[1] FRYKHOLM E, GEPHINE S, SAEY D, et al. Inter-day test-retest reliability and feasibility of isokinetic, isometric, and isotonic measurements to assess quadriceps endurance in people with chronic obstructive pulmonary disease: a multicenter study[J]. Chronic Respiratory Disease, 2018, 16: 1-9.
[2] KREBS H I. Twenty+ years of robotics for upper-extremity rehabilitation following a stroke[M]. Rehabilitation Robotics. Elsevier, 2018: 175-92.
[3] ROBERTSON J W, ENGLEHART K B, SCHEME E J. Effects of confidence-based rejection on usability and error in pattern recognition-based myoelectric control[J]. IEEE Journal of Biomedical and Health Informatics, 2018, 23(5): 2002-8.
[4] LEE M-J, LEE J-H, KOO H-M, et al. Effectiveness of bilateral arm training for improving extremity function and activities of daily living performance in hemiplegic patients[J]. Journal of Stroke and Cerebrovascular Diseases, 2017, 26(5): 1020-5.
[5] MEKKI M, DELGADO A D, FRY A, et al. Robotic rehabilitation and spinal cord injury: a narrative review[J]. Neurotherapeutics, 2018, 15(3): 604-17.
[6] MAZLAN S, RAHMAN H A, FAI Y C, et al. Kinematic variables for upper limb rehabilitation robot and correlations with clinical scales: a review[J]. Bulletin of Electrical Engineering and Informatics, 2020, 9(1): 75-82.
[7] ZHANG S, FU Q, GUO S, et al. Coordinative motion-based bilateral rehabilitation training system with exoskeleton and haptic devices for biomedical application[J]. Micromachines, 2018, 10(1): 8.
[8] SHENG B, XIE S, TANG L, et al. An industrial robot-based rehabilitation system for bilateral exercises[J]. IEEE Access, 2019, 7: 151282-94.
[9] SONG Z, GUO S. Design process of exoskeleton rehabilitation device and implementation of bilateral upper limb motor movement[J]. Journal of Medical and Biological Engineering, 2012, 32(5): 323-30.
[10] SONG Z, GUO S, PANG M, et al. Implementation of resistance training using an upper-limb exoskeleton rehabilitation device for elbow joint[J]. Journal of Medical and Biological Engineering, 2014, 34(2): 188-96.
[11] KREBS H I, VOLPE B T, WILLIAMS D, et al. Robot-aided neurorehabilitation: a robot for wrist rehabilitation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(3): 327-35.
[12] WOLBRECHT E T, CHAN V, REINKENSMEYER D J, et al. Optimizing compliant, model-based robotic assistance to promote neurorehabilitation[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2008, 16(3): 286-97.
[13] BURGAR C G, LUM P S, SHOR P C, et al. Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience[J]. Journal of Rehabilitation Research and Development, 2000, 37(6): 663-74.
[14] PEHLIVAN A U, LOSEY D P, OMALLEY M K. Minimal assist-as-needed controller for upper limb robotic rehabilitation[J]. IEEE Transactions on Robotics, 2016, 32(1): 113–24.
[15] KIM J, KIM H, KIM J, et al. Quantitative assessment test for upper-limb motor function by using EMG and kinematic analysis in the practice of occupational therapy[C]. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017: 1158-61.
[16] SHENG B, WANG X B, HOU M J, et al. An automated system for motor function assessment in stroke patients using motion sensing technology: a pilot study[J]. Measurement, 2020, 161: 107896.
[17] ZIMMERLI L, JACKY M, LüNENBURGER L, et al. Increasing patient engagement during virtual reality-based motor rehabilitation[J]. Archives of Physical Medicine and Rehabilitation, 2013, 94(9): 1737-46.
[18] 陆晓, 张文通, 苏盼盼, 等. 基于表面肌电特征的脑卒中后下肢运动功能障碍评估[J]. 中国生物医学工程学报, 2022, 41(6): 759-63.
[19] 季林红, 张宇博, 王子羲, 等. 基于自适应Chirplet分解的偏瘫肌强直症状评[J]. 清华大学学报(自然科学版), 2007, 47(5): 627-30.
