.0148

Cited by 61 publications

(45 citation statements)

References 39 publications

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“…Map Reduce is a task for each component of Hadoop input, and then call Map to calculate. In the task, the system will process the input records one by one, and then the map will be key-value after processing [31]. The form of the key-value pair will output the result.…”

Section: Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Research on intelligent medical big data system based on Hadoop and blockchain

Zhang

Wang

2021

J Wireless Com Network

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In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

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Section: Methodsmentioning

confidence: 99%

RETRACTED ARTICLE: Research on intelligent medical big data system based on Hadoop and blockchain

Zhang

Wang

2021

J Wireless Com Network

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“…Furthermore, the smaller convolution kernel is easier to get the partial abstract features than the larger convolution kernel, so that the CNN finally achieves better accuracy. [45][46][47] In the field of sEMG gesture recognition, the core idea of CNN applications is to define gesture recognition as an image classification problem. After data pre-processing and sliding window segmentation, each original sample or feature sample is converted into an image, which is then input into deep learning.…”

Section: Related Workmentioning

confidence: 99%

“…At the same time, some related works show that: under the premise of ensuring that the calculation cost is unchanged, it is found that the convolution depth of the CNN is more important than the size of the convolution kernel itself. Furthermore, the smaller convolution kernel is easier to get the partial abstract features than the larger convolution kernel, so that the CNN finally achieves better accuracy 45‐47 …”

Section: Related Workmentioning

confidence: 99%

Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

Concurrency and Computation

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For the problem of surface electromyography (sEMG) gesture recognition, considering the fact that the traditional machine learning model is susceptible to the sEMG feature extraction method, it is difficult to distinguish the subtle differences between similar gestures. The NinaPro DB1 dataset is used as the research object, and the sEMG feature image and the Convolutional Neural Network (CNN) are combined to recognize 52 gesture movements. The CNN model effectively solves the limitations of traditional machine learning in sEMG gesture recognition, and combines 1-dim convolution kernel to extract deep abstract features to improve the recognition effect. Finally, the simulation experiment shows that compared with the accuracy of the raw-sEMG images based on the CNN and the sEMG-feature-images based on the CNN and sEMG based on the traditional machine learning, the multi-sEMG-features image based on the CNN is the highest, which coming up to 82.54%.

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“…for sensing reliabilities. Generally, the entropy weight method includes several steps of object gathering, normalization of index value, determination of index weight, and calculation of synthetic index [41,42]. (24) where kn x is the n -th index value of the k -th CH.…”

Section: B Weight Value Based On Entropy Theorymentioning

confidence: 99%

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

Lin

Liu

2020

IEEE Access

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By fully utilizing the spatial gain and exploiting the multiuser diversity, cooperative spectrum sensing can enhance the sensing accuracy. In the actual wireless environment, the effect of shadowing and fading will result in the different features of signals received by the sensing nodes with different distances from primary user. As a result, some cooperative nodes in deep fading will suffer from serious missed detection, which will affect the final results during the fusing operation. To solve the above problems, a soft decision cooperative spectrum sensing with entropy weight method for cognitive radio sensor networks is presented. Initially, the sensor nodes will be organized into logical groups to obtain energy efficiency and improvement of sensing performance. After receiving the soft sensing information from all member nodes, the cluster heads employs the equal gain soft combination for inter-cluster fusion and then forwards the local decision to the fusion center. During the final decision, the entropy weight method is applied to assign optimal weight value to corresponding cluster local decisions. The simulation results show that the proposed method can outperform some typical clustering scheme for cooperative spectrum sensing in terms of the detection probability and the total error probability.

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Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images

Cited by 61 publications

References 39 publications

RETRACTED ARTICLE: Research on intelligent medical big data system based on Hadoop and blockchain

RETRACTED ARTICLE: Research on intelligent medical big data system based on Hadoop and blockchain

Gesture recognition based on surface electromyography‐featureimage

Soft Decision Cooperative Spectrum Sensing With Entropy Weight Method for Cognitive Radio Sensor Networks

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