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| Intelligent walkers for disabled: Current state and future perspective |
| TAO Chunjing1, YAN Qingyang2, MA Lifang1, HUANG Jian2 |
1. Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids, Beijing 100176, China;
2. School of Artificial Intelligence and Automation, Key Laboratory of Ministry of Education for Image Processing and Intelligent Control, Huazhong University of Science and Technology, Wuhan 430074, China |
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Abstract: China is facing the serious problem of a huge number of disabled people. The intelligent walkers can help the disabled to walk more easily and freely, improve the quality of life of the disabled, and effectively ease the conflict between the single traditional walker function and the diverse patient needs. The intelligent walkers are expected to provide a solution to solve some social problems such as the walking-aids for the disabled. This paper reviews the related researches of intelligent walkers that provide the mobility services for disabled people. This paper also reviews the senses, the interactions and the controls as well as the safety of the intelligent walkers. At the end of the paper, the development of the intelligent walker by combining new materials and artificial intelligence is discussed.
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Received: 02 July 2019
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[1] 世界卫生组织. 世界残疾报告[EB/OL].[2018-12-14]. https://www.who.int/disabilities/world_report/2011/report/zh.
[2] 崔斌, 陈功, 郑晓瑛. 中国残疾人口致残原因分析[J]. 人口与发展, 2009(5):51-56.
[3] 赵燕潮. 中国残联发布我国最新残疾人口数据[J]. 残疾人研究, 2012(A01):11.
[4] 义肢[EB/OL].[2018-04-13]. https://zh.wikipedia.org/w/index.php?title=E7%BE%A9%E8%82%A2&oldid=49126048.
[5] 十一五规划[EB/OL]. (2018-11-23)[2018-12-14]. https://zh.wikipedia.org/w/index.php?title=%E5%8D%81%E4%B8%80%E4%BA%94%E8%A7%84%E5%88%92&oldid=52127177.
[6] 十二五规划[EB/OL]. (2018-09-25)[2018-12-14]. https://zh.wikipedia.org/w/index.php?title=%E5%8D%81%E4%BA%8C%E4%BA%94%E8%A7%84%E5%88%92&oldid=51414721.
[7] Chan A, Kwok E, Bhuanantanondh P. An assessment platform for upper limb myoelectric prothesis[C]//CMBES Proceedings. Ottawa:CMBES, 2018.
[8] Lei M, Wang Z Z. The study advances and prospects of processing surface EMG signal in prosthesis control[J]. Chinese Journal of Medical Instrumentation, 2001, 25(3):156-160.
[9] Lee S, Sankai Y. Power assist control for walking aid with HAL-3 based on EMG and impedance adjustment around knee joint[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002. Piscataway NJ:IEEE, 2002, 2:1499-1504.
[10] Khokhar Z O, Xiao Z G, Menon C. Surface EMG pattern recognition for real-time control of a wrist exoskeleton[J]. Biomedical Engineering Online, 2010, 9(1):41.
[11] O'Neill A, Petrie H, Lacey G, et al. Establishing initial user requirements for PAM-AID:A mobility and support device to assist frail and elderly visually impaired persons[M]//Improving the Quality of Life for the European Citizen. Clifton VA:IOS Press, 1998:292-295.
[12] Mori H, Kotani S, Kiyohiro N. A robotic travel aid "HITOMI"[C]//Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94). Piscataway NJ:IEEE, 1994, 3:1716-1723.
[13] Mori H, Kotani S. A Robotic Travel Aid for the Blind[M]//Robotics Research. London:Springer, 1998:237-245.
[14] Kotani S, Nakata T, Hideo M. A strategy for crossing of the robotic travel aid "Harunobu"[C]//2001 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2001. Piscataway NJ:IEEE, 2001, 2:668-673.
[15] Shoval S, Ulrich I, Borenstein J. NavBelt and the GuideCane[J]. IEEE Robotics and Automation Magazine, 2003, 10(1):9-20.
