髋关节外骨骼的日常多地形变速行走助力方法研究

    下肢外骨骼机器人已被广泛研究用于运动辅助、医疗康复等领域，能够显著增强人体的运动能力，扩大人体的活动范围，减少行走的代谢消耗。近年来，下肢外骨骼机器人的研究不再局限于固定场景，越来越多的下肢外骨骼被研究用于日常环境帮助行动不便的人。而日常行走辅助面临着两个关键性挑战：多地形下的人体意图识别、变速行走下的自适应力矩助力。因为日常行走环境的复杂性、穿戴者行走步态的差异性，现有的下肢外骨骼存在行走意图识别方法精度较低，变速行走辅助适应性较弱的问题。本文针对上述问题，结合行走过程中的视觉信息与运动信息，研究了髋关节下肢外骨骼的多地形行走意图识别方法和变速行走自适应助力方法，实现了有效的多地形、变速行走辅助，提高了髋关节助力外骨骼在日常行走中应用的实用性和适用性。
    首先，本文针对日常出行辅助外骨骼的功能性、安全性、舒适性需求，研究了髋关节下肢助力外骨骼的系统设计，包括硬件系统设计和控制系统设计。其中，硬件系统包括外骨骼视觉模块和外骨骼助力模块；控制系统包括高层控制器、中层控制器和底层控制器。搭建了用于评估外骨骼在多地形、变速行走下的助力效果测试平台。多地形、变速行走髋关节外骨骼系统的设计与搭建，为日常多地形、变速行走助力研究提供了稳定、可靠的平台保障。
    其次，本文基于人体行走过程中的足部运动信息以及前方环境信息，研究了适用于复杂地形（平地、斜坡、楼梯、障碍路面）下的行走落足点预测方法。该方法首先训练了一个深度学习模型，用于对行走落足点的初步预测；其次，通过视觉模块获取的前方环境信息，提取了前方地形对行走的落足点约束；进一步通过行走落足约束修正初步预测落足点，实现了复杂环境下的行走落足点准确预测。通过平地、斜坡、复杂路面实验验证了所提方法在复杂地形上的有效性和准确性。
    进一步，本文基于人体行走双侧髋关节角度信息，研究了适用于变速行走的髋关节力矩估计方法，为进一步外骨骼助力提供指导。该方法首先利用大腿角度历史状态信息，估计未来状态信息，并计算角度引导单元；其次，利用行走生物力学数据优化了幂指数与符号函数模型，能够利用角度引导单元对髋关节力矩进行准确估计，实现了变速行走下的髋关节力矩估计。通过22名受试者在跑步机变速行走的生物力学数据集实验，验证了所提方法在变速行走情况下对髋关节力矩估计的有效性和准确性。
    最后，本文基于髋关节估计力矩，研究了多地形、变速行走情况下的助力方法，并展开了多地形、变速行走外骨骼助力实验。通过对比不穿外骨骼行走，穿外骨骼基线助力行走，分析了本文方法在多地形、变速行走中助力的优越性。实验结果表明，相比于不穿外骨骼行走，本文所提助力方法在多地形、 变速行走情况下平均能降低12.5%的净新陈代谢速率，能够有效帮助人体在多地形、变速情况下行走，降低行走过程中的新陈代谢，提高了髋关节助力外骨骼在日常行走中应用的实用性和适用性。

[1] 国家统计局. 第七次全国人口普查公报（第五号）[J]. 2021.
[2] 中华人民共和国国家统计局. 2010年第六次全国人口普查主要数据公报（第1号）[J]. 2011.
[3] KELLY-HAYES M. Influence of age and health behaviors on stroke risk: lessons from longitudinal studies[J]. Journal of the American Geriatrics Society, 2010, 58: S325-S328.
[4] 苏镇培, 黄如训. 脑卒中[M]. 北京:人民卫生出版社, 2001:27-45.
[5] 缪鸿石. 中枢神经系统（CNS）损伤后功能恢复的理论（二）[J]. 中国康复理论与实践, 1996(01):1-5.
[6] CHEN B, ZI B, QIN L, et al. State-of-the-art research in robotic hip exoskeletons: A general review[J]. Journal of orthopaedic translation, 2020, 20: 4-13.
[7] YOUNG A J, FERRIS D P. State of the art and future directions for lower limb robotic exoskeletons[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2016, 25(2): 171-182.
[8] STRICKLAND E. Good-bye, wheelchair[J]. IEEE Spectrum, 2012, 49(1): 30-32.
[9] ESQUENAZI A, TALATY M, PACKEL A, et al. The ReWalk powered exoskeleton to restore ambulatory function to individuals with thoracic-level motor-complete spinal cord injury[J]. American Journal of Physical Medicine & Rehabilitation, 2012, 91(11): 911-921.
