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学者姓名：蔡英杰
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Abstract ：
本发明公开了一种具有改进渐开线型罗茨转子型线的转子及其设计方法，其中具有改进渐开线型罗茨转子型线的转子包括转子本体和布设在转子本体上且沿圆周方向均布的三个端头，每个端头由若干段线段构成，所述端头为轴对称叶形，且所述端头的对称半边由AB段齿谷圆弧、BC段渐开线、CD段偏心圆弧和DE段直线四部分组成。本发明的罗茨鼓风机转子型线的径距比达1.61，面积利用系数达0.603，具有较好的密封性的同时且可以提供更大的风量，提高了罗茨鼓风机的效率；同时保证了转子配合过程中能够实现全段的啮合和曲线的平滑过渡，结构简单，设计优化，具有较强的实用性。
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| GB/T 7714 | 蔡英杰 , 胡强 , 姚立纲 et al. 具有改进渐开线型罗茨转子型线的转子及其设计方法 : CN202210863309.8[P]. | 2022-07-20 00:00:00 . |
| MLA | 蔡英杰 et al. "具有改进渐开线型罗茨转子型线的转子及其设计方法" : CN202210863309.8. | 2022-07-20 00:00:00 . |
| APA | 蔡英杰 , 胡强 , 姚立纲 , 杨仁义 , 周浩 . 具有改进渐开线型罗茨转子型线的转子及其设计方法 : CN202210863309.8. | 2022-07-20 00:00:00 . |
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Abstract ：
The gesture recognition (GR) technology as one of the human-machine interfaces can conveniently and effectively express the intention of human and has become the hot research hot spot in recent years. Force level is a key factor while GR for more dexterous and natural prosthetic control. To provide researchers with a systematic and quick overview of the relevant and future developments in GR and force assessment (FA) techniques, this review synthesizes current commonly used sensor interfaces, data processing methods, and methods that have improved recognition performance. The experimental design and related results of GR and FA with various types of sensors are analyzed and compared to understand the scope of application and recognition performance of different sensors. This review summarizes the challenges and future work in the five areas of hardware, use environment, broad applicability, physiological factors, and comfort of use in practical applications. Finally, the conclusion prospects that future research may need to focus on improving model generalization and robustness to environmental, physiological factors, and so on by building large datasets and developing flexible, long-lasting, lightweight, and senseless, high-performance interfaces.
Keyword ：
Feature extraction Feature extraction Force Force Force assessment (FA) Force assessment (FA) Gesture recognition Gesture recognition gesture recognition (GR) gesture recognition (GR) human-machine interaction (HMI) human-machine interaction (HMI) Image segmentation Image segmentation Nails Nails sensors sensors Sensors Sensors signal acquisition signal acquisition Support vector machines Support vector machines
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| GB/T 7714 | Lu Zongxing , He Baizheng , Cai Yingjie et al. Human-Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review [J]. | IEEE SENSORS JOURNAL , 2023 , 23 (22) : 26981-26996 . |
| MLA | Lu Zongxing et al. "Human-Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review" . | IEEE SENSORS JOURNAL 23 . 22 (2023) : 26981-26996 . |
| APA | Lu Zongxing , He Baizheng , Cai Yingjie , Chen Bingxing , Yao Ligang , Huang Haibin et al. Human-Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review . | IEEE SENSORS JOURNAL , 2023 , 23 (22) , 26981-26996 . |
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Abstract ：
The gesture recognition (GR) technology as one of the human-machine interfaces can conveniently and effectively express the intention of human and has become the hot research hot spot in recent years. Force level is a key factor while GR for more dexterous and natural prosthetic control. To provide researchers with a systematic and quick overview of the relevant and future developments in GR and force assessment (FA) techniques, this review synthesizes current commonly used sensor interfaces, data processing methods, and methods that have improved recognition performance. The experimental design and related results of GR and FA with various types of sensors are analyzed and compared to understand the scope of application and recognition performance of different sensors. This review summarizes the challenges and future work in the five areas of hardware, use environment, broad applicability, physiological factors, and comfort of use in practical applications. Finally, the conclusion prospects that future research may need to focus on improving model generalization and robustness to environmental, physiological factors, and so on by building large datasets and developing flexible, long-lasting, lightweight, and senseless, high-performance interfaces.