[16] Shim I, Yoon J. A human robot interaction system "RoJi"[C]//2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2003. Piscataway NJ:IEEE, 2003, 2:723-728.
[17] Schaeffer C, May T. Care-o-bot-a system for assisting elderly or disabled persons in home environments[C]//Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society. Piscataway NJ:IEEE, 1998, doi:10.1109/IECON.1998.724115.
[18] Nemoto Y, Egawa S, Koseki A, et al. Power-assisted walking support system for elderly[C]//Proceedings of the 20th Annual International Conference of the IEEE.Piscataway NJ:IEEE, 1998, 5:2693-2695.
[19] Egawa S, Nemoto Y, Fujie M G, et al. Power-assisted walking support system with imbalance compensation control for hemiplegics[C]//Proceedings of the First Joint BMES/EMBS Conference, 1999. Piscataway NJ:IEEE, 1999, 1:635.
[20] Wasson G, Gunderson J, Graves S, et al. An assistive robotic agent for pedestrian mobility[C]//Proceedings of the Fifth International Conference on Autonomous Agents. New York:ACM, 2001:169-173.
[21] Dubowsky S, Genot F, Godding S, et al. PAMM-A robotic aid to the elderly for mobility assistance and monitoring:A "helping-hand" for the elderly[C]//IEEE International Conference on Robotics and Automation, 2000. Piscataway NJ:IEEE, 2000, 1:570-576.
[22] Yu H, Spenko M, Dubowsky S. An adaptive shared control system for an intelligent mobility aid for the elderly[J]. Autonomous Robots, 2003, 15(1):53-66.
[23] Hirata Y, Hara A, Kosuge K. Passive-type intelligent walking support system "RT Walker"[C]//2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. Piscataway NJ:IEEE, 2004, 4:3871-3876.
[24] Kai Y, Tanioka T, Inoue Y, et al. A walking support/evaluation machine for patients with parkinsonism[J]. The Journal of Medical Investigation, 2004, 51(1-2):117-124.
[25] Shim H M, Lee E H, Shim J H, et al. Implementation of an intelligent walking assistant robot for the elderly in outdoor environment[C]//9th International Conference on Rehabilitation Robotics, 2005. Piscataway NJ:IEEE, 2005:452-455.
[26] Xiong G, Gong J, Gao J, et al. Optimum design and simulation of a mobile robot with standing-up devices to assist elderly people and paraplegic patients[C]//IEEE International Conference on Robotics and Biomimetics, 2006. Piscataway NJ:IEEE, 2006:807-812.
[27] Lacey G J, Rodriguez-Losada D. The evolution of guido[J]. IEEE Robotics and Automation Magazine, 2008, 15(4):75-83.
[28] Nejatbakhsh N, Kosuge K. User-environment based navigation algorithm for an omnidirectional passive walking aid system[C]//9th International Conference on Rehabilitation Robotics, 2005. Piscataway NJ:IEEE, 2005:178-181.
[29] Alwan M, Rajendran P J, Ledoux A, et al.[C]//Proceedings of AAAI Symposium. 2005.
[30] Merlet J P. Preliminary design of ANG, a low-cost automated walker for elderly[M]//New Trends in Mechanism Science. Dordrecht:Springer, 2010:529-536.
[31] Zhou W, Xu L, Yang J. An intent-based control approach for an intelligent mobility aid[C]//2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010). Piscataway NJ:IEEE, 2010, 2:54-57.
[32] Ye J, Huang J, He J, et al. Development of a widthchangeable intelligent walking-aid robot[C]//2012 International Symposium on Micro-NanoMechatronics and Human Science (MHS). Piscataway NJ:IEEE, 2012:358-363.
[33] Mou W H, Chang M F, Liao C K, et al. Context-aware assisted interactive robotic walker for parkinson's disease patients[C]//2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway NJ:IEEE, 2012:329-334.
