Hand medical monitoring system based on machine learning and optimal EMG feature set

Self Cite

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%.

“…For multi-sEMG characteristics, the three sEMG features of MAV, SSC, and MDF are used. 20 The experimental results are shown in Table 4 and Figure 10.…”

Section: Experimental Results Of Different Inputsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

Self Cite

“…When a material changes in an object, a new grid cell should be divided at the material change. This is done to ensure that only one material appears in each cell 54 . Nodes should be set up at the point of load concentration or sudden change in load, and the mesh density should be appropriately densified around it 55 …”

Section: Temperature Field Analysis Of New Ladle Under Typical Workinmentioning

confidence: 99%

Numerical simulation of thermal insulation and longevity performance in new lightweight ladle

Sun

Tian

et al. 2020

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For meeting the comprehensive requirements of "super insulation," "lightweight," and "longevity" of the contemporary ladle, this article designs a new kind of lightweight ladle with heat preservation and longevity performance, and based on steady-state analysis method and numerical simulation technology, the comparison of temperature distribution between new lightweight and traditional ladle under typical operating modes is made and analyzed. The simulation results of temperature field prove that the performance of heat preservation in new kind of lightweight ladle has been improved obviously from the two aspects of ladle shell temperature and molten steel file rate. At the same time, the simulation results of stress field indicate that the stress of designed lightweight ladle reduced and distributed more evenly, which is conducive to prolonging the work time in-service of the ladle. Finally, based on the field test, the simulation is proved to be effective, and the designed ladle structure achieves the expected purpose.

Enhancement ofreal‐timegrasp detection by cascaded deep convolutional neural networks

Weng

Sun

et al. 2020

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Robot grasping technology is a hot spot in robotics research. In relatively fixed industrialized scenarios, using robots to perform grabbing tasks is efficient and lasts a long time. However, in an unstructured environment, the items are diverse, the placement posture is random, and multiple objects are stacked and occluded each other, which makes it difficult for the robot to recognize the target when it is grasped and the grasp method is complicated. Therefore, we propose an accurate, real‐time robot grasp detection method based on convolutional neural networks. A cascaded two‐stage convolutional neural network model with course to fine position and attitude was established. The R‐FCN model was used as the extraction of the candidate frame of the picking position for screening and rough angle estimation, and aiming at the insufficient accuracy of the previous methods in pose detection, an Angle‐Net model is proposed to finely estimate the picking angle. Tests on the Cornell dataset and online robot experiment results show that the method can quickly calculate the optimal gripping point and posture for irregular objects with arbitrary poses and different shapes. The accuracy and real‐time performance of the detection have been improved compared to previous methods.