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機器視覺技術(shù)在康復(fù)領(lǐng)域的應(yīng)用

Application of machine vision technology in rehabilitation

作者: 楊榮  宋亮  魏鵬緒  潘國新  
單位:國家康復(fù)輔具研究中心(北京 100176), 國家康復(fù)輔具研究中心附屬康復(fù)醫(yī)院(北京 100176) <p>通信作者:楊榮。E-mail:[email protected]</p> <p>&nbsp;</p>
關(guān)鍵詞: 機器視覺;人工智能;康復(fù)工程;輔具控制;圖像處理 
分類號:R318
出版年·卷·期(頁碼):2021·40·4(425-429)
摘要:

機器視覺技術(shù)通過視覺采集和分析系統(tǒng)對外界環(huán)境進行實時圖像采集和處理,得到目標(biāo)的特征信息,最終實現(xiàn)外部設(shè)備的控制。機器視覺技術(shù)具有精度高、實時性強、自動化與智能化程度高的優(yōu)點,已廣泛應(yīng)用于機器人控制、工業(yè)生產(chǎn)、輔助醫(yī)療診斷等領(lǐng)域。隨著醫(yī)療技術(shù)的發(fā)展,機器視覺作為人工智能的重要分支,在康復(fù)領(lǐng)域也得到越來越多的應(yīng)用。本文綜述了機器視覺的基本結(jié)構(gòu)和工作原理,并對其在輔助輔具、肢體康復(fù)、心理康復(fù)等五種康復(fù)領(lǐng)域的常見應(yīng)用進展?fàn)顩r進行簡要歸納與介紹,最后總結(jié)了機器視覺應(yīng)用于康復(fù)領(lǐng)域的主要問題和發(fā)展趨勢。

Machine vision technology can collect and process the external environment image in real-time through the vision acquisition and analysis system to get the characteristic information of the target. According to the characteristic information, the control of the external equipment is realized. Machine vision technology has been widely used in robot control, industrial production, auxiliary medical diagnosis and other fields with its advantages of high precision, strong real-time, high degree of automation and intelligence. With the development of medical technology, machine vision, as an important branch of artificial intelligence, has been applied more and more in the field of rehabilitation. This paper summarizes the basic structure and working principle of machine vision, and briefly summarizes and introduces its common application progress in five rehabilitation fields, such as auxiliary equipment, limb rehabilitation and psychological rehabilitation. Finally, the main problems and development trend of machine vision in the field of rehabilitation has been summarized.

參考文獻(xiàn):

[1] 中國殘疾人聯(lián)合會. 2019年殘疾人事業(yè)發(fā)展統(tǒng)計公報[R/OL].(2020-04-02)[2020-07-02]. http://www.cdpf.org.cn//zwgk/zccx/tjgb0aeb930262974effaddfc41a45ceef58.htm.

[2]? 余文勇,石繪.機器視覺自動檢測技術(shù) [M]. 北京: 化學(xué)工業(yè)出版社 ,2013.

[3]? 工控幫教研組.機器視覺原理與案例詳解[M]. 北京:電子工業(yè)出版社,2020.

[4] 黃志鵬, 郁漢琪, 張聰,等. 機器視覺的發(fā)展及應(yīng)用[J]. 信息與電腦, 2020(17): 127-129.

Huang ZP, Yu HQ, Zhang C, et al. The development and application of machine vision[J]. China computer& Communication, 2020(17): 127-129.

[5] 趙霞,袁家政,劉宏哲.基于視覺的目標(biāo)定位技術(shù)的研究進展[J].計算機科學(xué),2016,43(6):10-16,43.

Zhao X, Yuan JZ, Liu HZ. Advances in vision-based target location technology[J]. Computer Science, 2016,43(6):10-16,43.

[6] 楊晨曦,華云松.基于雙目立體視覺的目標(biāo)物測距研究[J].軟件,2020,41(1):128-132.

Yang CX, Hua YS. Research on target ranging based on binocular stereo vision[J]. Computer Engineering & Software,2020,41(1):128-132.

[7]? 周瑩亮. 基于計算機視覺的輪椅跟隨控制系統(tǒng)研究[D]. 西安:陜西科技大學(xué), 2020.

Zhou YL. Research on wheelchair following control system based on computer vision[D]. Xi’an: Shaanxi University of science and technology, 2020.

[8] 任恒樂, 徐方, 邸霈,等. 基于深度相機的移動機器人自主跟隨技術(shù)[J]. 計算機工程與設(shè)計, 2020, 41(2): 562-566.

