[1]宣伯凯,刘作军,陈玲玲,等.膝上型假肢的运动意图识别与控制[J].东南大学学报(自然科学版),2017,47(6):1107-1116.[doi:10.3969/j.issn.1001-0505.2017.06.005]
 Xuan Bokai,Liu Zuojun,Chen Lingling,et al.Motion intention recognition and control of above knee prosthesis[J].Journal of Southeast University (Natural Science Edition),2017,47(6):1107-1116.[doi:10.3969/j.issn.1001-0505.2017.06.005]
点击复制

膝上型假肢的运动意图识别与控制()
分享到:

《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
47
期数:
2017年第6期
页码:
1107-1116
栏目:
自动化
出版日期:
2017-11-20

文章信息/Info

Title:
Motion intention recognition and control of above knee prosthesis
作者:
宣伯凯1刘作军12陈玲玲12杨鹏12
1河北工业大学控制科学与工程学院, 天津 300130; 2河北工业大学智能康复装置与检测技术教育部工程研究中心, 天津 300130
Author(s):
Xuan Bokai1 Liu Zuojun12 Chen Lingling12 Yang Peng12
1School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China
2Engineering Research Center of Intelligent Rehabilitation of Ministry of Education, Hebei University of Technology, Tianjin 300130, China
关键词:
假肢 运动意图 迭代学习控制 有限状态机
Keywords:
prosthesis motion intention iterative learning control finite state machine
分类号:
TP242
DOI:
10.3969/j.issn.1001-0505.2017.06.005
摘要:
根据不同路况条件和典型步速的笛卡尔积组合,利用装配在残肢侧的陀螺仪、加速度计和足底前后的压力传感器的信息,通过相关性系数分析、传感器融合、隐马尔可夫模型的方法,判断假肢使用者的运动意图.以健肢运动状态为参考值,利用迭代学习控制分别建立不同路况和步速情况下的控制知识数据库.通过传感器的关键状态变化信号驱动有限状态机状态转换,输出控制知识库中的控制量,实现假肢膝关节在不同路况、步速条件下对步态相位的控制.针对控制过程中出现的输出量实时偏差,采取了在线校正措施.对于有限状态机输出控制数据序列在时间同步上的超前和滞后问题,采取了相应的保持和补偿措施.结果表明,经隐马尔可夫模型处理后路况判断准确率可提升到91.7%,基于数据驱动的无模型控制方法能够实现对不同路况、步速下假肢步态的有效控制.
Abstract:
According to the combination of different terrains and walking speeds in the way of Cartesian product, a motion intention recognizer for amputee was presented. The sensor system was composed of an accelerometer, a gyroscope mounted on the prosthetic socket, and two pressure sensors mounted under the sole. The motion intention was inferred by intra-class correlation coefficient, sensor fusion and hidden Markov model. And a flexible iterative learning control(ILC)was proposed to build an experience database for the control of knee joint in prosthesis. And the motion state of the healthy knee was set as the learning sample in ILC. Furthermore, the sensor signals of the state transition were used to drive a finite state machine(FSM). The control experience in the knowledge database was output to control the stride phase according to the terrain, and speed. Besides, an online correction was adopted to reduce the real-time errors in the output axis. Moreover, to regulate the output sequence lead and lag in time axis, an output holder and a compensator were used. The experimental results show that the accuracy of the terrain recognition using the hidden Markor model is improved by 91.7%. Thus, the model-free control method is effective for prosthesis gait control of prosthesis according to the terrain and speed.

参考文献/References:

