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

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%.

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Gesture recognition based on surface electromyography‐featureimage

Cheng

et al. 2020

Self Cite

“…Because Mobilenets-SSD network uses deep separable convolution, the resolution of feature map of back-end detection network is only half of SSD network, so the network has less computation and computational complexity. [29][30][31]…”

Section: ) Width Multiplier and Resolution Multipliermentioning

confidence: 99%

“…From the perspective of SSD back‐end detection network, both Mobilenets‐SSD and SSD network use feature extraction from six‐scale feature map for detection. Because Mobilenets‐SSD network uses deep separable convolution, the resolution of feature map of back‐end detection network is only half of SSD network, so the network has less computation and computational complexity 29‐31 …”

Section: Improved Ssd Networkmentioning

confidence: 99%

Occlusion gesture recognition based on improvedSSD

Liao

et al. 2020

Self Cite

Gesture recognition has always been a research hotspot in the field of human-computer interaction. Its purpose is to realize the natural interaction with the machine by recognizing the semantics expressed by gesture. In the process of gesture recognition, the occlusion of gesture is an inevitable problem. In the process of gesture recognition, some or even all of the gesture features will be lost due to the occlusion of the gesture, resulting in the wrong recognition or even unrecognizability of the gesture. Therefore, it is of great significance to study gesture recognition under occlusion. The single shot multibox detector (SSD) algorithm is analyzed, and the front-end network is compared. Mobilenets is selected as the front-end network, and the Mobilenets-SSD network is improved. In tensorflow environment, based on the improved network model, the self-occlusion gesture and object occluding gesture are trained in color map, depth map, and color and depth fusion respectively. The recognition models of self-occlusion gestures and object-occlusion gestures in color map, depth map, and color and depth fusion are obtained. And compare and analyze the learning rate, loss function, and average accuracy of various models obtained for occlusion gesture recognition.

“…36,37 The whole body vibration mode has been used in rehabilitation centers, fitness facilities, and clinical facilities to provide specific rehabilitation training. 38 Vibration training can not only stimulate muscles, but also stimulate other tissues such as ligaments and tendons of the human body. 39 The previous studies have focused on the effect of vibration intensity on muscle activity.…”

Section: Treadmill Type Linear Vertical Vibration Devicementioning

confidence: 99%

Compound lower limb vibration training rehabilitation robot

Yin

et al. 2020

This paper uses a bionic lower limb rehabilitation mechanism combined with a running frame structure to simulate human gait movement for lower limb rehabilitation training. The vibration excited by the vertical vibration module causes the muscle to oscillate, and the mechanical vibration excites the neuromuscular system to obtain corresponding rehabilitation functions. The robot system is modeled in three dimensions, and the static analysis and modal analysis of the running frame are carried out. The mechanism and application of vertical vibration in the key technology are clarified, and the vibration element in the vertical vibration device is analyzed. The vibration theory is deduced, and the vibration displacement figure is drawn using simulation software. The related cooperative vibration spring has also been analyzed with single-degree-of-freedom damping vibration, and the spring's center of mass movement, momentum, and position changes over time are illustrated. The design of the robot system solves the current situation of single movement of the lower limb rehabilitation robot and unsatisfactory rehabilitation effect, laying a foundation for the practical application of the subsequent lower limb rehabilitation robot system.