柔性下肢助力外骨骼的步态识别和助力控制方法研究

       柔性下肢助力外骨骼已广泛应用于运动辅助、医疗康复等领域，能够显著增强人体运动能力，减少人体行走消耗。近年来柔性下肢助力外骨骼的研究取得了一定成果，但在多地形环境中助力弱行走能力人群行走时仍有较多问题亟待解决。因为行走环境、穿戴者步态特征及穿戴方式等方面的差异性，现有的柔性下肢助力外骨骼存在驱动助力结构单一、助力地形环境较少、助力控制策略固定等问题。本文针对弱行走能力人群在多地形环境行走时的下肢关节辅助需求，结合人体下肢行走的生物力学特性，从仿生学角度设计柔性下肢助力外骨骼的助力结构，研究人体行走步态识别方法及个体自适应助力控制方法，提高柔性下肢助力外骨骼的实用性和适用性。

       针对弱行走能力人群在面向多地形环境时的下肢运动辅助需求，研究柔性下肢助力外骨骼的系统设计。分析在不同地形行走时的人体下肢关节运动学信息及动力学信息，明确柔性下肢助力外骨骼的目标助力关节。开展柔性下肢助力外骨骼的系统设计，包括穿戴系统设计、模块化的机械系统设计以及电气控制系统设计，为柔性下肢助力外骨骼的助力技术研究提供稳定可靠的实验平台。搭建用于采集人体下肢关节数据、评估外骨骼助力效果的测试平台。柔性下肢助力外骨骼系统设计的实现，为弱行走能力人群在多地形环境中的行走助力研究提供了稳定可靠的实验平台。

       基于人体行走过程中的摆动足运动特征，研究适用于多地形环境的人体行走步态模式识别方法。结合IMU测量的人体脚部运动学数据，设计基于脚部状态事件的行走步态相位识别方法，明确柔性下肢助力外骨骼的助力时段。针对人体在多地形环境中的平地行走、上楼梯、下楼梯、上斜坡及下斜坡步态模式，通过上一个步态周期摆动相的脚部运动学数据提取与地形具有较强相关性的人体脚部运动特征。以人体脚部运动特征作为神经网络的输入识别当前步态周期的行走步态模式，并通过实验验证适用于多地形环境的人体行走步态模式识别方法的有效性和准确性。

       结合人体下肢膝踝关节运动机理，研究为人体膝、踝关节提供非同时助力单电机多关节分时助力方法。基于人体下肢膝、踝关节的运动学及动力学数据，分析人体下肢多关节耦合运动机理。开展单电机多关节分时助力的助力结构设计并建立动力学模型；进行单电机多关节分时助力结构的运动学及动力学分析。基于柔性下肢助力外骨骼系统开展单电机多关节分时助力实验，验证单电机多关节分时助力方法的功能性以及对人体下肢关节助力的有效性，实现外骨骼轻量化并有效降低穿戴者自身肌肉发力产生的关节力矩及关节功率。

       面向弱行走能力人群的个性化下肢关节辅助需求，研究个体差异自适应的柔性下肢助力外骨骼助力控制方法。建立层次分明且功能清晰的柔性下肢助力外骨骼助力控制系统框架，主要包括助力参数调节器、助力轨迹生成器以及运动轨迹生成器等。基于人在回路的助力控制参数优化方法，选择小腿的比目鱼肌肌肉激活程度作为人体生理目标，分别采用二维网格搜索和贝叶斯优化的方法自动优化柔性下肢助力外骨骼的关节助力参数设置。通过不同助力条件下的外骨骼穿戴实验评价人在回路的关节助力参数优化方法的效果，实现穿戴者个性化关节助力参数的自动寻优。

As a kind of lower-limb exoskeleton robot, the soft lower-limb exoskeleton has a compliant and comfortable wearing and assistive structure, which reduces the rigid constraint between the exoskeleton and the wearer's joints when providing assistance for the movement of the lower-limb joints. In the past decade, various domestic and abroad research groups have carried out a lot of research on soft lower-limb exoskeletons, and have achieved fruitful research results. However, due to the differences in wearers and their gait characteristics, wearable approach, walking environment, etc., the existing soft lower-limb exoskeletons have problems when applied to help people with weak walking ability in daily walking environment, such as large body weight, single assistive structure, less application scenarios, and fixed assistive control strategies. In this paper, based on needs of lower-limb joint assistance for the person with weak walking ability in the daily walking environment and the biomechanical characteristics of human walking, the assistive structure of the soft lower-limb exoskeleton is designed from the perspective of bionics, the human walking gait pattern recognition method in complex environments and the individual adaptive assistive control method are studied, so as to improve the practicability and applicability of the soft lower-limb exoskeleton. The soft lower-limb exoskeleton system for application of person with weak walking ability in the various terrain walking environment is designed based on the lower-limb assisted needs of the person with weak walking ability. The kinematic and biomechanical information of human lower limb joints during walking on different terrain were analyzed, and on this basis, the target assisted joints of soft lower-limb exoskeleton were defined. The system design of the soft lower-limb exoskeleton was carried out, including the design of the soft lower-limb exoskeleton wearing system, modular mechanical system and electrical control system, so as to provide a stable and reliable experimental platform for the research of the soft lower-limb exoskeleton used in daily walking environment. An exoskeleton test platform was built to collect the data of human lower limb joints and evaluate the