2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)最新文献_第3页

Safety Evaluation Technologies for Defecation Assistance Devices 辅助排便装置的安全评价技术

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535849

K. Homma, K. Fujiwara, I. Kajitani, T. Ogure

引用次数: 2

A Wearable EEG Real-time Measure and Analysis Platform for Home Applications 一种用于家庭应用的可穿戴式脑电实时测量与分析平台

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535959

Shuai Li, Zeyu Wang, Chunsheng Li

{"title":"A Wearable EEG Real-time Measure and Analysis Platform for Home Applications","authors":"Shuai Li, Zeyu Wang, Chunsheng Li","doi":"10.1109/IISR.2018.8535959","DOIUrl":"https://doi.org/10.1109/IISR.2018.8535959","url":null,"abstract":"Scalp electroencephalography (EEG) is widely used to study human electrophysiological activity noninvasively. With the gradual improvement of the quality of the multi-channel bioelectric signal acquisition device, the volume and power consumption are gradually reduced, which makes it suitable for portable and wearable application. To ensure sufficient signal gain and acquisition accuracy, it is a huge challenge to effectively suppress external interference and obtain a better bioelectric signal. This paper presents a new kind of wireless EEG signal real-time analysis and acquisition system. The system can be wearable at home environment and wirelessly send the real-time EEG signal to the host. The results show that the system can collect EEG signal robustly. The noise of the test system is reduced by 60% through the design of the isolated circuit. The software algorithm also enables the ability to perform basic analysis of biological signals by using Python digital signal processing module. The system provides a new safety platform for human-machine interfacing, rehabilitation and mental disease monitoring at home.","PeriodicalId":201828,"journal":{"name":"2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)","volume":"392 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116523640","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: 2

An Investigation of the Stabilization of Bilateral Robot Systems under Communication Delay 通信延迟下双边机器人系统的镇定问题研究

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535810

Hitoshi Watanabe, Pingguo Huang, Y. Ishibashi

引用次数: 3

Safe Human-Robot-Interaction in Highly Flexible Warehouses using Augmented Reality and Heterogenous Fleet Management System 基于增强现实和异构车队管理系统的高度灵活仓库安全人机交互

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535808

J. Jost, T. Kirks, P. Gupta, Dennis Lünsch, Jonas Stenzel

引用次数: 8

The Risks of Low Level Narrow Artificial Intelligence 低水平狭义人工智能的风险

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535903

J. Page, Michael Bain, F. Mukhlish

引用次数: 10

Fatigue Detection and Analysis of Upper Limb Muscles 上肢肌肉疲劳检测与分析

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535659

Qiuhao Zhang, Youlin Yao, Baiqing Sun, Dianchun Bai, Junyou Yang

引用次数: 0

Accelerating Noise-Free MRI Reconstruction for Image-Guided Medical Robot Interventions 加速图像引导医疗机器人介入的无噪声MRI重建

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535814

Xiaoyan Wang, Zhenzhou An, Haifeng Wang, Yuchou Chang

{"title":"Accelerating Noise-Free MRI Reconstruction for Image-Guided Medical Robot Interventions","authors":"Xiaoyan Wang, Zhenzhou An, Haifeng Wang, Yuchou Chang","doi":"10.1109/IISR.2018.8535814","DOIUrl":"https://doi.org/10.1109/IISR.2018.8535814","url":null,"abstract":"Image-guided medical robot interventions require high quality medical image as well as high imaging speed. Parallel MRI reconstruction accelerates imaging speed with keeping quality of image content. An infinite impulse response (IIR) model has been proposed to improve the finite impulse response (FIR) model, which is applied in generalized auto-calibrating partially parallel acquisitions (GRAPPA) image reconstruction method. Recursive terms of IIR GRAPPA are able to improve conventional GRAPPA reconstruction quality. However it has the limitation that outliers and noise lead to poor estimation in the recursive coefficients. On the other hand, auto-regressive moving average (ARMA) is one of the most common models in time series analysis. Time series analysis is using the system time series data obtained by the curve fitting and parameter estimation to establish the mathematical model and theoretical methods. We propose a novel scheme using nonlinear ARMA (NLARMA) model to address the noise and outlier problems in IIR GRAPPA reconstruction. The proposed method extends the linear MA model which has been applied in conventional GRAPPA by incorporating both recursive and nonlinear terms. The results of experimental phantom and in vivo brain datasets illustrate the proposed method can decrease noise and aliasing artifacts comparing with conventional GRAPPA and IIR GRAPPA reconstruction.","PeriodicalId":201828,"journal":{"name":"2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133024220","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

