徐琳 宋國(guó)明
關(guān)鍵詞: 最優(yōu)交換協(xié)議; Kinect點(diǎn)云數(shù)據(jù); 數(shù)據(jù)傳輸; 遠(yuǎn)程傳輸系統(tǒng); 智能診斷; 壓縮方案
中圖分類號(hào): TN919?34; TP391 ? ? ? ? ? ? ? ? ?文獻(xiàn)標(biāo)識(shí)碼: A ? ? ? ? ? ? ? ? ? 文章編號(hào): 1004?373X(2019)14?0100?04
A medical Kinect point cloud data remote transmission system
based on optimal exchange protocol
XU Lin, SONG Guoming
(Chengdu Technological University, Chengdu 610000, China)
Abstract: The traditional data remote transmission system based on the heterogeneous network does not compress the Kinect point cloud data, and has a low remote data transmission accuracy. Therefore, a medical Kinect point cloud data remote transmission system based on the optimal exchange protocol is designed. The system includes two operation modes: intelligent diagnosis and real?time diagnosis. The data optimal exchange protocol IEEE 11073?20601 is set up on the transport layer, whose core part includes the domain model, communication model and service model. For the system software, the 16?nary tree based compression scheme is used to compress the Kinect point cloud data and eliminate redundant data. The remote transmission of the Kinect point cloud data is conducted by means of the two stages of feasibility judgment and data transmission. The network realization bandwidth is used to judge the transmission reliability of the Kinect point cloud data. The experimental results show that the designed system has a maximum data arrival rate of 98%, and the accuracy of all the arrived data is 100%, which indicates that the system has a high data remote transmission performance.
Keywords: optimal exchange protocol; Kinect point cloud data; data transmission; remote transmission system; intelligent diagnosis; compression scheme
0 ?引 ?言
當(dāng)前,我國(guó)人口老齡化日趨明顯,各種慢性病成為威脅中老年人健康的主要因素,慢性病的防治與長(zhǎng)期治療變得愈發(fā)重要[1]。遠(yuǎn)程醫(yī)療作為新的醫(yī)療手段,可以使病人足不出戶即可進(jìn)行遠(yuǎn)程醫(yī)療服務(wù),給醫(yī)生和患者帶來(lái)便利[2]。因此,建立標(biāo)準(zhǔn)化的醫(yī)療遠(yuǎn)程傳輸系統(tǒng)具有重大意義。
Kinect應(yīng)用于醫(yī)療具有三個(gè)功能,即3D影像檢測(cè)、人體骨骼追蹤和音頻處理[3]。目前,對(duì)Kinect點(diǎn)云數(shù)據(jù)及其應(yīng)用的研究還處于初始階段,大部分調(diào)研屬于算法研究,在系統(tǒng)設(shè)計(jì)層面和遠(yuǎn)程醫(yī)療數(shù)據(jù)傳輸領(lǐng)域還有很大的空缺。文獻(xiàn)[4]提出異構(gòu)網(wǎng)絡(luò)制式下的3G點(diǎn)對(duì)點(diǎn)遠(yuǎn)程數(shù)據(jù)傳輸系統(tǒng),缺乏點(diǎn)云數(shù)據(jù)壓縮操作,系統(tǒng)到達(dá)數(shù)據(jù)準(zhǔn)確率低。因此,設(shè)計(jì)基于最優(yōu)交換協(xié)議的醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸系統(tǒng),構(gòu)建系統(tǒng)整體架構(gòu),設(shè)計(jì)點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸方案,并進(jìn)行仿真模擬驗(yàn)證其可行性,為傳統(tǒng)醫(yī)療向互聯(lián)網(wǎng)醫(yī)療發(fā)展提供必要的科學(xué)依據(jù)。
