Please wait a minute...
Front. Inform. Technol. Electron. Eng.  2016, Vol. 17 Issue (4): 348-364    DOI: 10.1631/FITEE.1500347
    
应用于康复治疗的多摄像系统:微软Kinect传感器的精度研究
Miguel Oliver, Francisco Montero, José Pascual Molina, Pascual González, Antonio Fernández-Caballero
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete 02071, Spain; Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
Multi-camera systems for rehabilitation therapies: a study of the precision of Microsoft Kinect sensors
Miguel Oliver, Francisco Montero, José Pascual Molina, Pascual González, Antonio Fernández-Caballero
Instituto de Investigación en Informática de Albacete, Universidad de Castilla-La Mancha, Albacete 02071, Spain; Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, Albacete 02071, Spain
 全文: PDF 
摘要: 目的:借助多组Kinect传感器监控试验研究多组红外光束的重合对定位的影响。
方法:首先介绍了近年来使用微软Kinect传感器开发康复系统的研究成果,以及红外饱和度衍生问题的相关研究。指出现有研究没有系统全面考虑干扰因素以及不同传感器布局的影响。然后,采用一系列实验先后分析了监控器数量、光照角度以及传感器和病人之间的距离等因素对监控精度的影响;说明了在康复系统中适宜同时在一个房间中进行康复的合适病人数、病人本身的身体状况对监控准确度的影响、以及房间内部的机构对传感器布局的影响。最后,将本文的研究结果和已有研究成果进行详细对比,并将实验收集的数据和得到的结果用于预测康复系统中每种传感器布局的效果。
结论:本文试验发现能够支持康复治疗地点监控传感器的合理布局设计,并实现控制被监控病人的数量。当病人不在治疗的合适区域内时,能够给出提示并引导其移动至合适位置。在康复过程中,该系统能够帮助识别出最适合监控每一位病人的传感器。
关键词: Kinect传感器康复系统捕捉精度多摄像系统    
Abstract: This paper seeks to determine how the overlap of several infrared beams affects the tracked position of the user, depending on the angle of incidence of light, distance to the target, distance between sensors, and the number of capture devices used. We also try to show that under ideal conditions using several Kinect sensors increases the precision of the data collected. The results obtained can be used in the design of telerehabilitation environments in which several RGB-D cameras are needed to improve precision or increase the tracking range. A numerical analysis of the results is included and comparisons are made with the results of other studies. Finally, we describe a system that implements intelligent methods for the rehabilitation of patients based on the results of the tests carried out.
Key words: Kinect sensor    Rehabilitation system    Capture precision    Multi-camera system
收稿日期: 2015-10-21 出版日期: 2016-04-05
CLC:  TP391  
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
Miguel Oliver
Francisco Montero
José Pascual Molina
Pascual González
Antonio Fernández-Caballero

引用本文:

Miguel Oliver, Francisco Montero, José Pascual Molina, Pascual González, Antonio Fernández-Caballero. Multi-camera systems for rehabilitation therapies: a study of the precision of Microsoft Kinect sensors. Front. Inform. Technol. Electron. Eng., 2016, 17(4): 348-364.

链接本文:

http://www.zjujournals.com/xueshu/fitee/CN/10.1631/FITEE.1500347        http://www.zjujournals.com/xueshu/fitee/CN/Y2016/V17/I4/348

[1] Rong-Feng Zhang , Ting Deng , Gui-Hong Wang , Jing-Lun Shi , Quan-Sheng Guan . 基于可靠特征点分配算法的鲁棒性跟踪框架[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 545-558.
[2] Gopi Ram , Durbadal Mandal , Sakti Prasad Ghoshal , Rajib Kar . 使用猫群算法优化线性天线阵列的最佳阵因子辐射方向图:电磁仿真验证[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 570-577.
[3] Lin-bo Qiao, Bo-feng Zhang, Jin-shu Su, Xi-cheng Lu. 结构化稀疏学习综述[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 445-463.
[4] Yuan-ping Nie, Yi Han, Jiu-ming Huang, Bo Jiao, Ai-ping Li. 基于注意机制编码解码模型的答案选择方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(4): 535-544.
[5] . 一种基于描述逻辑的体系质量需求建模与验证方法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 346-361.
[6] Ali Darvish Falehi, Ali Mosallanejad. 使用基于多目标粒子群算法多层自适应模糊推理系统晶闸管控制串联电容器补偿技术的互联多源电力系统动态稳定性增强器[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 394-409.
[7] Wen-yan Xiao, Ming-wen Wang, Zhen Weng, Li-lin Zhang, Jia-li Zuo. 基于语料库的小学英语认识率及教材选词策略研究[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(3): 362-372.
[8] Li Weigang. 用于评估共同作者学术贡献的第一和其他合作者信用分配模式[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 180-194.
[9] Hui Chen, Bao-gang Wei, Yi-ming Li, Yong-huai Liu, Wen-hao Zhu. 一种易用的实体识别消歧系统评测框架[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 195-205.
[10] Jun-hong Zhang, Yu Liu. 应用完备集合固有时间尺度分解和混合差分进化和粒子群算法优化的最小二乘支持向量机对柴油机进行故障诊断[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(2): 272-286.
[11] Yue-ting Zhuang, Fei Wu, Chun Chen, Yun-he Pan. 挑战与希望:AI2.0时代从大数据到知识[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 3-14.
[12] Bo-hu Li, Hui-yang Qu, Ting-yu Lin, Bao-cun Hou, Xiang Zhai, Guo-qiang Shi, Jun-hua Zhou, Chao Ruan. 基于综合集成研讨厅的群体智能设计研究[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 149-152.
[13] Yong-hong Tian, Xi-lin Chen, Hong-kai Xiong, Hong-liang Li, Li-rong Dai, Jing Chen, Jun-liang Xing, Jing Chen, Xi-hong Wu, Wei-min Hu, Yu Hu, Tie-jun Huang, Wen Gao. AI2.0时代的类人与超人感知:研究综述与趋势展望[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 58-67.
[14] Yu-xin Peng, Wen-wu Zhu, Yao Zhao, Chang-sheng Xu, Qing-ming Huang, Han-qing Lu, Qing-hua Zheng, Tie-jun Huang, Wen Gao. 跨媒体分析与推理:研究进展与发展方向[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44-57.
[15] Le-kui Zhou, Si-liang Tang, Jun Xiao, Fei Wu, Yue-ting Zhuang. 基于众包标签数据深度学习的命名实体消歧算法[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 97-106.