2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)最新文献_第6页

A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements 基于深度学习的快速复杂运动中使用较少传感器获得更多生物力学参数的方法

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872240

Ye Wang, Gongbing Shan, Hua Li, Ruliang Feng, Yilan Zhang, Guanglin Li, Lin Wang

{"title":"A Deep-Learning-Based Method for Gaining More Biomechanical Parameters with Fewer Sensors in Fast and Complex Movements","authors":"Ye Wang, Gongbing Shan, Hua Li, Ruliang Feng, Yilan Zhang, Guanglin Li, Lin Wang","doi":"10.1109/RCAR54675.2022.9872240","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872240","url":null,"abstract":"Real-time biomechanical feedback can provide direct and objective quantified information for any practitioners, such as the athletes/coaches, to assist in accelerating their motor skills’ learning and training process. However, it is usually difficult to monitor the human motion in full and acquire the key biomechanical parameters (i.e., the kinematic and kinetic data, the EMG, etc.) just with few sensors in some elite sports involving fast movements and complex motor skills. Using too many sensors in the field tests may limit the athletes’ motor ability and affect the collected data’s validity and reliability. In this paper, we employ a deep learning method to immensely reduce the number of sensors required for providing the real-time biomechanical feedback in field, according to the hammer-throw local motion features found from our pilot study. Based on the Keras API imported from the TensorFlow open-source platform, two Sequential Neural Network models are implemented and compared. One model has two inputs (i.e., vertical displacements and velocities on waist) and six outputs (i.e., vital joint angles on lower limbs). The other one has four inputs (i.e., vertical displacements and velocities on wrist and waist) and thirteen outputs (i.e., vital joint angles on both upper and lower limbs). The experimental results demonstrate that the vital joint angles on the upper and lower limbs have strong correlation with the vertical wrist and waist/hip displacements respectively. This study indicates that fewer wearable sensors can be applied in fast and complex movements to obtain the most significant kinematic data, whereas more biomechanical parameters can be further gained by prediction.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132617618","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A New Robotic Grasp Detection Method based on RGB-D Deep Fusion* 基于RGB-D深度融合的机器人抓握检测新方法*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872259

Hao Ma, Ding Yuan, Qingke Wang, Hong Zhang

引用次数: 0

CP+: Camera Poses Augmentation with Large-scale LiDAR Maps CP+:相机姿态增强与大规模激光雷达地图

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872176

Jiadi Cui, S. Schwertfeger

引用次数: 0

A real-time neuro-robot system for robot state control 一种用于机器人状态控制的实时神经机器人系统

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872184

Zhe Chen, Tao Sun, Zihou Wei, Xie Chen, S. Shimoda, Toshio Fukuda, Qiang Huang, Qing Shi

{"title":"A real-time neuro-robot system for robot state control","authors":"Zhe Chen, Tao Sun, Zihou Wei, Xie Chen, S. Shimoda, Toshio Fukuda, Qiang Huang, Qing Shi","doi":"10.1109/RCAR54675.2022.9872184","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872184","url":null,"abstract":"Embodying an in vitro biological neural network (BNN) with a robot body to achieve in vitro biological intelligence has been attracting increasing attention in the fields of neuroscience and robotics. As a step forward toward this aim, here we propose a real-time neuro-robot system based on calcium recording, which consists of a modular BNN and a simulated mobile robot. In this system, the neural signal of the BNN is recorded, analyzed, and decoded to control the motion state of the mobile robot in real-time. The sensor data of the robot is encoded and transmitted to control an electrical pump. The electrical pump is included in the system to estimate the real-time performance of the system. An obstacle avoidance task is chosen as proof-of-concept experiments. In the experiments, a calcium recording video of a BNN is replayed to emulate the real-time video stream. The video is monitored and analyzed by a custom-made graphical user interface (GUI) to control the robot motion state and the electrical pump. Experimental results demonstrate that the proposed neuro-robot system can control the robot motion state in real-time. In the future, we will connect the electrical pump to the BNN and transmit the signal from the robot to the BNN by applying local drug stimulation, therefore realizing a closed-loop neuro-robot system.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"62 10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116816345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A New EEG-based Paradigm for Classifying Intention of Compound-Limbs Movement 基于脑电图的复合肢体动作意向分类新范式

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872213

Rui Ma, Yichuan Jiang, Yifeng Chen, Mingming Zhang

{"title":"A New EEG-based Paradigm for Classifying Intention of Compound-Limbs Movement","authors":"Rui Ma, Yichuan Jiang, Yifeng Chen, Mingming Zhang","doi":"10.1109/RCAR54675.2022.9872213","DOIUrl":"https://doi.org/10.1109/RCAR54675.2022.9872213","url":null,"abstract":"Traditional lower limb exoskeleton robots utilize electromechanical control panels or buttons to assist patients with physical disabilities, which is a passive training way of rehabilitation. Over the past few years, extensive research has been conducted on brain-controlled lower limb exoskeleton robot technology combined with an electroencephalogram (EEG) signals. However, the way most paradigms are designed does not conform to the natural walking posture of human beings. In this study, a new EEG-based paradigm is proposed for detecting the intention of compound-limbs movement, which is closer to human walking posture. The time-frequency analysis presents that there showed stronger event-related desynchronization (ERD) at the main channels. Besides, the brain topographical distribution shows that the ERD not only exists in the contralateral sensorimotor area, but also appears on the central parietal lobe region (the leg motion mapping region), which initially verified the possibility of differentiating this pattern. Then, after extracting time-frequency-spatial features by common spatial pattern method, three supervised machine learning algorithms are used to classify the compound limb movement. The results demonstrate that the classification performance of compound-limbs movement mode are much higher than that of single-leg movement (>20%). This research introduces a new paradigm for classifying lower-limbs related movement intention, which might help control the lower limbs exoskeleton with subjects’ voluntary intention and improve the effect of human-machine interface system.","PeriodicalId":304963,"journal":{"name":"2022 IEEE International Conference on Real-time Computing and Robotics (RCAR)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124876903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improved Adaptive Controller Design Scheme to Attenuate the Noise Amplification Effect of Differentials* 一种改进的自适应控制器设计方案，用于衰减差分的噪声放大效应*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872239

Zhi Fa, Yongchun Fang, Yinan Wu, Chun-Shan Liu

引用次数: 0

3-D Path Following Control for a Miniature Maneuverable Robotic Fish with Hybrid Actuators 混合驱动器微型机动机器鱼的三维路径跟踪控制

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872178

Sijie Li, Yating Li, Zhengxing Wu, Jian Wang, Min Tan

引用次数: 0

An Efficient Learning Based Autonomous Exploration Algorithm For Mobile Robots* 基于高效学习的移动机器人自主探索算法*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872229

Zhiwei Xing, Jintao Wang, Xiaorui Zhu

引用次数: 0

Medicine bottle recognition based on machine vision and deep learning in intravenous medicine dispensing robot* 基于机器视觉和深度学习的静脉给药机器人药瓶识别*

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872280

Haiyang Jin, Zhen Teng, Yucheng He, Qi Chen, Ruiqiang Wang, Ying Hu

引用次数: 0

The Research on Current Automated Driving ODD Regulations, Standards and Applications 当前自动驾驶ODD法规、标准及应用研究

2022 IEEE International Conference on Real-time Computing and Robotics (RCAR) Pub Date : 2022-07-17 DOI: 10.1109/RCAR54675.2022.9872246

Chen Chen, Qidong Zhao, Zheng Tong, Zhai Yang, Xianglei Zhu

引用次数: 1