1 ?基于最優(yōu)交換協(xié)議的醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸系統(tǒng)
基于最優(yōu)交換協(xié)議的醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸系統(tǒng)具有智能診斷和實(shí)時(shí)診斷兩種工作模式,該系統(tǒng)通過(guò)比較Kinect獲取的患者運(yùn)動(dòng)信息和醫(yī)生設(shè)定的判斷標(biāo)準(zhǔn),可以診斷患者的病情。如果患者對(duì)系統(tǒng)的診斷結(jié)果滿意,便不需要醫(yī)生對(duì)其進(jìn)行實(shí)時(shí)監(jiān)控,Kinect會(huì)把獲取的數(shù)據(jù)生成視頻文件存儲(chǔ)在客戶端,同時(shí)將備份傳輸?shù)綌?shù)據(jù)庫(kù),供醫(yī)生查看[5]。若患者轉(zhuǎn)入實(shí)時(shí)監(jiān)控模式,則Kinect會(huì)將實(shí)時(shí)視頻傳輸給醫(yī)生所在的監(jiān)控端進(jìn)行人工診斷。系統(tǒng)智能診斷和實(shí)時(shí)診斷兩種模式有效減少了醫(yī)生的工作量,也減少了網(wǎng)絡(luò)傳輸?shù)臄?shù)據(jù)量,節(jié)省帶寬。
系統(tǒng)實(shí)時(shí)傳輸?shù)狞c(diǎn)云數(shù)據(jù)需要進(jìn)行壓縮處理,以保證在有限帶寬中的實(shí)時(shí)診斷效率和質(zhì)量。系統(tǒng)設(shè)計(jì)以最優(yōu)交換協(xié)議為基礎(chǔ),以點(diǎn)云數(shù)據(jù)壓縮技術(shù)為核心,實(shí)現(xiàn)Kinect點(diǎn)云視頻動(dòng)態(tài)壓縮和數(shù)據(jù)遠(yuǎn)程傳輸,從而降低對(duì)傳輸帶寬的要求,同時(shí)保證傳輸質(zhì)量,滿足醫(yī)療診斷的需要。
1.1 ?IEEE 11073?20601最優(yōu)交換協(xié)議
IEEE 11073?20601最優(yōu)交換協(xié)議在傳輸層上設(shè)置了數(shù)據(jù)的組合方式和對(duì)應(yīng)的交互方法,它使符合該交換標(biāo)準(zhǔn)的不同設(shè)備可以進(jìn)行邏輯上的交互和數(shù)據(jù)的傳輸[6]。系統(tǒng)傳輸層上不同設(shè)備利用IEEE 11073?20601最優(yōu)交換協(xié)議設(shè)計(jì)的域模型、通信模型以及服務(wù)方法完成數(shù)據(jù)交互。IEEE 11073?20601是設(shè)置在傳輸層上的數(shù)據(jù)交換協(xié)議,不受傳輸層制約,能夠與傳輸層協(xié)議中的任何一種協(xié)議完成數(shù)據(jù)交互[7]。IEEE 11073?20601標(biāo)準(zhǔn)設(shè)定了設(shè)備之間交互的3個(gè)模型,分別為:域模型、通信模型和服務(wù)模型。這3個(gè)模型協(xié)調(diào)工作,共同構(gòu)成了IEEE 11073?20601協(xié)議的核心部分。域模型采用面向?qū)ο蟮姆椒ㄒ?guī)定設(shè)備傳輸數(shù)據(jù)的類型和設(shè)備信息,如配置信息、設(shè)備地址等。通信模型利用狀態(tài)機(jī)通過(guò)APDU完成主設(shè)備端與客設(shè)備端邏輯上的數(shù)據(jù)交互,并管理交互狀態(tài)。服務(wù)模型通過(guò)服務(wù)規(guī)范實(shí)現(xiàn)相關(guān)屬性的采集和數(shù)據(jù)的發(fā)送。
1.2 ?基于16叉樹(shù)的Kinect點(diǎn)云數(shù)據(jù)壓縮方案
系統(tǒng)在最優(yōu)交換協(xié)議的基礎(chǔ)上,為保證數(shù)據(jù)傳輸質(zhì)量,需對(duì)點(diǎn)云數(shù)據(jù)進(jìn)行壓縮處理。一般情況下,相鄰幀圖像數(shù)據(jù)的變化部分遠(yuǎn)少于不變化部分,若采用8叉樹(shù)壓縮會(huì)將所有數(shù)據(jù)每過(guò)一幀傳輸一次,從而加大帶寬壓力[8]。為減小帶寬壓力,只對(duì)圖像數(shù)據(jù)變化部分進(jìn)行傳輸,不變化的部分則繼續(xù)使用上一幀的圖像數(shù)據(jù),利用16叉樹(shù)記錄圖像中變化部分的位置數(shù)據(jù),并傳輸該位置數(shù)據(jù)的二進(jìn)制編碼。16叉樹(shù)技術(shù)可以將Kinect點(diǎn)云數(shù)據(jù)中不變化的位置數(shù)據(jù)作為冗余數(shù)據(jù)加以消除。