Ren HL, Xu F, Di P, et al. Autonomous following technology of mobile robot based on depth camera [J]. Computer Engineering and Design, 2020, 41(2): 562-566.

[9]鄒超, 汪秉文, 孫志剛. 基于機器視覺的織物疵點檢測方法綜述[J]. 天津工業(yè)大學(xué)學(xué)報, 2009, 28(2): 78-82, 85.

Zou C, Wang BW, Sun ZG. Survey on fabric defect detection based on machine vision[J]. Journal of Tianji Polytechnic University, 2009, 28(2): 78-82, 85.

[10] Wang YN, Lu X, Ling ZG, et al. A method to calibrate vehicle-mounted cameras under urban traffic scenes[J]. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(6): 3270-3279.

[11] Zhang ZY. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.

[12] Yuan XC, Wu LS, Peng QJ. An improved Otsu method using the weighted object variance for defect detection[J]. Applied Surface Science, 2015, 349: 472-484.

[13]Girshick R, Donahue J, Darrell T, et al. Region-based convolutional networks for accurate object detection and segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(1): 142-158.

[14] Gorai AK, Raval S, Patel AK, et al. Design and development of a machine vision system using artificial neural network-based algorithm for automated coal characterization[J]. International Journal of Coal Science & Technology, 2020: 1-19.

[15] 趙月鵬. 基于機器視覺的人體坐姿檢測系統(tǒng)的設(shè)計與實現(xiàn)[D]. 哈爾濱: 哈爾濱理工大學(xué), 2020.

Zhao YP. Design and implementation of human sitting posture detection system based on machine vision[D]. Harbin: Harbin University of science and technology, 2020.

[16] 李旻擇, 李小霞, 王學(xué)淵,等. 基于多尺度核特征卷積神經(jīng)網(wǎng)絡(luò)的實時人臉表情識別[J]. 計算機應(yīng)用, 2019, 39(9): 2568-2574.

Li MZ, Li XX, Wang XY, et al. Real-time facial expression recognition based on convolutional neural network with multi-scale kernel feature[J]. Journal of Computer Applications, 2019, 39(9): 2568-2574.

[17] 劉勇, 李杰, 張建林,等. 基于深度學(xué)習(xí)的二維人體姿態(tài)估計研究進展[J]. 計算機工程,2021,47(3): 1-16.

Liu Y, Li J, Zhang JL, et al. Research progress of two-dimensional human pose estimation based on deep learning[J]. Computer Engineering, 2021, 47(3): 1-16.

[18]鄭季煒. 基于機器視覺的智能人機交互技術(shù)研究 [J]. 海峽科學(xué) ,2018(1): 24-26.

[19] Hartman A, Nandikolla VK. Human-machine interface for a smart wheelchair[J]. Journal of Robotics, 2019, 2019: 4837058.

[20] 李杰. 康復(fù)機器人輔助患者起立軌跡預(yù)測方法研究[D].沈陽: 沈陽工業(yè)大學(xué), 2017.

Li J. Research on prediction method of rehabilitation robot assisted patient standing-up trajectory[D]. Shenyang: Shenyang University of technology, 2017.

[21]? Ustinova KI, Perkins J, Szostakowski L. Effect of viewing angle on arm reaching while standing in a virtual environment: potential for virtual rehabilitation [J]. Acta Psychologica, 2010, 133(2): 180-190.

[22] 祝敏航.基于機器視覺的下肢外骨骼康復(fù)運動檢測系統(tǒng)[D]. 杭州:浙江大學(xué),2016.

Zhu MH. Machine vision-based motion detecting system for rehabilitation of lower extremity exoskeleton[D]. Hangzhou: Zhejiang University, 2016.

[23] 閆航. 康復(fù)訓(xùn)練場景下的動作與行為識別方法研究[D]. 鄭州:鄭州大學(xué), 2020.

Yan H. Research on the method of action and behavior recognition in rehabilitation training[D]. Zhengzhou:Zhengzhou University,2020.

[24] Adriella A, Alenyà G, Hernández-farigola J, et al. Deciding the different robot roles for

patient? cognitive? training[J].? International? Journal? of? Human-Computer? Studies,? 2018, 117: 20-29.

[25]陳浩東. 基于機器視覺的認(rèn)知康復(fù)機器人系統(tǒng)設(shè)計[D]. 合肥: 合肥工業(yè)大學(xué), 2019.