[20] 陈茂启. 基于盲源分离技术的高密度表面肌电分解[D]. 安徽: 中国科学技术大学, 2018.
[21] LI X, FISHER M, RYMER W Z, et al. Application of the F-response for estimating motor unit number and amplitude distribution in hand muscles of stroke survivors[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 24(6): 674-81.
[22] 钱玲玲, 陈香, 吴德, 等. 基于运动单位动作电位的平均发放间隔评估痉挛型脑瘫儿童的可行性研究[J]. 安徽医科大学学报, 2013, 48(10): 1205-8.
[23] HU X, SURESH A K, RYMER W Z, et al. Assessing altered motor unit recruitment patterns in paretic muscles of stroke survivors using surface electromyography[J]. Journal of Neural Engineering, 2015, 12(6): 066001.
[24] UMEHARA J, YAGI M, HIRONO T, et al. Quantification of muscle coordination underlying basic shoulder movements using muscle synergy extraction[J]. Journal of Biomechanics, 2021, 120: 110358.
[25] HONG Y N G, BALLEKERE A N, FREGLY B J, et al. Are muscle synergies useful for stroke rehabilitation?[J]. Current Opinion in Biomedical Engineering, 2021, 19: 100315.
[26] SCANO A, CHIAVENNA A, MALOSIO M, et al. Robotic assistance for upper limbs may induce slight changes in motor modules compared with free movements in stroke survivors: a cluster-based muscle synergy analysis[J]. Frontiers in Human Neuroscience, 2018, 12: 290.
[27] FERRANTE S, BEJARANO N C, AMBROSINI E, et al. A personalized multi-channel FES controller based on muscle synergies to support gait rehabilitation after stroke[J]. Frontiers in Neuroscience, 2016, 10: 12.
[28] CAPPELLINI G, IVANENKO Y P, POPPELE R E, et al. Motor patterns in human walking and running[J]. Journal of Neurophysiology, 2006, 95(6): 3426-37.
[29] D'AVELLA A, PORTONE A, FERNANDEZ L, et al. Control of fast-reaching movements by muscle synergy combinations[J]. Journal of Neuroscience, 2006, 26(30): 7791-810.
[30] IVANENKO Y P, POPPELE R E, LACQUANITI F. Five basic muscle activation patterns account for muscle activity during human locomotion[J]. Journal of Physiology-London, 2004, 556(1): 267-82.
[31] 张百发, 徐昌橙, 周兴龙, 等. 肌肉协同理论在专项技术分析中的应用——以射箭运动为例[J]. 体育科学, 2021, 41(8): 70-8.
[32] 何勇, 施长城, 左国坤, 等. 训练轨迹对上肢肌肉协同的影响[J]. 北京生物医学工程, 2019, 38(5): 441-9.
[33] CHEUNG V C K, TUROLLA A, AGOSTINI M, et al. Muscle synergy patterns as physiological markers of motor cortical damage[J]. Proceedings of the National Academy of Sciences of the United States of America, 2012, 109(36): 14652-6.
[34] STEELE K, ROZUMALSKI A, SCHWARTZ M. Muscle synergies and complexity of neuromuscular control during gait in cerebral palsy[J]. Developmental Medicine and Child Neurology, 2015, 57(12): 1176-82.
[35] YANG N, AN Q, KOGAMI H, et al. Temporal features of muscle synergies in sit-to-stand motion reflect the motor impairment of post-stroke patients[J]. IEEE Transactions Neural System Rehabilitation Engineering, 2019, 27(10): 2118-27.
[36] FOX E J, TESTER N J, KAUTZ S A, et al. Modular control of varied locomotor tasks in children with incomplete spinal cord injuries[J]. Journal of Neurophysiology, 2013, 110(6): 1415-25.
[37] TANG L, CHEN X, CAO S, et al. Assessment of upper limb motor dysfunction for children with cerebral palsy based on muscle synergy analysis[J]. Frontiers in Human Neuroscience, 2017, 11: 130.
[38] 李飞. 基于表面肌电信号的小儿脑瘫步态肌肉协同分析[D]. 安徽:中国科学技术大学, 2014.