1.3 ?醫(yī)療環(huán)境下的遠(yuǎn)程傳輸方案
1.3.1 ?Kinect點(diǎn)云數(shù)據(jù)的遠(yuǎn)程傳輸
在實(shí)際醫(yī)療環(huán)境中,患者行動(dòng)相對(duì)不便,且時(shí)常需要醫(yī)生實(shí)時(shí)監(jiān)控,因此在系統(tǒng)設(shè)計(jì)壓縮傳輸方案時(shí),應(yīng)去除冗余數(shù)據(jù),降低復(fù)雜度,節(jié)省帶寬,以滿足醫(yī)療數(shù)據(jù)實(shí)時(shí)監(jiān)控的需要[9]。Kinect點(diǎn)云數(shù)據(jù)的遠(yuǎn)程傳輸方案分為可行性判斷和數(shù)據(jù)傳輸兩個(gè)階段,詳細(xì)過(guò)程如下:
1) 對(duì)患者進(jìn)行Kinect視頻采集,獲得Kinect點(diǎn)云數(shù)據(jù);
2) 采用丟棄幀技術(shù)獲得每秒幀的實(shí)際傳輸數(shù)預(yù)估值[Ptransport];
3) 利用[Ptransport]計(jì)算最低帶寬要求[Drequire]和網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬[Dreal];
4) 根據(jù)[Drequire]和[Dreal]求得最小壓縮倍數(shù)N;
5) 對(duì)比N和醫(yī)生設(shè)定的最大壓縮倍數(shù)[Nh],若[N≤Nh],則繼續(xù)下一步,否則終止傳輸;
6) 運(yùn)用基于16叉樹(shù)的壓縮算法壓縮Kinect點(diǎn)云數(shù)據(jù);
7) 將壓縮完成的Kinect點(diǎn)云數(shù)據(jù)傳輸?shù)椒?wù)器,即醫(yī)生監(jiān)控端。
上述過(guò)程中,步驟1)~步驟5)是數(shù)據(jù)傳輸可行性判斷階段,步驟6)~步驟7)是數(shù)據(jù)傳輸階段。
1.3.2 ?網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬
系統(tǒng)每隔一段時(shí)間(30 s)會(huì)發(fā)送4個(gè)測(cè)試數(shù)據(jù)包到服務(wù)器并請(qǐng)求響應(yīng),收到響應(yīng)后,系統(tǒng)根據(jù)測(cè)試包的大小和傳輸所用時(shí)間計(jì)算帶寬并求出平均值,以確定網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬[10]。設(shè)t時(shí)刻發(fā)送的數(shù)據(jù)包[i(i=1,2,3,4)]的大小是[Pi(t)],傳輸所用時(shí)間為[γi(t)],那么此時(shí)的網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬為:
[Dreal(t)=13[Dreal1(t)+Dreal2(t)+Dreal3(t)+Dreal4(t)] ? ? ? ? ? ? =13P1(t)γ1(t)+P2(t)γ2(t)+P3(t)γ3(t)+P4(t)γ4(t)] ? ?有別于服務(wù)商設(shè)置的名義帶寬,網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬是實(shí)時(shí)測(cè)量所得,代表網(wǎng)絡(luò)實(shí)際數(shù)據(jù)傳輸?shù)哪芰?,其?duì)于判斷系統(tǒng)傳輸Kinect點(diǎn)云數(shù)據(jù)的可靠性尤為關(guān)鍵。
2 ?實(shí)驗(yàn)分析
2.1 ?仿真實(shí)驗(yàn)
利用C++編程模擬本文系統(tǒng)進(jìn)行Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸可行性判斷階段的工作情況。在[Drequire(Ptransport(t),N(t-1))]和[Dreal]一定的情況下,編程計(jì)算[t+1]時(shí)刻的壓縮倍數(shù)[Nt+1],以檢驗(yàn)本文系統(tǒng)運(yùn)行的終止條件。因?yàn)橹荒M系統(tǒng)t時(shí)刻Kinect點(diǎn)云數(shù)據(jù)傳輸可行性判斷階段的工作情況,所以可設(shè)定[N(t-1)]為常數(shù),不影響測(cè)試的準(zhǔn)確性。
假設(shè)本文系統(tǒng)每秒最多傳輸30幀,每幀需要使用30 MB數(shù)據(jù)量,那么t時(shí)刻最低帶寬要求預(yù)估值為750 Mb/s。根據(jù)實(shí)際醫(yī)療環(huán)境,設(shè)置壓縮倍數(shù)最大值為[Nh=120]。在[Ptransport(t)]與[Dreal(t)]不同取值的情況下,計(jì)算壓縮倍數(shù)預(yù)估值[Nt+1],所得結(jié)果如表1所示。