effect of soft lower-limb exoskeleton on human lower limb joints. The implementation of the design of the soft lower-limb exoskeleton system provides a stable and reliable experimental platform for the research of the soft lower-limb exoskeleton for multiple walking environment. The gait pattern recognition method for walking based on motion characteristics of swinging foot is studied for the gait mode detection in the various terrain. Based on the human foot kinematics data measured by IMU, a walking gait phase recognition method based on foot state events was designed to determine the assisting period of the soft lower-limb exoskeleton. Aiming at the gait pattern recognition of walking on flat ground, ascending stairs, descending stairs, ascending slope and descending slope in the multiple walking environment, the kinematic data of the swing phase in the last gait cycle are used to extract the human motion features, which are strongly correlated with the terrain. Then, the human motion characteristics of swinging foot are used as the input of neural network to identify the gait pattern of the current gait cycle. The effectiveness and accuracy of the continuous walking gait pattern recognition method for daily walking in complex environment are verified by experiments. A single-motor-multi-joint time-sharing assistance method based on the motion mechanism of human knee and ankle joints was studied to provide asynchronous assistance to human lower limb joints. Based on the kinematic and dynamic data of knee and ankle joints of human lower limbs, the mechanism of multi-joint coupling motion of human lower limbs was analyzed, which was used as the theoretical basis of multi-joint asynchronous assistive method of single motor. Carry out the single-motor-multi-joint asynchronous assistive structure design and establish the dynamic model. The kinematics and dynamics analysis of single-motor-multi-joint asynchronous assistive structure are carried out. Based on the soft lower-limb exoskeleton system, a singlemotor-multi-joint asynchronous assistive experiment was carried out to verify the functionality of the single-motor-multi-joint asynchronous assistive method and its effectiveness in assisting human lower limb joints, so as to achieve lightweight and effectively reduce the joint torque and joint power generated by the wearer's own muscle. The adaptive assistive control method of the soft lower-limb exoskeleton is proposed based on the individualized joint assistance needs of people with weak walking ability. The clearly and layered control system framework of soft lower-limb exoskeleton mainly including assistive parameter adjuster, assistive force trajectory generator, and assistive position trajectory generator. Based on the human-in-the-loop assistive control parameters optimization method, the surface EMG signal of the soleus muscle is selected as the human physiological objective. Meanwhile, the two-dimensional grid search and Bayesian optimization methods are used to optimize the assistive parameter setting of the soft lower-limb exoskeleton. The performance of the human-in-the-loop assistive control parameters optimization method is evaluated through the experiment, so as to realize the automatic optimization of wearers personalized assistance control parameters.

[1] 第七次全国人口普查公报（第五号）——人口年龄构成情况[J]. 中国统计, 2021(05): 10-11.
[2] M Grimmer, R Riener, C J Walsh, et al. Mobility related physical and functional losses due to aging and disease - a motivation for lower limb exoskeletons[J]. Journal of NeuroEngineering and Rehabilitation, 2019, 16(1): 2.
