1 田宝,张扬,邱卓英. 两次全国残疾人抽样调查主要数据的比较与分析[J]. 中国特殊教育, 2007(8): 54-56. 2 沈凌,喻洪流. 国内外假肢的发展历程[J]. 中国组织工程研究, 2012, 16(13): 2451-2454. 3 WangX J, LiR, FangJ, et al. A powered ankle prothesis driven by EHA technique [C]. IEEE Conference on Industrial Electronics and Applications, 2018: 1492-1497. 4 AuS K, WeberJ, HerrH. Powered ankle-foot prosthesis improves walking metabolic economy [J]. IEEE Trans Robot, 2009, 25(1): 51-66. 5 FengY G, WangQ N. Combining push-off power and nonlinear damping behaviors for a lightweight motor-driven transtibial prosthesis [J]. IEEE ASME Trans Mechatron, 2017, 22(6): 2512-2523. 6 张远深,刘明春,赵娜,等. McKibben气动人工肌肉技术的发展历程[J]. 液压与气动, 2008(7): 13-15. 7 SupF, BoharaA, GoldfarbM. Design and control of a powered knee and ankle prosthesis [C]. IEEE Int Conf Robot Autom, 2007: 4134-4139. 8 VersluysR, DesomerA, LenaertsG, et al. A biomechatronical transtibial prosthesis powered by pleated pneumatic artificial muscles [J]. Inter J Model Identification Control, 2008, 4(4): 394-405. 9 VersluysR, DesomerA, LenaertsG, et al. A pneumatically powered below-knee prosthesis: Design specifications and first experiments with an amputee [C]. Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron, 2009: 372-377. 10 YuT, PlummerA, IravaniP, et al. The design, analysis and testing of a compact electrohydrostatic powered ankle prosthesis [C]. Proc ASME Dyn Syst Control Conf, 2016: 1-7. 11 ValleryH, BurgkartR, HartmannC, et al. Complementary limb motion estimation for the control of active knee prostheses[J]. Biomed Tech (Berl), 2011, 56(1): 45-51. 12 BergelinB J, MattosJ O, WellsJ G, et al. Concept through preliminary bench testing of a powered lower limb prosthetic device [J]. J Mech Robot, 2010, 2(4): 41005-41013. 13 HerrH, GrabowskiA. Bionic ankle-foot prosthesis normalizes walking gait for persons with leg amputation [J]. Proc Biol Sci, 2012, 279(1728): 457-464. 14 KannapeO A, HerrH. Split-belt adaptation and gait symmetry in transtibial amputees walking with a hybrid EMG controlled ankle-foot prosthesis [C]. Conf Proc IEEE Eng Med Biol Soc, 2016: 5469-5472. 15 韩亚丽,贾山,王兴松. 基于人体生物力学的低功耗踝关节假肢的设计与仿真[J]. 机器人, 2013, 35(3): 276-282. 16 CherelleP, MatthysA, GrosuV, et al. The AMP-Foot 2.0: Mimicking intact ankle behavior with a powered transtibial prosthesis [C]. Proc IEEE RAS EMBS Int Conf Biomed Robot Biomechatron, 2012: 544-549. 17 CherelleP, GrosuV, MatthysA, et al. Design and Validation of the Ankle Mimicking Prosthetic (AMP-) Foot 2.0 [J]. IEEE Trans Neural Syst Rehabil Eng, 2013, 22(1): 138-148. 18 ZhuJ Y, WangQ N, WangL. On the design of a powered transtibial prosthesis with stiffness adaptable ankle and toe joints [J]. IEEE Trans Ind Electron, 2014, 61(9): 4797-4807. 19 WangQ N, YuanK B, ZhuJ Y, et al. Walk the walk: a lightweight active transtibial prosthesis [J]. IEEE Robot Autom Mag, 2015, 22(4): 80-89. 20 GeyerH, EilenbergM F, HerrH. Control of a powered ankle-foot prosthesis based on a neuromuscular model [J]. IEEE Trans Neural Syst Rehabil Eng, 2010, 18(2): 164-173. 21 SupF, VarolA H, GoldfarbM. Upslope walking with a powered knee and ankle prosthesis: initial results with an amputee subject [J]. IEEE Trans Neural Syst Rehabil Eng, 2010, 19(1): 71-78. 22 LaPrèA, SupF. A control strategy for an active alignment transtibial prosthesis [C]. Proc ASME Dyn Syst Control Conf, 2015: 1-6. 