由表1可以看出:隨著實(shí)際傳輸數(shù)預(yù)估值[Ptransport]從1~20不斷增加,當(dāng)網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬[Dreal]為5 Mb/s時(shí),壓縮倍數(shù)預(yù)估值從122逐漸減少至8;當(dāng)實(shí)際傳輸數(shù)預(yù)估值[Ptransport]為1時(shí),網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬[Dreal]從5~30不斷增加,壓縮倍數(shù)預(yù)估值從122逐漸增加至722。因此可以得出,壓縮倍數(shù)預(yù)估值隨著實(shí)際傳輸數(shù)預(yù)估值的增加而減少,隨著網(wǎng)絡(luò)可實(shí)現(xiàn)帶寬的增加而增加。在實(shí)際醫(yī)療診斷中,每秒傳輸?shù)膸瑪?shù)范圍為5~10之間,若帶寬為10 Mb/s,則壓縮倍數(shù)應(yīng)為25~50。從該表可知,當(dāng)實(shí)際傳輸數(shù)預(yù)估值[Ptransport]范圍為5~10之間,本文系統(tǒng)此時(shí)的可實(shí)現(xiàn)帶寬,也就是壓縮倍數(shù)為28~45,在實(shí)際壓縮倍數(shù)范圍內(nèi),驗(yàn)證了本文系統(tǒng)進(jìn)行醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸?shù)挠行浴?/p>
2.2 ?系統(tǒng)測(cè)試
實(shí)驗(yàn)為了檢測(cè)本文系統(tǒng)中最優(yōu)交換協(xié)議IEEE 11073?20601核心模塊的正確性,采用模擬軟件模擬血壓計(jì)、血糖儀等數(shù)據(jù),以正常速度多次發(fā)送,測(cè)試數(shù)據(jù)是否可以正確傳輸?shù)絀EEE 11073?20601核心模塊并正確解析,測(cè)試結(jié)果如表2所示。通過(guò)該表可以看出,3個(gè)設(shè)備的數(shù)據(jù)到達(dá)率分別為98%,96%,97%,到達(dá)數(shù)據(jù)準(zhǔn)確率均為100%,說(shuō)明系統(tǒng)最優(yōu)交換協(xié)議IEEE 11073?20601核心模塊具有良好的解析能力,具有較高的正確性,進(jìn)而也驗(yàn)證了本文系統(tǒng)具有較高的醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸性能。
實(shí)驗(yàn)為測(cè)試本文系統(tǒng)穩(wěn)定性,在一定時(shí)間內(nèi)采用模擬軟件連續(xù)向IEEE 11073?20601核心模塊發(fā)送血壓、血糖數(shù)據(jù),發(fā)送次數(shù)為60次,頻率由慢到快,檢驗(yàn)安卓端是否能正常接收數(shù)據(jù),得到的結(jié)果如圖1所示。由該圖可以看出:5次實(shí)驗(yàn)所得的血壓接收數(shù)據(jù)次數(shù)分別為57次、60次、58次、59次、58次;血糖接收數(shù)據(jù)次數(shù)分別為56次、57次、56次、57次、57次。血壓和血糖正常接收數(shù)據(jù)次數(shù)所占百分比分別為98%和96%,說(shuō)明本文系統(tǒng)具有較高的穩(wěn)定性。
3 ?結(jié) ?論
本文設(shè)計(jì)基于最優(yōu)交換協(xié)議的醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸系統(tǒng),其具有智能診斷和實(shí)時(shí)診斷兩種工作模式,患者可通過(guò)智能診斷進(jìn)行自我健康狀態(tài)評(píng)估,或通過(guò)實(shí)時(shí)診斷由醫(yī)生評(píng)估患者身體狀態(tài)。實(shí)驗(yàn)結(jié)果表明:當(dāng)帶寬為10 Mb/s時(shí),本文系統(tǒng)的數(shù)據(jù)壓縮倍數(shù)為28~45,處于實(shí)際要求的壓縮倍數(shù)范圍內(nèi),驗(yàn)證了本文系統(tǒng)進(jìn)行醫(yī)療Kinect點(diǎn)云數(shù)據(jù)遠(yuǎn)程傳輸?shù)挠行?本文系統(tǒng)數(shù)據(jù)到達(dá)率分別為98%,96%,97%,到達(dá)數(shù)據(jù)準(zhǔn)確率均為100%,說(shuō)明系統(tǒng)具有較高的遠(yuǎn)程數(shù)據(jù)解析能力和傳輸性能。
參考文獻(xiàn)
[1] 李明,陳怡霖,潘曉英.遠(yuǎn)程醫(yī)療中Kinect點(diǎn)云數(shù)據(jù)的實(shí)時(shí)傳輸[J].西安郵電大學(xué)學(xué)報(bào),2016,21(1):33?37.