The Design of an Augmented Reality System for Urban Search and Rescue 城市搜救增强现实系统的设计

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535823

Runze Wang, Huimin Lu, Junhao Xiao, Yi Li, Qihang Qiu

引用次数: 5

Path Preference Recognition for Intelligent Robotic Wheelchair based on Evidence Network 基于证据网络的智能轮椅机器人路径偏好识别

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535612

Yong Li, Zhenyun Sun, Baiqing Sun, Junyou Yang

{"title":"Path Preference Recognition for Intelligent Robotic Wheelchair based on Evidence Network","authors":"Yong Li, Zhenyun Sun, Baiqing Sun, Junyou Yang","doi":"10.1109/IISR.2018.8535612","DOIUrl":"https://doi.org/10.1109/IISR.2018.8535612","url":null,"abstract":"Intelligent robotic wheelchairs can improve the quality of life who cannot walk independently and handicapped, especially in society of serious aging. In the process of helping people moving from one place to another, how to select the favorite path for users are very important. And existing methods mainly respond to get a safe path for users, which is not enough obviously. To be acceptable to users, the path should not only be safe, comfortable and fast, but also the favorite one for the user. However, it is difficult to identify the favorite path for different users, because everyone will have their preference. Hence, this study proposes a path preference recognitions method based on evidence network for different users. In this paper, human preference was extracted from the user who sitting on the autonomous wheelchair. The user preferences are affected by the width of paths, the number and the shape of obstacles and the distance from the obstacles to users, and they are all quantified respectively. Then all of them are put into network and fused to get the user's preference from complex conditions of different paths. In the end, the results show that there is a fair correlation between the user and the proposed method, and the match rate of the preference for paths is 70.83%.","PeriodicalId":201828,"journal":{"name":"2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)","volume":"22 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114401695","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

Machine-Vision Based Location Detection Solutions for Autonomous Horticulture Rover During Early Growth Season 自主园艺漫游车生长季早期基于机器视觉的位置检测解决方案

2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR) Pub Date : 2018-08-01 DOI: 10.1109/IISR.2018.8535837

D. Langan, Ryan Vraa, Chong Xu

{"title":"Machine-Vision Based Location Detection Solutions for Autonomous Horticulture Rover During Early Growth Season","authors":"D. Langan, Ryan Vraa, Chong Xu","doi":"10.1109/IISR.2018.8535837","DOIUrl":"https://doi.org/10.1109/IISR.2018.8535837","url":null,"abstract":"In this paper, we present a complete set of solutions to detect the location of the autonomous horticulture rover used for precision agriculture purposes during the early growth stage of the crops. The horticulture rover autonomously traverses the furrow by aligning itself with the centerline using the dynamic inversion navigation algorithm. The navigation algorithm takes the cross-track error and heading error of the rover deviating from the centerline as the location input, and outputs the desired left and right track speeds to the control system to correct the rover course. The rover has been using the real time kinematic (RTK) -based global position system (GPS) as the sensor to detect the location of the rover, but satellite signals can suffer from sporadic loss, and the geographical characteristics of the environment need to be accurately mapped beforehand. These limits of RTK GPS drastically increase the work load of the operators and decrease the location detection accuracy of the rover. The proposed easy-to-implement machine-vision based location detection solution set accepts the raw pictures taken by the camera mounted on the rover as input and calculate the cross-track and heading errors of the rover as outputs. It neither requires the usage of GPS, nor acquisition of environmental geographical information beforehand. Our test results indicate that the proposed machine-vision based solution set is able to outperform the RTK GPS by providing the cross-track location and heading information of the rover with an error less than 20% of that achieved by a RTK GPS, when the rover is within the furrow during the early growth stage.","PeriodicalId":201828,"journal":{"name":"2018 IEEE International Conference on Intelligence and Safety for Robotics (ISR)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116263347","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: 2