23 吕荣安,曹恒,朱钧,等. 主被动踝关节假肢力/位混合控制研究[J]. 华东理工大学学报:自然科学版, 2018, 44(2): 283-288. 24 朱钧,曹恒,王瑜,等. 混合驱动踝足假肢阻抗控制方法[J]. 中国科技论文, 2014(1): 117-121. 25 FicanhaE M, RastgaarM, KaufmanK R. Control of a 2-DOF powered ankle-foot mechanism [C]. IEEE International Conference on Robotics and Automation, 2015: 6439-6444. 26 YuanK B, ZhuJ Y, WangQ N, et al. Finite-state control of powered below-knee prosthesis with ankle and toe [C]. Proc IFAC World Congress, 2011: 2865-2870. 27 LawsonB E, ShultzA H, GoldfarbM. Evaluation of a coordinated control system for a pair of powered transfemoral prostheses [C]. IEEE International Conference on Robotics and Automation, 2013: 3888-3893. 28 SimonA M, IngrahamK A, FeyN P, et al. Configuring a powered knee and ankle prosthesis for transfemoral amputees within five specific ambulation modes [J]. PLoS One, 2014, 9(6): e99387. 29 HuangH, CrouchD L, LiuM, et al. A cyber expert system for auto-tuning powered prosthesis impedance control parameters [J]. Ann Biomed Eng, 2016, 44(5): 1613-1624. 30 LawsonB E, MitchellJ, TruexD, et al. A robotic leg prosthesis: design, control, and implementation [J]. IEEE Robot Autom Mag, 2014, 21(4): 70-81. 31 WangW J, LiJ, LiW D, et al. An echo-based gait phase determination method of lower limb prosthesis [J]. Adv Mater, 2013: 706-708, 629-634. 32 陈静,刘洋,邱长青,等. 主动式踝关节假肢运动轨迹的迭代学习控制[J]. 计算机技术与自动化, 2008, 27(4): 69-71. 33 刘国荣,张扬名. 移动机器人轨迹跟踪的模糊PID-P型迭代学习控制[J]. 电子学报, 2013, 41(8): 1536-1541. 34 YinK Y, PangM Y, XiangK, et al. Fuzzy iterative learning control strategy for powered ankle prosthesis [J]. Int J Robot Autom, 2018, 2(1): 122-131. 35 MazumderO, KunduA, LenkaP, et al. Design of speed adaptive myoelectric active ankle prosthesis [J]. Electron Lett, 2017, 53(23): 1508-1510. 36 WangJ, KannapeO A, HerrH. Proportional EMG control of ankle plantar flexion in a powered transtibial prosthesis [C]. IEEE Int Conf Rehabil Robot, 2013: 1-5. 37 KannapeO A, HerrH. Volitional control of ankle plantar flexion in a powered transtibial prosthesis during stair-ambulation [C]. Conf Proc IEEE Eng Med Biol Soc, 2014: 1662-1665. 38 HuangS, WensmanJ, FerrisD. Locomotor adaptation by transtibial amputees walking with an experimental powered prosthesis under continuous myoelectric control [J]. IEEE Trans Neural Syst Rehabil Eng, 2016, 24(5): 573-581. 39 ChenB J, WangQ N, WangL. Adaptive slope walking with a robotic transtibial prosthesis based on volitional EMG control [J]. IEEE ASME Trans Mechatron, 2015, 20(5): 2146-2157. 40 ThatteN, GeyerH. Toward balance recovery with leg prostheses using neuromuscular model control [J]. IEEE Trans Biomed Eng, 2016, 63(5): 904-913. 41 ZhangC S, AiQ S, MengW, et al. A subject-specific EMG-Driven musculoskeletal model for the estimation of moments in ankle plantar-dorsiflexion movement [C]. International Conference on Neural Information Processing, 2017: 685-693. 42 GhazaeiG, AlameerA, DegenaarP, et al. Deep learning-based artificial vision for grasp classification in myoelectric hands [J]. J Neural Eng, 2017, 14(3): 36025-36043. 43 LuN, LiT F, RenX D, et al. A deep learning scheme for motor imagery classification based on restricted boltzmann machines [J]. IEEE Trans Neural Syst Rehabil Eng, 2016, 25(6): 566-576. |