[3] S Viteckova, P Kutilek, G de Boisboissel, et al. Empowering lower limbs exoskeletons: state-of-the-art[J]. Robotica, 2018, 36(11): 1743-1756.
[4] A Norhafizan, R A R Ghazilla, V Kasi, et al. A Review on Lower-Limb Exoskeleton System for Sit to Stand, Ascending and Descending Staircase Motion[J]. Applied Mechanics and Materials, 2014, 541-542: 1150-1155.
[5] B Kalita, J Narayan, S K Dwivedy. Development of Active Lower Limb Robotic-Based Orthosis and Exoskeleton Devices: A Systematic Review[J]. International Journal of Social Robotics, 2021, 13(4): 775-793.
[6] H Herr. Exoskeletons and orthoses: classification, design challenges and future directions[J]. Journal of NeuroEngineering and Rehabilitation, 2009, 6(1): 21.
[7] A M Dollar, H Herr. Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art[J]. IEEE Transactions on Robotics, 2008, 24(1): 144-158.
[8] T Yan, M Cempini, C M Oddo, et al. Review of assistive strategies in powered lower-limb orthoses and exoskeletons[J]. Robotics and Autonomous Systems, 2015, 64: 120-136.
[9] A J Young, D P Ferris. State of the Art and Future Directions for Lower Limb Robotic Exoskeletons[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(2): 171-182.
[10] C Kopp. Exoskeletons for warriors of the future[J]. Defence Today, 2011, 9(2): 38-40.
[11] D Shi, W Zhang, W Zhang, et al. A Review on Lower Limb Rehabilitation Exoskeleton Robots[J]. Chinese Journal of Mechanical Engineering, 2019, 32(1): 74.
[12] I Díaz, J J Gil, E Sánchez. Lower-Limb Robotic Rehabilitation: Literature Review and Challenges[J]. Journal of Robotics, 2011, 2011: 1-11.
[13] A Esquenazi, M Talaty, A Jayaraman. Powered Exoskeletons for Walking Assistance in Persons with Central Nervous System Injuries: A Narrative Review[J]. PM&R, 2017, 9(1): 46-62.
[14] M Wehner, B Quinlivan, P M Aubin, et al. A lightweight soft exosuit for gait assistance[C]//2013 IEEE International Conference on Robotics and Automation. Karlsruhe, Germany: IEEE, 2013: 3362-3369.
[15] A T Asbeck, R J Dyer, A F Larusson, et al. Biologically-inspired soft exosuit[C]//2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR). Seattle, WA: IEEE, 2013: 1-8.
[16] A T Asbeck, S M M De Rossi, I Galiana, et al. Stronger, Smarter, Softer: Next-Generation Wearable Robots[J]. IEEE Robotics & Automation Magazine, 2014, 21(4): 22-33.
[17] P Malcolm, W Derave, S Galle, et al. A Simple Exoskeleton That Assists Plantarflexion Can Reduce the Metabolic Cost of Human Walking[J]. PLoS ONE, 2013, 8(2): e56137.
[18] 万诗龙. 可穿戴下肢柔性外骨骼助力系统设计[D]. 东南大学, 2017.
[19] S Wan, M Yang, R Xi, et al. Design and control strategy of humanoid lower limb exoskeleton driven by pneumatic artificial muscles[C]//2016 23rd International Conference on Mechatronics and Machine Vision in Practice (M2VIP). Nanjing, China: IEEE, 2016: 1-5.
[20] 李洋. 柔性下肢助力外骨骼研究[D]. 东南大学, 2020.
[21] 李超. 气动肌肉驱动的外骨骼助力系统研究[D]. 浙江大学, 2016.
[22] 陶俊. 气动助力外骨骼机器人人机协同运动控制研究[D]. 浙江大学, 2018.
[23] 王东海. 基于行走步态的被动式重力支撑柔性下肢外骨骼系统[D]. 浙江大学, 2016.
[24] D Wang, K M Lee, J Ji. A Passive Gait-Based Weight-Support Lower Extremity Exoskeleton with Compliant Joints[J]. IEEE Transactions on Robotics, 2016, 32(4): 933-942.