[1] Huang H, Kuiken T A, Lipschutz R D. A strategy for identifying locomotion modes using surface electromyography[J]. IEEE Trans Biomed Eng, 2009, 56(1): 65-73. DOI:10.1109/TBME.2008.2003293.
[2] Ha K H, Varol H A, Goldfarb M. Volitional control of a prosthetic knee using surface electromyography [J]. IEEE Trans Biomed Eng, 2011, 58(1): 144-151. DOI:10.1109/TBME.2010.2070840.
[3] Huang H, Zhang F, Hargrove L J, et al. Continuous locomotion-mode identification for prosthetic legs based on neuromuscular-mechanical fusion[J]. IEEE Trans Biomed Eng, 2011, 58(10): 2867-2875. DOI:10.1109/TBME.2011.2161671.
[4] Du L, Zhang F, Liu M, et al. Toward design of an environment-aware adaptive locomotion mode recognition system[J]. IEEE Trans Biomed Eng, 2012, 59(10): 2716-2725.
[5] Varol H A, Sup F, Goldfarb M. Multiclass real-time intent recognition of a powered lower limb prosthesis[J]. IEEE Trans Biomed Eng, 2010, 57(3): 542-551. DOI:10.1109/TBME.2009.2034734.
[6] Young A J, Simon A M, Hargrove L J. A training method for locomotion mode prediction using powered lower limb prostheses[J]. IEEE Trans Neural Syst Rehabil Eng, 2014, 22(3): 671-677. DOI:10.1109/TNSRE.2013.2285101.
[7] Lawson B E, Varol H A, Huff A, et al. Control of stair ascent and descent with a powered transfemoral prosthesis[J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 21(3): 466-473. DOI:10.1109/TNSRE.2012.2225640.
[8] 龚思远, 杨鹏, 宋亮, 等. 基于迭代学习控制智能下肢假肢研制:实现了对健肢步速的跟随[J]. 中国组织工程研究与临床康复, 2010, 14(13): 2295-2298. DOI:10.3969/j.issn.1673-8225.2010.13.005.
Gong Siyuan, Yang Peng, Song Liang, et al. Development of intelligent lower limb prostheses based on iterative learning control: A follow of normal walking speed[J]. Journal of Clinical Rehabilitative Tissue Engineering Research, 2010, 14(13): 2295-2298. DOI:10.3969/j.issn.1673-8225.2010.13.005. (in Chinese)
[9] Vallery H, Burgkart R, Hartmann C, et al. Complementary limb motion estimation for the control of active knee prostheses[J]. Biomed Tech, 2011, 56(1): 45-51. DOI:10.1515/BMT.2010.057.
[10] Ryu J K, Chong N Y, You B J, et al. Adaptive CPG based coordinated control of healthy and robotic lower limb movements[C]//The 18th IEEE International Symposium on Robot and Human Interactive Communication. Toyama, Japan, 2009:122-127.
[11] Chen G, Liu Z, Chen L, et al. Control of powered knee joint prosthesis based on finite-state machine[J]. Lecture Notes in Electrical Engineering, 2015, 337: 395-403. DOI:10.1007/978-3-662-46463-2_40.
[12] Sup F, Bohara A, Goldfarb M. Design and control of a powered transfemoral prosthesis[J]. Int J Rob Res, 2008, 27(2): 263-273. DOI:10.1177/0278364907084588.
[13] Lenzi T, Hargrove L, Sensinger J. Speed-adaptation mechanism: Robotic prostheses can actively regulate joint torque[J]. IEEE Robotics & Automation Magazine, 2014, 21(4): 94-107. DOI:10.1109/mra.2014.2360305.
[14] 丁其川, 熊安斌, 赵新刚, 等. 基于表面肌电的运动意图识别方法研究及应用综述[J]. 自动化学报, 2016,42(1): 13-25.
  Ding Qichuan, Xiong Anbin, Zhao Xingang, et al. A review on researches and applications of sEMG-based motion intent recognition methods[J]. Acta Automatica Sinica, 2016, 42(1): 13-25.(in Chinese)
[15] 胡进,侯增广,陈翼雄,等. 下肢康复机器人及其交互控制方法[J]. 自动化学报, 2014, 40(11): 2377-2389.
  Hu Jin, Hou Zengguang, Chen Yixiong, et al. Lower limb rehabilitation robots and interactive control methods[J]. Acta Automatica Sinica, 2014, 40(11): 2377-2389.(in Chinese)
[16] Sant’Anna A, Salarian A, Wickström N. A new measure of movement symmetry in early Parkinson’s disease patients using symbolic processing of inertial sensor data[J]. IEEE Trans Biomed Eng, 2011, 58(7): 2127-2135. DOI:10.1109/TBME.2011.2149521.
[17] Arami A, Barre A, Berthelin R, et al. Estimation of prosthetic knee angles via data fusion of implantable and wearable sensors[C]//2013 IEEE International Conference on Body Sensor Networks. Boston, USA, 2013: 1-6. DOI:10.1109/bsn.2013.6575473.
[18] 赵丽娜,刘作军,苟斌,等. 基于隐马尔可夫模型的动力型下肢假肢步态预识别[J]. 机器人, 2014, 36(3): 337-341.
Zhao Lina, Liu Zuojun, Gou Bin, et al. Gait recognition pre-judgment of dynamic lower limb prosthesis based on hidden Markov model[J]. Robot, 2014, 36(3): 337-341. DOI:10.3724/SP.J.1218.2014.00337. (in Chinese)

备注/Memo

备注/Memo:
收稿日期: 2017-06-30.
作者简介: 宣伯凯(1984—),男,博士生;杨鹏(联系人),男,博士,教授,博士生导师,yphebut@163.com.
基金项目: 国家自然科学基金资助项目(61703135,61773151)、河北省自然科学基金青年基金资助项目(F2016202327).
引用本文: 宣伯凯,刘作军,陈玲玲,等.膝上型假肢的运动意图识别与控制[J].东南大学学报(自然科学版),2017,47(6):1107-1116. DOI:10.3969/j.issn.1001-0505.2017.06.005.
更新日期/Last Update: 2017-11-20