[34] Takanashi H, Miyake Y. Co-emergence robot WalkMate and its support for elderly people[J]. Transactions of the Society of Instrument and Control Engineers, 2003, 39(1):74-81.
[35] Jiang S Y, Lin C Y, Huang K T, et al. Shared control design of a walking-assistant robot[J]. IEEE Transactions on Control Systems Technology, 2017, 25(6):2143-2150.
[36] Lee G, Ohnuma T, Chong N Y. Design and control of JAIST active robotic walker[J]. Intelligent Service Robotics, 2010, 3(3):125-135.
[37] Yang D, Zhou M, Huang J, et al. Aided sit to stand transfer by assistive robot and wearable sensors[C]//2nd International Conference on Advanced Robotics and Mechatronics (ICARM), 2017. Piscataway NJ:IEEE, 2017:239-244.
[38] Jun H G, Chang Y Y, Dan B J, et al. Walking and sitto-stand support system for elderly and disabled[C]//2011 IEEE International Conference on Rehabilitation Robotics (ICORR). Piscataway NJ:IEEE, 2011:1-5.
[39] Huang J, Di P, Fukuda T, et al. Motion control of omnidirectional type cane robot based on human intention[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. Piscataway NJ:IEEE, 2008:273-278.
[40] Di P, Huang J, Sekiyama K, et al. Motion control of intelligent cane robot under normal and abnormal walking condition[C]//RO-MAN, 2011 IEEE. Piscataway NJ:IEEE, 2011:497-502.
[41] Yan Q, Huang J, Luo Z. Human-robot coordination stability for fall detection and prevention using cane robot[C]//2016 International Symposium on Micro-Nano Mechatronics and Human Science (MHS). Piscataway NJ:IEEE, 2016:1-7.
[42] 侯增广, 赵新刚, 程龙, 等. 康复机器人与智能辅助系统的研究进展[J]. 自动化学报, 2016, 42(12):1765-1779.
[43] Lionis G S, Kyriakopoulos K J. A laser scanner based mobile robot SLAM algorithm with improved convergence properties[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, 2002. Piscataway NJ:IEEE, 2002, 1:582-587.
[44] Molton N, Se S, Brady J M, et al. A stereo vision-based aid for the visually impaired[J]. Image and Vision Computing, 1998, 16(4):251-264.
[45] Moeslund T B, Granum E. A survey of computer visionbased human motion capture[J]. Computer Vision and Image Understanding, 2001, 81(3):231-268.
[46] Dalal N, Triggs B, Schmid C. Human detection using oriented histograms of flow and appearance[C]//European Conference on Computer Vision. Berlin, Heidelberg:Springer, 2006:428-441.
[47] Liu H, Dong N, Zha H. Omni-directional vision based human motion detection for autonomous mobile robots[C]//2005 IEEE International Conference on Systems, Man and Cybernetics. Piscataway NJ:IEEE, 2005, 3:2236-2241.
[48] Mehta D, Sridhar S, Sotnychenko O, et al. Vnect:Realtime 3d human pose estimation with a single rgb camera[J]. ACM Transactions on Graphics, 2017, 36(4):44.
[49] Huang J, Yu X, Wang Y, et al. An integrated wireless wearable sensor system for posture recognition and indoor localization[J]. Sensors, 2016, 16(11):1825.
[50] Ryu S, Lee P, Chou J B, et al. Extremely elastic wearable carbon nanotube fiber strain sensor for monitoring of human motion[J]. ACS Nano, 2015, 9(6):5929-5936.
[51] Park J J, Hyun W J, Mun S C, et al. Highly stretchable and wearable graphene strain sensors with controllable sensitivity for human motion monitoring[J]. ACS Applied Materials & Interfaces, 2015, 7(11):6317-6324.
[52] Huang J, Xu W, Mohammed S, et al. Posture estimation and human support using wearable sensors and walkingaid robot[J]. Robotics and Autonomous Systems, 2015, 73:24-43.