Keyword ：
Force assessment (FA) Force assessment (FA) gesture recognition (GR) gesture recognition (GR) human-machine interaction (HMI) human-machine interaction (HMI) sensors sensors signal acquisition signal acquisition
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| GB/T 7714 | Lu, Zongxing , He, Baizheng , Cai, Yingjie et al. Human-Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review [J]. | IEEE SENSORS JOURNAL , 2023 , 23 (22) : 26981-26996 . |
| MLA | Lu, Zongxing et al. "Human-Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review" . | IEEE SENSORS JOURNAL 23 . 22 (2023) : 26981-26996 . |
| APA | Lu, Zongxing , He, Baizheng , Cai, Yingjie , Chen, Bingxing , Yao, Ligang , Huang, Haibin et al. Human-Machine Interaction Technology for Simultaneous Gesture Recognition and Force Assessment: A Review . | IEEE SENSORS JOURNAL , 2023 , 23 (22) , 26981-26996 . |
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In this paper, we propose a pneumatic double-joint soft actuator based on fiber winding and build a dexterous hand with 11 degrees of freedom. Firstly, soft actuator structural design is carried out according to the actuator driving principle and gives the specific manufacturing process. Then, an experimental analysis of the bending performance of a single soft actuator, including bending angle, speed, and force magnitude, is carried out by building a pneumatic control experimental platform. Finally, a series of dexterous robotic hand-grasping experiments is conducted. Different grasping methods are used to catch the objects and measure the objects' change in height, length, and rotation angle during the experiment. The results show that the proposed soft actuator is more consistent with the bending rule of human fingers, and that the gestures of the dexterous hand are more imaginable and flexible when grasping objects. The soft actuator can carry out horizontal and vertical movements, and rotation of the object in the dexterous hand, thus achieving better human-computer interaction.
Keyword ：
bend performance bend performance dexterous robotic hand dexterous robotic hand double-joint soft actuator double-joint soft actuator
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| GB/T 7714 | Chen, Bingxing , Meng, Qiuxu , Wang, Junjie et al. Experimental Study on Double-Joint Soft Actuator and Its Dexterous Hand [J]. | MICROMACHINES , 2023 , 14 (10) . |
| MLA | Chen, Bingxing et al. "Experimental Study on Double-Joint Soft Actuator and Its Dexterous Hand" . | MICROMACHINES 14 . 10 (2023) . |
| APA | Chen, Bingxing , Meng, Qiuxu , Wang, Junjie , Lu, Zongxing , Cai, Yingjie . Experimental Study on Double-Joint Soft Actuator and Its Dexterous Hand . | MICROMACHINES , 2023 , 14 (10) . |
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The existing Human-Machine Interfaces (HMI) based on gesture recognition using surface electromyography (sEMG) have made significant progress. However, the sEMG has inherent limitations as well as the gesture classification and force estimation have not been effectively combined. There are limitations in applications such as prosthetic control and clinical rehabilitation, etc. In this paper, a grasping gesture and force recognition strategy based on wearable A-mode ultrasound and two-stage cascade model is proposed, which can simultaneously estimate the force while classifying the grasping gesture. This paper experiments five grasping gestures and four force levels (5-50%MVC). The results demonstrate that the performance of the proposed model is significantly better than that of the traditional model both in classification and regression (p < 0.001). Additionally, the two-stage cascade regression model (TSCRM) used the Gaussian Process regression model (GPR) with the mean and standard deviation (MSD) feature obtains excellent results, with normalized root-mean-square error (nRMSE) and correlation coefficient (CC) of 0.10490.0374 and 0.94610.0354, respectively. Besides, the latency of the model meets the requirement of real-time recognition (T < 15ms). Therefore, the research outcomes prove the feasibility of the proposed recognition strategy and provide a reference for the field of prosthetic control, etc.
Keyword ：
