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CLC number: TP391

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2016-03-17

Cited: 5

Clicked: 8538

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Antonio Fernndez-Caballero

http://orcid.org/0000-0002-8211-0398

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.4 P.348-364

http://doi.org/10.1631/FITEE.1500347


Multi-camera systems for rehabilitation therapies: a study of the precision of Microsoft Kinect sensors


Author(s):  Miguel Oliver, Francisco Montero, Jos Pascual Molina, Pascual Gonzlez, Antonio Fernndez-Caballero

Affiliation(s):  Instituto de Investigacin en Informtica de Albacete, Universidad de Castilla-La Mancha, Albacete 02071, Spain; more

Corresponding email(s):   Antonio.Fdez@uclm.es

Key Words:  Kinect sensor, Rehabilitation system, Capture precision, Multi-camera system


Miguel Oliver, Francisco Montero, Jos Pascual Molina, Pascual Gonzlez, Antonio Fernndez-Caballero. Multi-camera systems for rehabilitation therapies: a study of the precision of Microsoft Kinect sensors[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(4): 348-364.

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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.

应用于康复治疗的多摄像系统:微软Kinect传感器的精度研究

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

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Bonnechère, B., Jansen, B., Salvia, P., et al., 2014. Determination of the precision and accuracy of morphological measurements using the KinectTM} sensor: comparison with standard stereophotogrammetry. Ergonomics, 57(4):622-631.

[2]Chang, Y., Chen, S., Huang, J., 2011. A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res. Deve. Disabil., 32(6):2566-2570.

[3]Essmaeel, K., Gallo, L., Damiani, E., et al., 2012. Temporal denoising of Kinect depth data. Proc. 8th Int. Conf. on Signal Image Technology and Internet Based Systems, p.47-52.

[4]Essmaeel, K., Gallo, L., Damiani, E., et al., 2014. Comparative evaluation of methods for filtering Kinect depth data. Multim. Tools Appl., 74(17):7331-7354.

[5]Fernández-Baena, A., Susín, A., Lligadas, X., 2012. Biomechanical validation of upper-body and lower-body joint movements of Kinect motion capture data for rehabilitation treatments. Proc. 4th Int. Conf. on Intelligent Networking and Collaborative Systems, p.656-661.

[6]Freitas, D., da Gama, A., Figueiredo, L., et al., 2012. Development and evaluation of a Kinect based motor rehabilitation game. Proc. SBGames, p.144-153.

[7]Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., et al., 2013. Metrological evaluation of Microsoft Kinect and Asus Xtion sensors. Measurement, 46(6):1800-1806.

[8]Haggag, H., Hossny, M., Filippidis, D., et al., 2013. Measuring depth accuracy in RGBD cameras. Proc. 7th Int. Conf. on Signal Processing and Communication Systems, p.1-7.

[9]Khoshelham, K., Elberink, S.O., 2012. Accuracy and resolution of Kinect depth data for indoor mapping applications. Sensors, 12(2):1437-1454.

[10]Mallick, T., Das, P.P., Majumdar, A.K., 2014. Characterizations of noise in Kinect depth images: a review. IEEE Sens. J., 14(6):1731-1740.

[11]Mkhitaryan, A., Burschka, D., 2013. RGB-D sensor data correction and enhancement by introduction of an additional RGB view. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, p.1077-1083.

[12]Olesen, S.M., Lyder, S., Kraft, D., et al., 2015. Real-time extraction of surface patches with associated uncertainties by means of Kinect cameras. J. Real-Time Image Process., 10(1):105-118.

[13]Oliver, M., Molina, J.P., Montero, F., et al., 2014a. A multisensor system for positioning of multiple users. Proc. XV Int. Conf. on Human Computer Interaction, Article 59.

[14]Oliver, M., Molina, J.P., Montero, F., et al., 2014b. Wireless multisensory interaction in an intelligent rehabilitation environment. Proc. 5th Int. Symp. on Ambient Intelligence, p.193-200.

[15]Oliver, M., Montero, F., Molina, J.P., et al., 2015a. How many Kinects should look at you? A multi-agent system approach. Proc. 13th Int. Conf. on Practical Applications of Agents and Multi-Agent Systems, p.105-112.

[16]Oliver, M., Montero, F., Fernández-Caballero, A., et al., 2015b. RGB-D assistive technologies for acquired brain injury: description and assessment of user experience. Expert Syst., 32(3):370-380.

[17]Regazzoni, D., de Vecchi, G., Rizzi, C., 2014. RGB cams vs RGB-D sensors: low cost motion capture technologies performances and limitations. J. Manuf. Syst., 33(4):719-728.

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