[25] A T Asbeck, S M M De Rossi, K G Holt, et al. A biologically inspired soft exosuit for walking assistance[J]. The International Journal of Robotics Research, 2015, 34(6): 744-762.
[26] Y Ding, I Galiana, A Asbeck, et al. Multi-joint actuation platform for lower extremity soft exosuits[C]//2014 IEEE International Conference on Robotics and Automation (ICRA). Hong Kong, China: IEEE, 2014: 1327-1334.
[27] J Park, H Park, J Kim. Performance estimation of the lower limb exoskeleton for plantarflexion using surface electromyography (sEMG) signals[J]. Journal of Biomechanical Science and Engineering, 2017, 12(2): 16-00595-16-00595.
[28] F A Panizzolo, I Galiana, A T Asbeck, et al. A biologically-inspired multi-joint soft exosuit that can reduce the energy cost of loaded walking[J]. Journal of NeuroEngineering and Rehabilitation, 2016, 13(1): 43.
[29] K Schmidt, J E Duarte, M Grimmer, et al. The Myosuit: Bi-articular Anti-gravity Exosuit That Reduces Hip Extensor Activity in Sitting Transfers[J]. Frontiers in Neurorobotics, 2017, 11: 57.
[30] F L Haufe, A M Kober, K Schmidt, et al. User-driven walking assistance: first experimental results using the MyoSuit[C]//2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR). Toronto, ON, Canada: IEEE, 2019: 944-949.
[31] F L Haufe, K Schmidt, J E Duarte, et al. Activity-based training with the Myosuit: a safety and feasibility study across diverse gait disorders[J]. Journal of NeuroEngineering and Rehabilitation, 2020, 17(1): 135.
[32] L N Awad, J Bae, K O’Donnell, et al. A soft robotic exosuit improves walking in patients after stroke[J]. Science Translational Medicine, 2017, 9(400): eaai9084.
[33] ReStoreTM Soft Exo-Suit For Stroke Rehabilitation - ReWalk Robotics[EB/OL]. https://rewalk.com/restore-exo-suit/.
[34] L N Awad, A Esquenazi, G E Francisco, et al. The ReWalk ReStoreTM soft robotic exosuit: a multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation[J]. Journal of NeuroEngineering and Rehabilitation, 2020, 17(1): 80.
[35] S Y Shin, K Hohl, M Giffhorn, et al. Soft robotic exosuit augmented high intensity gait training on stroke survivors: a pilot study[J]. Journal of NeuroEngineering and Rehabilitation, 2022, 19(1): 51.
[36] K Murakami, S W John, M Komatsu, et al. External control of walking direction, using cross-wire mobile assist suit[C]//2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Vancouver, BC: IEEE, 2017: 1046-1051.
[37] S W John, K Murakami, M Komatsu, et al. Cross-wire assist suit concept, for mobile and lightweight multiple degree of freedom hip assistance[C]//2017 International Conference on Rehabilitation Robotics (ICORR). London: IEEE, 2017: 387-393.
[38] S W John, M Komatsu, K Murakami, et al. Soft hip walking assist experimental system featuring variable compliance control[C]//2017 IEEE International Conference on Consumer Electronics (ICCE). Las Vegas, NV, USA: IEEE, 2017: 400-401.
[39] C Di Natali, T Poliero, M Sposito, et al. Design and Evaluation of a Soft Assistive Lower Limb Exoskeleton[J]. Robotica, 2019, 37(12): 2014-2034.
[40] E S Graf, C M Bauer, V Power, et al. Basic functionality of a prototype wearable assistive soft exoskeleton for people with gait impairments: a case study[C]//Proceedings of the 11th PErvasive Technologies Related to Assistive Environments Conference. Corfu Greece: ACM, 2018: 202-207.
[41] J Chen, J Han, J Zhang. Design and Evaluation of a Mobile Ankle Exoskeleton With Switchable Actuation Configurations[J]. IEEE/ASME Transactions on Mechatronics, 2022, 27(4): 1846-1853.
[42] 刘洋. 基于绳-滑轮机构的欠驱动下肢外骨骼研究[D]. 2018.