[53] Prutchi D. A high-resolution large array(HRLA) surface EMG system[J]. Medical Engineering & Physics, 1995, 17(6):442-454.
[54] Lin C T, Liao L D, Liu Y H, et al. Novel dry polymer foam electrodes for long-term EEG measurement[J]. IEEE Transactions on Biomedical Engineering, 2011, 58(5):1200-1207.
[55] Supuk T G, Skelin A K, Cic M. Design, development and testing of a low-cost sEMG system and its use in recording muscle activity in human gait[J]. Sensors, 2014, 14(5):8235-8258.
[56] Gandhi V, Prasad G, Coyle D, et al. EEG-based mobile robot control through an adaptive brain-robot interface[J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2014, 44(9):1278-1285.
[57] Leeb R, Friedman D, Müller-Putz G R, et al. Selfpaced (asynchronous) BCI control of a wheelchair in virtual environments:A case study with a tetraplegic[J]. Computational Intelligence and Neuroscience, 2007, doi:10.115512007/79642.
[58] Huang D, Qian K, Fei D Y, et al. Electroencephalography (EEG)-based brain-computer interface(BCI):A 2-D virtual wheelchair control based on event-related desynchronization/synchronization and state control[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2012, 20(3):379-388.
[59] Li Z, Zhao S, Duan J, et al. Human cooperative wheelchair with brain-machine interaction based on shared control strategy[J]. IEEE/ASME Transactions on Mechatronics, 2017, 22(1):185-195.
[60] Bostanov V. BCI competition 2003-data sets Ib and IIb:Feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6):1057-1061.
[61] Scherer R, Muller G R, Neuper C, et al. An asynchronously controlled EEG-based virtual keyboard:Improvement of the spelling rate[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6):979-984.
[62] Kaper M, Meinicke P, Grossekathoefer U, et al. BCI competition 2003-data set IIb:Support vector machines for the P300 speller paradigm[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(6):1073-1076.
[63] Garrett D, Peterson D A, Anderson C W, et al. Comparison of linear, nonlinear, and feature selection methods for EEG signal classification[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003, 11(2):141-144.
[64] Schlögl A, Lee F, Bischof H, et al. Characterization of four-class motor imagery EEG data for the BCI-competition 2005[J]. Journal of Neural Engineering, 2005, 2(4):L14.
[65] Chiappa S, Bengio S. HMM and IOHMM modeling of EEG rhythms for asynchronous BCI systems[R]. IDIAP, 2003.
[66] Wakita K, Huang J, Di P, et al. Human-walking-intention-based motion control of an omnidirectional-type cane robot[J]. IEEE/ASME Transactions On Mechatronics, 2013, 18(1):285-296.
[67] Erden M S, Tomiyama T. Human-intent detection and physically interactive control of a robot without force sensors[J]. IEEE Transactions on Robotics, 2010, 26(2):370-382.
[68] Wang Z, Peer A, Buss M. An HMM approach to realistic haptic human-robot interaction[C]//EuroHaptics Conference, 2009 and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. Piscataway NJ:IEEE, 2009:374-379.
[69] Li Y, Ge S S. Human-robot collaboration based on motion intention estimation[J]. IEEE/ASME Transactions on Mechatronics, 2014, 19(3):1007-1014.
[70] Kiguchi K, Hayashi Y. An EMG-based control for an upper-limb power-assist exoskeleton robot[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2012, 42(4):1064-1071.
[71] Yan Q, Xu W, Huang J, et al. Laser and force sensors based human motion intent estimation algorithm for walking-aid robot[C]//2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems(CYBER). Piscataway NJ:IEEE, 2015:1858-1863.
[72] Xu W, Huang J, Cheng L. A novel coordinated motion fusion-based walking-aid robot system[J]. Sensors, 2018, 18(9):2761.