LI Ming, CHEN Yilin, PAN Xiaoying. Real?time Kinect point cloud data transmission in telemedicine applications [J]. Journal of Xian University of Posts and Telecommunications, 2016, 21(1): 33?37.
[2] 張毅,陳起,羅元.基于Kinect傳感器的三維點(diǎn)云地圖構(gòu)建與優(yōu)化[J].半導(dǎo)體光電,2016,37(5):754?757.
ZHANG Yi, CHEN Qi, LUO Yuan. Building and optimization of Kinect?based 3D point cloud map [J]. Semiconductor optoelectronics, 2016, 37(5): 754?757.
[3] 何東健,邵小寧,王丹,等.Kinect獲取植物三維點(diǎn)云數(shù)據(jù)的去噪方法[J].農(nóng)業(yè)機(jī)械學(xué)報(bào),2016,47(1):331?336.
HE Dongjian, SHAO Xiaoning, WANG Dan, et al. Denoising method of 3D point cloud data of plants obtained by Kinect [J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(1): 331?336.
[4] 方志力,溫維亮,郭新宇,等.基于Kinect的三維玉米植株骨架提取[J].系統(tǒng)仿真學(xué)報(bào),2017,29(3):524?530.
FANG Zhili, WEN Weiliang, GUO Xinyu, et al. Skeleton extraction from three?dimensional Maize based on Kinect [J]. Journal of system simulation, 2017, 29(3): 524?530.
[5] 咼維,胡濤,朱欣焰.基于Kinect的深度數(shù)據(jù)融合方法[J].計(jì)算機(jī)應(yīng)用研究,2014,31(1):285?288.
GUO Wei, HU Tao, ZHU Xinyan. Kinect?based depth image fusion method [J]. Application research of computers, 2014, 31(1): 285?288.
[6] 王梅,于遠(yuǎn)芳,屠大維,等.基于Kinect的環(huán)境平面特征提取與重構(gòu)[J].計(jì)算機(jī)應(yīng)用,2016,36(5):1366?1370.
WANG Mei, YU Yuanfang, TU Dawei, et al. Feature extraction and reconstruction of environmental plane based on Kinect [J]. Journal of computer applications, 2016, 36(5): 1366?1370.
[7] 楊曉燕,侯孟波,魏曉超.基于驗(yàn)證元的三方口令認(rèn)證密鑰交換協(xié)議[J].計(jì)算機(jī)研究與發(fā)展,2016,53(10):2229?2237.
YANG Xiaoyan, HOU Mengbo, WEI Xiaochao. Verifier?based three?party password authenticated key exchange protocol [J]. Journal of computer research and development, 2016, 53(10): 2229?2237.
[8] 鄭安剛,巫鐘興,張樂(lè)群.面向?qū)ο蟮臄?shù)據(jù)交換協(xié)議研究與應(yīng)用[J].電測(cè)與儀表,2017,54(24):105?109.
ZHENG Angang, WU Zhongxing, ZHANG Lequn. Research and application of object?oriented data exchange protocol [J]. Electrical measurement & instrumentation, 2017, 54(24): 105?109.
[9] 高小娟,車(chē)明,黎賀.異構(gòu)網(wǎng)絡(luò)制式下的3G點(diǎn)對(duì)點(diǎn)遠(yuǎn)程數(shù)據(jù)傳輸[J].計(jì)算機(jī)工程,2015,41(9):120?125.
GAO Xiaojuan, CHE Ming, LI He. Point?to?point 3G remote data transmission under heterogeneous network standard [J]. Computer engineering, 2015, 41(9): 120?125.
[10] 胡代弟,董素鴿.遠(yuǎn)程實(shí)驗(yàn)信息數(shù)據(jù)采集方法研究仿真[J].計(jì)算機(jī)仿真,2017,34(4):186?189.
HU Daidi, DONG Suge. Simulation of the remote experiment data collection method research [J]. Computer simulation, 2017, 34(4): 186?189.