[43] 杨业勤. 基于柔绳互绞驱动原理的柔性下肢外骨骼机器人研究[D]. 2021.
[44] 张宗伟. 面向弱能人群的助行外骨骼机器人系统研究[D]. 哈尔滨工业大学, 2021.
[45] C Xiong, T Zhou, L Zhou, et al. Multi-articular passive exoskeleton for reducing the metabolic cost during human walking[C]//2019 Wearable Robotics Association Conference. WearRAcon, 2019: 63-67.
[46] T Zhou, C Xiong, J Zhang, et al. Regulating Metabolic Energy Among Joints During Human Walking Using a Multiarticular Unpowered Exoskeleton[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 662-672.
[47] D Hu, C Xiong, T Wang, et al. Modulating Energy Among Foot-Ankle Complex With an Unpowered Exoskeleton Improves Human Walking Economy[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1961-1970.
[48] R W Nuckols, K Swaminathan, S Lee, 等. Automated detection of soleus concentric contraction in variable gait conditions for improved exosuit control[C]//2020 IEEE International Conference on Robotics and Automation (ICRA). 2020: 4855-4862.
[49] 马如. 柔性下肢助力装置及其效果评价方法的研究[D]. 河北工业大学, 2018.
[50] S Jin, N Iwamoto, K Hashimoto, et al. Experimental Evaluation of Energy Efficiency for a Soft Wearable Robotic Suit[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(8): 1192-1201.
[51] 张雷雨, 贺彦东, 李剑锋, 等. 下肢柔性助力外衣的工效学设计与步态预测[J]. 中南大学学报(自然科学版), 2021, 4(52): 1171-1184.
[52] G Lee, J Kim, F A Panizzolo, et al. Reducing the metabolic cost of running with a tethered soft exosuit[J]. Science Robotics, 2017, 2(6): eaan6708.
[53] J Kim, R Heimgartner, G Lee, et al. Autonomous and Portable Soft Exosuit for Hip Extension Assistance with Online Walking and Running Detection Algorithm[C]//2018 IEEE International Conference on Robotics and Automation (ICRA). Brisbane, QLD: IEEE, 2018: 5473-5480.
[54] E J Park, T Akbas, A Eckert-Erdheim, et al. A Hinge-Free, Non-Restrictive, Lightweight Tethered Exosuit for Knee Extension Assistance During Walking[J]. IEEE Transactions on Medical Robotics and Bionics, 2020, 2(2): 165-175.
[55] Z Zhou, X Liu, Q Wang. Concept and Prototype Design of a Soft Knee Exoskeleton with Continuum Structure (SoftKEX)[C]//Intelligent Robotics and Applications. Cham: Springer International Publishing, 2019: 73-82.
[56] X Liu, Z Zhou, Q Wang. Real-time onboard recognition of gait transitions for a bionic knee exoskeleton in transparent mode[C]//2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2018: 3202-3205.
[57] Z Zhou, Y Liao, C Wang, et al. Preliminary evaluation of gait assistance during treadmill walking with a light-weight bionic knee exoskeleton[C]//2016 IEEE International Conference on Robotics and Biomimetics (ROBIO). Qingdao, China: IEEE, 2016: 1173-1178.
[58] B T Quinlivan, S Lee, P Malcolm, et al. Assistance magnitude versus metabolic cost reductions for a tethered multiarticular soft exosuit[J]. Science Robotics, 2017, 2(2): eaah4416.
[59] J Lee, K Seo, B Lim, et al. Effects of assistance timing on metabolic cost, assistance power, and gait parameters for a hip-type exoskeleton[C]//2017 International Conference on Rehabilitation Robotics (ICORR). London: IEEE, 2017: 498-504.
[60] W Wang, J Chen, J Ding, et al. Improving Walking Economy With an Ankle Exoskeleton Prior to Human-in-the-Loop Optimization[J]. Frontiers in Neurorobotics, 2022, 15: 1-12.
[61] W Felt, J C Selinger, J M Donelan, et al. “Body-In-The-Loop”: Optimizing Device Parameters Using Measures of Instantaneous Energetic Cost[J]. PLOS ONE, 2015, 10(8): e0135342.