[39] 辜禹, 陈楠, 刘倩, 等. 肌肉协同理论在小儿脑性瘫痪康复评定中的应用进展[J]. 中国康复理论与实践, 2020, 26(6): 673-7.
[40] WANG W, JIANG N, TENG L, et al. Synergy analysis of back muscle activities in patients with adolescent idiopathic scoliosis based on high-density electromyogram[J]. IEEE Transactions Biomedical Engineering, 2022, 69(6): 2006-17.
[41] ZHANG L, KUMAR K S, HE H, et al. Fully organic compliant dry electrodes self-adhesive to skin for long-term motion-robust epidermal biopotential monitoring[J]. Nature Communications, 2020, 11(1): 4683.
[42] YANG H, JI S, CHATURVEDI I, et al. Adhesive biocomposite electrodes on sweaty skin for long-term continuous electrophysiological monitoring[J]. ACS Materials Letters, 2020, 2(5): 478-84.
[43] NAWROCKI R A, JIN H, LEE S, et al. Self-adhesive and ultra-conformable, sub-300 nm dry thin-film electrodes for surface monitoring of biopotentials[J]. Advanced Function Materials, 2018, 28(36): 1803279.
[44] KIM J-H, KIM S-R, KIL H-J, et al. Highly conformable, transparent electrodes for epidermal electronics[J]. Nano Letters, 2018, 18(7): 4531-40.
[45] TIAN L, ZIMMERMAN B, AKHTAR A, et al. Large-area MRI-compatible epidermal electronic interfaces for prosthetic control and cognitive monitoring[J]. Nature Biomedical Engineering, 2019, 3(3): 194-205.
[46] ZHOU W, YAO S, WANG H, et al. Gas-permeable, ultrathin, stretchable epidermal electronics with porous electrodes[J]. ACS Nano, 2020, 14(5): 5798-805.
[47] ISON M, ARTEMIADIS P. The role of muscle synergies in myoelectric control: trends and challenges for simultaneous multifunction control[J]. Journal of Neural Engineering, 2014, 11(5): 051001.
[48] WELTMAN A, YOO J, MENG E. Flexible, penetrating brain probes enabled by advances in polymer microfabrication[J]. Micromachines, 2016, 7(10): 180.
[49] TANG L-J, WANG M-H, TIAN H-C, et al. Progress in research of flexible MEMS microelectrodes for neural interface[J]. Micromachines, 2017, 8(9): 281.
[50] FERRO M D, MELOSH N A. Electronic and ionic materials for neurointerfaces[J]. Advanced Functional Materials, 2018, 28(12): 1704335.
[51] D'AVELLA A, SALTIEL P, BIZZI E. Combinations of muscle synergies in the construction of a natural motor behavior[J]. Nature Neuroscience, 2003, 6(3): 300-8.
[52] TING L H, MCKAY J L. Neuromechanics of muscle synergies for posture and movement[J]. Current Opinion in Neurobiology, 2007, 17(6): 622-8.
[53] TODOROV E, LI W W, PAN X C. From task parameters to motor synergies: a hierarchical framework for approximately optimal control of redundant manipulators[J]. Journal of Robotic Systems, 2005, 22(11): 691-710.
[54] LOEB G E, BROWN I E, CHENG E J. A hierarchical foundation for models of sensorimotor control[J]. Experimental Brain Research, 1999, 126(1): 1-18.
[55] SCOTT S H. Optimal feedback control and the neural basis of volitional motor control[J]. Nature Reviews Neuroscience, 2004, 5(7): 534-46.
[56] DELIAGINA T G, ORLOVSKY G N, ZELENIN P V, et al. Neural bases of postural control[J]. Physiology, 2006, 21(3): 216-25.
[57] GURFINKEL V S, IVANENKO Y P, LEVIK Y S, et al. Kinesthetic reference for human orthograde posture[J]. Neuroscience, 1995, 68(1): 229-43.
[58] LEE D D, SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-91.
[59] GERMER C M, FARINA D, ELIAS L A, et al. Surface EMG cross talk quantified at the motor unit population level for muscles of the hand, thigh, and calf[J]. Journal of Applied Physiology, 2021, 131(2): 808-20.
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