Chen HD. Design of cognitive rehabilitation robotic system based on machine vision[D]. Hefei: Hefei University of Technology, 2019.

[26] 謝俊祥, 張琳. 智能手術(shù)機器人及其應(yīng)用[J]. 中國醫(yī)療器械信息, 2015(3): 11-17.

Xie JX, Zhang L. The review and application of smart and surgical robots[J]. China Medical Device Information, 2015(3): 11-17.

[27] 鄭紅杰. 基于視覺的手術(shù)機器人腹腔鏡位姿自動調(diào)節(jié)方法研究[D]. 哈爾濱: 哈爾濱工程大學(xué), 2018.

Zheng HJ. Research on the laparoscopic pose vision-based automatic adjustment for robot assisted surgery[D]. Harbin: Harbin Engineering University, 2018.

[28] 彭璟, 羅浩宇, 趙淦森, 等. 深度學(xué)習(xí)下的醫(yī)學(xué)影像分割算法綜述[J]. 計算機工程與應(yīng)用, 2021,57(3):44-57.

Peng J, Luo HY, Zhao GS, et al. Survey of medical image segmentation algorithm in deep learning[J]. Computer Engineering and Applications, 2021, 57(3): 44-57.

[29] 李鵬. 基于3D打印扁平足個性化矯正鞋墊的設(shè)計及其對平衡能力的影響[D]. 蘇州: 蘇州大學(xué),2017.

Li P. The design of personalized orthopedic insoles for flatfoot based on 3D print and its impact on balance ability[D]. Suzhou: Soochow University, 2017.

[30]Germany EI, Pino EJ, Aqueveque PE. Myoelectric intuitive control and transcutaneous electrical stimulation of the forearm for vibrotactile sensation feedback applied to a 3Dprinted prosthetic hand[J]. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016, 2016: 5046-5050.

[31] 魯?shù)轮? 梅釗, 李向磊,等. 3D打印脊柱側(cè)凸矯形器的數(shù)字化設(shè)計及效果評估[J]. 中國組織工程研究, 2021,25(9):1329-1334.

Lu DZ, Mei Z, Li XL, et al. Digital design and effect evaluation of three-dimensional printing scoliosis orthosis[J]. Chinese Journal of Tissue Engineering Research, 2021,25(9):1329-1334.

[32]Choo YJ, Boudier-Revéret M, Chang MC. 3D printing technology applied to orthosis manufacturing: narrative review[J]. Annals of Palliative Medicine, 2020, 9(6):4262-4270.

[33]Portnoy S, Barmin N, Elimelech M, et al. Automated 3D-printed finger orthosis versus manual orthosis preparation by occupational therapy students: Preparation time, product weight, and user satisfaction[J]. Journal of Hand Therapy, 2020,33(2): 174-179.

[34] 顧飛,姚慶強,劉帥,等. 3D 打印截骨導(dǎo)板在膝關(guān)節(jié)單髁 置換術(shù)中的應(yīng)用[J]. 中國數(shù)字醫(yī)學(xué),2020, 15(6): 97-100.

Gu F, Yao QQ, Liu S, et al. Application of 3D-printed osteotomy guide plate in unicompartmental knee arthroplasty[J]. China Digital Medicine, 2020,15(6):97-100.

[35] 楊勇,邱志杰,徐紅革,等.3D 打印結(jié)合數(shù)字化設(shè)計在髖臼骨折手術(shù)治療中的應(yīng)用[J]. 臨床骨科雜志, 2021,24(1): 88-92.

Yang Y, Qiu ZJ, Xu HG, et al. The application of 3D printing combined with digital design in the surgical treatment of acetabular fracture[J]. Journal of Clinical Orthopaedics, 2021,24(1): 88-92.

[36]Raisian S, Fallahi HR, Khiabani KS, et al. Customized titanium mesh based on the 3D printed model vs. manual intraoperative bending of titanium mesh for reconstructing of orbital bone fracture: a randomized clinical trial[J]. Reviews on Recent Clinical Trials,2017,12(3):154-158.

[37] 高源. 三維模型和機器視覺結(jié)合的3D打印醫(yī)療導(dǎo)板質(zhì)量檢測[D]. 北京: 北京工業(yè)大學(xué), 2016.

Gao Y. The quality evaluation of 3D printing medical guide combining 3D model and computer vision[D]. Beijing: Beijing University of Technology, 2016.

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