[62] J Zhang, P Fiers, K A Witte, et al. Human-in-the-loop optimization of exoskeleton assistance during walking[J]. Science, 2017, 356(6344): 1280-1284.
[63] M Kim, Y Ding, P Malcolm, et al. Human-in-the-loop Bayesian optimization of wearable device parameters[J]. PLOS ONE, 2017, 12(9): e0184054.
[64] Y Ding, M Kim, S Kuindersma, et al. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking[J]. SCIENCE ROBOTICS, 2018: 10.
[65] D F N Gordon, C McGreavy, A Christou, et al. Human-in-the-Loop Optimization of Exoskeleton Assistance Via Online Simulation of Metabolic Cost[J]. IEEE Transactions on Robotics, 2022: 1-20.
[66] 黄章波. 基于气动肌肉的下肢外骨骼机器人设计与控制研究[D]. 华中科技大学, 2018.
[67] 涂细凯. 基于气动肌肉外骨骼和功能性电刺激的肢体康复技术研究[D]. 华中科技大学, 2016.
[68] X Tu, J Huang, J He. Leg hybrid rehabilitation based on hip-knee exoskeleton and ankle motion induced by FES[C]//2016 International Conference on Advanced Robotics and Mechatronics (ICARM). Macau, China: IEEE, 2016: 237-242.
[69] Y Cao, J Huang, C Xiong. Single-Layer Learning-Based Predictive Control With Echo State Network for Pneumatic-Muscle-Actuators-Driven Exoskeleton[J]. IEEE Transactions on Cognitive and Developmental Systems, 2021, 13(1): 80-90.
[70] 张宇. 柔性下肢外骨骼机器人智能控制策略研究[D]. 哈尔滨工业大学, 2020.
[71] C Chen, Y Zhang, Y Li, et al. Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance[J]. Sensors, 2020, 20(15): 4333.
[72] W Cao, C Chen, H Hu, et al. Effect of Hip Assistance Modes on Metabolic Cost of Walking With a Soft Exoskeleton[J]. IEEE Transactions on Automation Science and Engineering, 2021, 18(2): 426-436.
[73] Z Li, X Li, Q Li, et al. Human-in-the-Loop Control of Soft Exosuits Using Impedance Learning on Different Terrains[J]. IEEE Transactions on Robotics, 2022: 1-10.
[74] Q Li, W Qi, Z Li, et al. Fuzzy Based Optimization and Control of a Soft Exo-suit for Compliant Robot-Human-Environment Interaction[J]. IEEE Transactions on Fuzzy Systems, 2022: 1-13.
[75] 唐纳德·A.诺伊曼. 骨骼肌肉功能解剖学.第2版[M]. 骨骼肌肉功能解剖学.第2版, 2014.
[76] R Riener, M Rabuffetti, C Frigo. Stair ascent and descent at different inclinations[J]. Gait & Posture, 2002, 15(1): 32-44.
[77] A S McIntosh, K T Beatty, L N Dwan, et al. Gait dynamics on an inclined walkway[J]. Journal of Biomechanics, 2006, 39(13): 2491-2502.
[78] F L Buczek Jr. Three-dimensional kinematics and kinetics of the ankle and knee joints during uphill, level, and downhill walking[M]. The Pennsylvania State University, 1990.
[79] A Protopapadaki, W I Drechsler, M C Cramp, et al. Hip, knee, ankle kinematics and kinetics during stair ascent and descent in healthy young individuals[J]. Clinical Biomechanics, 2007, 22(2): 203-210.
[80] S Nadeau, B J McFadyen, F Malouin. Frontal and sagittal plane analyses of the stair climbing task in healthy adults aged over 40 years: what are the challenges compared to level walking?[J]. Clinical Biomechanics, 2003, 18(10): 950-959.
[81] A D Kuo, J M Donelan, A Ruina. Energetic Consequences of Walking Like an Inverted Pendulum: Step-to-Step Transitions:[J]. Exercise and Sport Sciences Reviews, 2005, 33(2): 88-97.
[82] 侯世伦, 张新, 王安利, 高颀. 老年人膝关节骨性关节炎的运动康复:机制、方法与进展[J]. 成都体育学院学报, 2018, 44(1): 110-115.