[73] Wang Z, Peer A, Buss M. An HMM approach to realistic haptic human-robot interaction[C]//EuroHaptics Conference, 2009 and Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems. Piscataway NJ:IEEE, 2009:374-379.
[74] Kazi Z, Chen S, Beitler M, et al. Speech and gesture mediated intelligent teleoperation[M]//Assistive Technology and Artificial Intelligence. Berlin, Heidelberg:Springer, 1998:194-210.
[75] Nehal S K, Obheroi R K, Anand A, et al. A navigation device with voice for visually impaired people[M]//Intelligent Communication, Control and Devices. Singapore:Springer, 2018:1369-1380.
[76] Walvekar V, Shetty S T, Shruthi N, et al. Blind hurdle stick:Android integrated voice based intimation via GPS with panic alert system[C]//3rd National Conference on Image Processing, Computing, Communication, Networking and Data Analytics. Piscataway NJ:IEEE, 2018:69.
[77] Han J S, Bien Z Z, Kim D J, et al. Human-machine interface for wheelchair control with EMG and its evaluation[C]//Proceedings of the 25th Annual International Conference of the IEEE. Piscataway NJ:IEEE, 2003, 2:1602-1605.
[78] Moon I, Lee M, Ryu J, et al. Intelligent robotic wheelchair with EMG-, gesture-, and voice-based interfaces[C]//2003 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2003. Piscataway NJ:IEEE, 2003, 4:3453-3458.
[79] Zhang Y Z, Yeh S S. Motion control design for robotic walking support systems using admittance motion command generator[C]//Proceedings of the International MultiConference of Engineers and Computer Scientists. 2011, 2.
[80] Di P, Hasegawa Y, Nakagawa S, et al. Fall detection and prevention control using walking-aid cane robot[J]. IEEE/ASME Transactions on Mechatronics, 2016, 21(2):625-637.
[81] Hirata Y, Hara A, Kosuge K. Motion control of passive intelligent walker using servo brakes[J]. IEEE Transactions on Robotics, 2007, 23(5):981-990.
[82] Xu W, Huang J, Wang Y, et al. Reinforcement learningbased shared control for walking-aid robot and its experimental verification[J]. Advanced Robotics, 2015, 29(22):1463-1481.
[83] Han H, Zhang X, Mu X. An approach for fuzzy control of elderly-assistant & walking-assistant robot[C]//2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). Piscataway NJ:IEEE, 2017:263-267.
[84] 徐文霞, 黄剑, 晏箐阳, 等. 兼具柔顺与安全的助行机器人运动控制研究[J]. 自动化学报, 2016, 42(12):1859-1873.
[85] Hirata Y, Muraki A, Kosuge K. Motion control of intelligent passive-type walker for fall-prevention function based on estimation of user state[C]//Proceedings 2006 IEEE International Conference on Robotics and Automation. Piscataway NJ:IEEE, 2006:3498-3503.
[86] Sugaiwa T, Iwata H, Sugano S. Shock absorbing skin design for human-symbiotic robot at the worst case collision[C]//8th IEEE-RAS International Conference on Humanoid Robots, 2008. Piscataway NJ:IEEE, 2008:481-486.
[87] Koganezawa K, Shimizu Y, Inomata H, et al. Actuator with non linear elastic system (ANLES) for controlling joint stiffness on antaonistic driving[C]//IEEE International Conference on Robotics and Biomimetics, 2004. Piscataway NJ:IEEE, 2004:51-55.
[88] Yamada Y, Hirasawa Y, Huang S, et al. Human-robot contact in the safeguarding space[J]. IEEE/ASME transactions on mechatronics, 1997, 2(4):230-236.
[89] Tejima N. Design method[C]//Integration of Assistive Technology in the Information Age:ICORR'2001, 7th International Conference on Rehabilitation Robotics. Clifton VA:IOS Press, 2001, 9:346.