cascade model cascade model Estimation Estimation Force Force force estimation force estimation Gesture classification Gesture classification Grasping Grasping Muscles Muscles Probes Probes Thumb Thumb Ultrasonic imaging Ultrasonic imaging wearable A-mode ultrasound wearable A-mode ultrasound
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| GB/T 7714 | Qing Zengyu , Lu Zongxing , Liu Zhoujie et al. A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model [J]. | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2022 , 30 : 2301-2311 . |
| MLA | Qing Zengyu et al. "A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model" . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 30 (2022) : 2301-2311 . |
| APA | Qing Zengyu , Lu Zongxing , Liu Zhoujie , Cai Yingjie , Cai Shaoxiong , He Baizheng et al. A Simultaneous Gesture Classification and Force Estimation Strategy Based on Wearable A-Mode Ultrasound and Cascade Model . | IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING , 2022 , 30 , 2301-2311 . |
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Abstract ：
本实用新型公开了一种具有改进渐开线型罗茨转子型线的转子，包括转子本体和布设在转子本体上且沿圆周方向均布的三个端头，每个端头由若干段线段构成，所述端头为轴对称叶形，且所述端头的对称半边由AB段齿谷圆弧、BC段渐开线、CD段偏心圆弧和DE段直线四部分组成。本实用新型的罗茨鼓风机转子型线的径距比达1.61，面积利用系数达0.603，具有较好的密封性的同时且可以提供更大的风量，提高了罗茨鼓风机的效率；同时保证了转子配合过程中能够实现全段的啮合和曲线的平滑过渡，结构简单，设计优化，具有较强的实用性。
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 蔡英杰 , 胡强 , 姚立纲 et al. 具有改进渐开线型罗茨转子型线的转子 : CN202221889013.5[P]. | 2022-07-20 . |
| MLA | 蔡英杰 et al. "具有改进渐开线型罗茨转子型线的转子" : CN202221889013.5. | 2022-07-20 . |
| APA | 蔡英杰 , 胡强 , 姚立纲 , 杨仁义 , 周浩 . 具有改进渐开线型罗茨转子型线的转子 : CN202221889013.5. | 2022-07-20 . |
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本实用新型提供一种加工罗茨鼓风机扭叶转子可转位盘形铣刀，包括基准圆管以及多个沿其周向设置在基准圆管外侧的刀片架，相邻的刀片架之间设置有固定块，每个刀片架外侧设有用以可拆安装圆形刀片的圆形刀片定位槽，所述圆形刀片定位槽两侧设置有用以可拆安装方形刀片的方形定位槽。本实用新型设计合理，构造简单，使用方便，可大幅提高加工效率，降低制造成本，而且可单独更换磨损刀片，延长刀具使用寿命。
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| GB/T 7714 | 蔡英杰 , 胡强 , 董朝乾 et al. 一种加工罗茨鼓风机扭叶转子可转位盘形铣刀 : CN202222226045.3[P]. | 2022-08-24 . |
| MLA | 蔡英杰 et al. "一种加工罗茨鼓风机扭叶转子可转位盘形铣刀" : CN202222226045.3. | 2022-08-24 . |
| APA | 蔡英杰 , 胡强 , 董朝乾 , 姚立纲 , 周浩 . 一种加工罗茨鼓风机扭叶转子可转位盘形铣刀 : CN202222226045.3. | 2022-08-24 . |
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本实用新型涉及一种异型管钻孔专用机床中工件压紧夹具，包括底部支撑机构、侧向夹紧机构以及管口压紧机构，底部支撑机构具有若干个沿纵向间隔分布的并用于与弯管的弯曲段相配合的底部定位槽；侧向夹紧机构包含设于底部支撑机构左、右两侧的两个夹紧件，两个紧件由驱动机构驱使相向或背向移动，夹紧件靠近底部支撑机构的一端设有与若干个底部定位槽的位置相对的若干个侧向定位槽，侧向定位槽用于与弯管的竖直段相配合；管口压紧机构包含设于底部支撑机构左、右两侧的两个上压板，上压板位于夹紧件的上方并用于压设在弯管的管口端面。本实用新型设计合理，可同时实现若干根床头弯管的可靠定位，大大提高了弯管的开孔效率，具有很好的市场应用推广价值。
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| GB/T 7714 | 蔡英杰 , 胡强 , 姚立纲 et al. 异型管钻孔专用机床中工件压紧夹具 : CN202121182033.4[P]. | 2021-05-31 . |
| MLA | 蔡英杰 et al. "异型管钻孔专用机床中工件压紧夹具" : CN202121182033.4. | 2021-05-31 . |
| APA | 蔡英杰 , 胡强 , 姚立纲 , 许智明 . 异型管钻孔专用机床中工件压紧夹具 : CN202121182033.4. | 2021-05-31 . |
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本发明涉及一种摆幅可调的便携脚踝康复机器人，包括设在底座上的电机、转轴、曲柄、踏板，电机固定安装在底座上，电机的输出轴经联轴器连转轴的一端，转轴的另一端与曲柄一端连接固定，转轴经轴承座安装在底座上，曲柄的另一端经前球副连接踏板前端，踏板前端后端连接有后球副，后球副的轴部经后支撑板安装在底座上，前球副、后球副的球头部均安装在踏板上，曲柄上设有用于安装前球副的安装条孔，后支撑板能相对底座沿转轴轴向移动，移动到位后，后支撑板经螺栓锁固在底座上，本发明在满足康复效果的前提下实现了机构的简化，可调摆幅、操作简单、经济成本低、易于携带与组装，可以根据需要调整运动摆幅的大小，方便不同人群使用。
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| GB/T 7714 | 蔡英杰 , 卿曾宇 , 卢宗兴 et al. 摆幅可调的便携脚踝康复机器人 : CN202111161980.X[P]. | 2021-09-30 . |
| MLA | 蔡英杰 et al. "摆幅可调的便携脚踝康复机器人" : CN202111161980.X. | 2021-09-30 . |
| APA | 蔡英杰 , 卿曾宇 , 卢宗兴 , 游圣贤 , 蔡少雄 . 摆幅可调的便携脚踝康复机器人 : CN202111161980.X. | 2021-09-30 . |
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The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.
Keyword ：
acquisition time acquisition time forearm angle forearm angle gesture decoding gesture decoding machine learning machine learning muscle fatigue muscle fatigue surface electromyography surface electromyography
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| GB/T 7714 | Qing, Zengyu , Lu, Zongxing , Cai, Yingjie et al. Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time [J]. | SENSORS , 2021 , 21 (22) . |
| MLA | Qing, Zengyu et al. "Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time" . | SENSORS 21 . 22 (2021) . |
| APA | Qing, Zengyu , Lu, Zongxing , Cai, Yingjie , Wang, Jing . Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time . | SENSORS , 2021 , 21 (22) . |
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