[83] J Kim, G Lee, R Heimgartner, et al. Reducing the metabolic rate of walking and running with a versatile, portable exosuit[J]. Science, 2019, 365(6454): 668-672.
[84] Xsens Technologies B.V. MTi-600 series Data Sheet. [EB/OL].
[2019-09-10].https://mtidocs.xsens.com/mti-600-series-datasheet.[Z].
[85] A C Villa-Parra, D Delisle-Rodríguez, A López-Delis, et al. Towards a Robotic Knee Exoskeleton Control Based on Human Motion Intention through EEG and sEMGsignals[J]. Procedia Manufacturing, 2015, 3: 1379-1386.
[86] A Protopapadaki, W I Drechsler, M C Cramp, et al. Hip, knee, ankle kinematics and kinetics during stair ascent and descent in healthy young individuals[J]. Clinical Biomechanics, 2007, 22(2): 203-210.
[87] J Jang, Kyungrock Kim, Jusuk Lee, et al. Online gait task recognition algorithm for hip exoskeleton[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Hamburg, Germany: IEEE, 2015: 5327-5332.
[88] X Yun, E R Bachmann, H Moore, et al. Self-contained Position Tracking of Human Movement Using Small Inertial/Magnetic Sensor Modules[C]//Proceedings 2007 IEEE International Conference on Robotics and Automation. Rome, Italy: IEEE, 2007: 2526-2533.
[89] M Hao, K Chen, C Fu. Smoother-Based 3-D Foot Trajectory Estimation Using Inertial Sensors[J]. IEEE Transactions on Biomedical Engineering, 2019, 66(12): 3534-3542.
[90] 朱大奇, 史慧. 人工神经网络原理及应用[M]. 科学出版社, 2006.
[91] J Y Jung, W Heo, H Yang, et al. A Neural Network-Based Gait Phase Classification Method Using Sensors Equipped on Lower Limb Exoskeleton Robots[J]. Sensors, 2015, 15(11): 27738-27759.
[92] V D M Laurens, G Hinton. Viualizing_data_using_t-SNE.pdf[J]. Journal of Machine Learning Research, 2008, 9(2605): 2579-2605.
[93] T D Collins, S N Ghoussayni, D J Ewins, et al. A six degrees-of-freedom marker set for gait analysis: Repeatability and comparison with a modified Helen Hayes set[J]. Gait & Posture, 2009, 30(2): 173-180.
[94] D A Winter. Biomechanics and motor control of human movement[M]. 4th ed. Hoboken, N.J: Wiley, 2009.
[95] R Featherstone. Rigid Body Dynamics Algorithms[M]. Boston, MA: Springer US, 2008.
[96] S Jin, N Iwamoto, K Hashimoto, et al. Experimental Evaluation of Energy Efficiency for a Soft Wearable Robotic Suit[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(8): 1192-1201.
[97] Y Ding, I Galiana, A T Asbeck, et al. Biomechanical and Physiological Evaluation of Multi-Joint Assistance With Soft Exosuits[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(2): 119-130.
[98] S Galle, P Malcolm, S H Collins, et al. Optimizing Robotic Exoskeletons Actuation based on Human Neuromechanics Experiments: Interaction of Push-off Timing and Work[C]//7th International symposium on Adaptive Motion of Animals and Machines (AMAM 2015). 2015: 1-3.
[99] A T Asbeck, S M M De Rossi, K G Holt, et al. A biologically inspired soft exosuit for walking assistance[J]. The International Journal of Robotics Research, 2015, 34(6): 744-762.
[100] W Wang, J Chen, Y Ji, et al. Evaluation of Lower Leg Muscle Activities During Human Walking Assisted by an Ankle Exoskeleton[J]. IEEE Transactions on Industrial Informatics, 2020, 16(11): 7168-7176.
[101] T K Uchida, A Seth, S Pouya, et al. Simulating Ideal Assistive Devices to Reduce the Metabolic Cost of Running[J]. PLOS ONE, 2016, 11(9): e0163417.
[102] S H Collins, A D Kuo. Recycling Energy to Restore Impaired Ankle Function during Human Walking[J]. PLoS ONE, 2010, 5(2): e9307.