[90] Ikuta K, Nokata M. General evaluation method of safety for human-care robots[C]//IEEE International Conference on Robotics and Automation, 1999. Piscataway NJ:IEEE, 1999, 3:2065-2072.
[91] Tao C, Yan Q, Li Y. Hierarchical shared control of cane-type walking-aid robot[J]. Journal of Healthcare Engineering, 2017, doi:10.1155/2017/8932938.
[92] Song K T, Jiang S Y, Wu S Y. Safe guidance for a walking-assistant robot using gait estimation and obstacle avoidance[J]. IEEE/ASME Transactions on Mechatronics, 2017, 22(5):2070-2078.
[93] Ko C H, Young K Y, Hsieh Y H. Optimized trajectory planning for mobile robot in the presence of moving obstacles[C]//2015 IEEE International Conference on Mechatronics (ICM). Piscataway NJ:IEEE, 2015:70-75.
[94] Kai Y, Arihara K. A walking support robot with velocity, torque, and contact force-based mechanical safety devices[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Piscataway NJ:IEEE, 2015:5026-5031.
[95] Hirata Y, Muraki A, Kosuge K. Motion control of intelligent passive-type walker for fall-prevention function based on estimation of user state[C]//Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. Piscataway NJ:IEEE, 2006:3498-3503.
[96] Hirata Y, Komatsuda S, Kosuge K. Fall prevention control of passive intelligent walker based on human model[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008. Piscataway NJ:IEEE, 2008:1222-1228.
[97] Kai Y, Arihara K, Kitaguchi S. Development of a walking support robot with velocity and torque-based mechanical safety devices[C]//2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). Piscataway NJ:IEEE, 2014:1498-1503.
[98] Huang J, Di P, Wakita K, et al. Study of fall detection using intelligent cane based on sensor fusion[C]//International Symposium on Micro-NanoMechatronics and Human Science, 2008. Piscataway NJ:IEEE, 2008:495-500.
[99] Di P, Huang J, Nakagawa S, et al. Fall detection and prevention in the elderly based on the ZMP stability control[C]//2013 IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO). Piscataway NJ:IEEE, 2013:82-87.
[100] Di P, Huang J, Nakagawa S, et al. Fall detection for the elderly using a cane robot based on ZMP estimation[C]//2013 International Symposium on Micro-NanoMechatronics and Human Science (MHS). Piscataway NJ:IEEE, 2013:1-6.
[101] Di P, Huang J, Nakagawa S, et al. Fall detection for elderly by using an intelligent cane robot based on center of pressure (COP) stability theory[C]//International Symposium on Micro-NanoMechatronics and Human Science (MHS), 2014. Piscataway NJ:IEEE, 2014:1-4.
[102] Nakagawa S, Hasegawa Y, Fukuda T, et al. Tandem stance avoidance using adaptive and asymmetric admittance control for fall prevention[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(5):542-550.
[103] Yan Q, Huang J, Xiong C, et al. Data-driven humanrobot coordination based walking state monitoring with cane-type robot[J]. IEEE Access, 2018, 6:8896-8908.
[104] Sun P, Wang S, Karimi H R. Robust redundant input reliable tracking control for omnidirectional rehabilitative training walker[J]. Mathematical Problems in Engineering, 2014, doi:10.1155/2014/636934.
[105] Srivastava S, Kao P C, Kim S H, et al. Assist-as-needed robot-aided gait training improves walking function in individuals following stroke[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2015, 23(6):956-963.
[106] Kober J, Peters J. Reinforcement learning in robotics:A survey[M]//Reinforcement Learning. Berlin:Springer, Heidelberg, 2012:579-610.
[107] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553):436.
[108] Zhang J, Fiers P, Witte K A, et al. Human-in-theloop optimization of exoskeleton assistance during walking[J]. Science, 2017, 356(6344):1280-1284.
[109] Walsh C. Human-in-the-loop development of soft wearable robots[J]. Nature Reviews Materials, 2018, 3(6):78. |
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2019, Vol. 37 