[103] Y Ding, F A Panizzolo, C Siviy, et al. Effect of timing of hip extension assistance during loaded walking with a soft exosuit[J]. Journal of NeuroEngineering and Rehabilitation, 2016, 13(1): 87.
[104] G Huang, L Ma, H Zhu, et al. A Biologically-inspired Soft Exosuit for Knee Extension Assistance during Stair Ascent[C]//2020 5th International Conference on Advanced Robotics and Mechatronics (ICARM). Shenzhen, China: IEEE, 2020: 570-575.
[105] Y Ding, I Galiana, A T Asbeck, et al. Biomechanical and Physiological Evaluation of Multi-Joint Assistance With Soft Exosuits[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017, 25(2): 119-130.
[106] B T Quinlivan, S Lee, P Malcolm, et al. Assistance magnitude versus metabolic cost reductions for a tethered multiarticular soft exosuit[J]. Science Robotics, 2017, 2(2): eaah4416.
[107] K Seo, J Lee, Y Lee, et al. Fully autonomous hip exoskeleton saves metabolic cost of walking[C]//2016 IEEE International Conference on Robotics and Automation (ICRA). Stockholm, Sweden: IEEE, 2016: 4628-4635.
[108] C Siviy, J Bae, L Baker, et al. Offline Assistance Optimization of a Soft Exosuit for Augmenting Ankle Power of Stroke Survivors During Walking[J]. IEEE Robotics and Automation Letters, 2020, 5(2): 828-835.
[109] P Malcolm, W Derave, S Galle, et al. A Simple Exoskeleton That Assists Plantarflexion Can Reduce the Metabolic Cost of Human Walking[J]. PLOS ONE, 2013, 8(2): 7.
[110] J B Ullauri. On the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeleton[C]//2015 International Conference on Advanced Robotics (ICAR). 2015: 302-307.
[111] M Hosseini, R Meattini, A San-Millan, et al. A sEMG-Driven Soft ExoSuit Based on Twisted String Actuators for Elbow Assistive Applications[J]. IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 5(3): 8.
[112] A Christie, J G Inglis, G Kamen, et al. Relationships between surface EMG variables and motor unit[J]. Eur J Appl Physiol, 2009: 9.
[113] E A Clancy, E L Morin, R Merletti. Sampling, noise-reduction and amplitude estimation issues in surface electromyographyଝ[J]. Journal of Electromyography and Kinesiology, 2002: 16.
[114] 王乾. 基于表面肌电信号的人体步态分析及其应用[D]. 中国科学技术大学, 2013.
[115] Y Bao, Z Liu. A Fast Grid Search Method in Support Vector Regression Forecasting Time Series[M]//E Corchado, H Yin, V Botti, et al. Intelligent Data Engineering and Automated Learning – IDEAL 2006: Vol. 4224. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006: 504-511.
[116] S Lee, J Kim, L Baker, et al. Autonomous multi-joint soft exosuit with augmentation-power-based control parameter tuning reduces energy cost of loaded walking[J]. Journal of NeuroEngineering and Rehabilitation, 2018, 15(1): 66.
[117] J Mockus. Application of Bayesian approach to numerical methods of global and stochastic optimization[J]. Journal of Global Optimization, 1994, 4(4): 347-365.
[118] B Shahriari, K Swersky, Z Wang, et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization[J]. Proceedings of the IEEE, 2016, 104(1): 148-175.
[119] S Greenhill, S Rana, S Gupta, et al. Bayesian Optimization for Adaptive Experimental Design: A Review[J]. IEEE Access, 2020, 8: 13937-13948.
[120] C E Rasmussen, C K I Williams. Gaussian processes for machine learning[M]. Cambridge, Mass: MIT Press, 2006.
[121] M Seeger. Gaussian Processes for Machine Learning[J]. International Journal of Neural Systems, 14(02): 69-106.
[122] J Mockus, V Tiesis, Zilinskas. The application of Bayesian methods for seeking the extremum[J]. LCW Dixon, GP Szegö, eds. Towards Global Optimisation, 1978, 2.
[123] 崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29(10): 3068-3090.