[10] TSUKAHARA A, HASEGAWA Y, SANKAI Y. Standing-up motion support for paraplegic patient with Robot Suit HAL[C]//2009 IEEE International Conference on Rehabilitation Robotics. IEEE, 2009: 211-217.
[11] 北京大艾机器人科技有限公司. 产品介绍iLegs[EB/OL]. https://www.ai-robotics.cn/, 2021.
[12] 深圳市迈步机器人科技有限公司. 下肢外骨骼康复训练机器人 BEAR-H1 [EB/OL]. http://milebot.com.cn/, 2021.
[13] 上海傅利叶智能科技有限公司. ExoMotus下肢外骨骼机器人[EB/OL]. https://www.fftai.cn/product/X2.php, 2022.
[14] DING Y, PANIZZOLO F A, SIVIY C, et al. Effect of timing of hip extension assistance during loaded walking with a soft exosuit[J]. Journal of neuroengineering and rehabilitation, 2016, 13: 1-10.
[15] DING Y, KIM M, KUINDERSMA S, et al. Human-in-the-loop optimization of hip assistance with a soft exosuit during walking[J]. Science robotics, 2018, 3(15): eaar5438.
[16] QIAN Y, HAN S, WANG Y, et al. Toward improving actuation transparency and safety of a hip exoskeleton with a novel nonlinear series elastic actuator[J]. IEEE/ASME Transactions on Mechatronics, 2022, 28(1): 417-428.
[17] 深圳市迈步机器人科技有限公司. 助行机器人MAX系列[EB/OL]. http://www.milebot.com.cn/max-1/, 2023.
[18] LIM B, LEE J, JANG J, et al. Delayed output feedback control for gait assistance with a robotic hip exoskeleton[J]. IEEE Transactions on Robotics, 2019, 35(4): 1055-1062.
[19] SEO K, LEE J, PARK Y J. Autonomous hip exoskeleton saves metabolic cost of walking uphill[C]//2017 International Conference on Rehabilitation Robotics (ICORR). IEEE, 2017: 246-251.
[20] 深圳肯綮科技有限公司. Ant-C1 Pro下肢外骨骼机器人[EB/OL]. http://www.kenqingkeji.com/product_details/12.html, 2021.
[21] XUE T, WANG Z, ZHANG T, et al. Adaptive oscillator-based robust control for flexible hip assistive exoskeleton[J]. IEEE Robotics and Automation Letters, 2019, 4(4): 3318-3323.
[22] TUCKER M R, OLIVIER J, PAGEL A, et al. Control strategies for active lower extremity prosthetics and orthotics: a review[J]. Journal of neuroengineering and rehabilitation, 2015, 12: 1-30.
[23] ZHANG K, LUO J, XIAO W, et al. A subvision system for enhancing the environmental adaptability of the powered transfemoral prosthesis[J]. IEEE transactions on cybernetics, 2020, 51(6): 3285-3297.
[24] CHEN C, ZHANG K, LENG Y, et al. Unsupervised sim-to-real adaptation for environmental recognition in assistive walking[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 1350-1360.
[25] QIAN Y, WANG Y, CHEN C, et al. Predictive locomotion mode recognition and accurate gait phase estimation for hip exoskeleton on various terrains[J]. IEEE Robotics and Automation Letters, 2022, 7(3): 6439-6446.
[26] XIONG J, CHEN C, ZHANG Y, et al. A probability fusion approach for foot placement prediction in complex terrains[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 4591-4600.
[27] CHEN X, LIU Z, ZHU J, et al. Comparison of machine learning regression algorithms for foot placement prediction[C]//2021 27th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). IEEE, 2021: 169-174.
[28] LEE S W, ASBECK A. A deep learning-based approach for foot placement prediction[J]. IEEE Robotics and Automation Letters, 2023.
[29] GAO F, LIU G, LIANG F, et al. IMU-based locomotion mode identification for transtibial prostheses, orthoses, and exoskeletons[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020, 28(6): 1334-1343.
[30] AL-DABBAGH A H A, RONSSE R. A review of terrain detection systems for applications in locomotion assistance[J]. Robotics and Autonomous Systems, 2020, 133: 103628.
[31] LASCHOWSKI B, MCNALLY W, WONG A, et al. Environment classification for robotic leg prostheses and exoskeletons using deep convolutional neural networks[J]. Frontiers in Neurorobotics, 2022, 15: 730965.
[32] BRUIJN S M, VAN DIEËN J H. Control of human gait stability through foot placement[J]. Journal of The Royal Society Interface, 2018, 15(143): 20170816.
[33] BAUBY C E, KUO A D. Active control of lateral balance in human walking[J]. Journal of biomechanics, 2000, 33(11): 1433-1440.
[34] CAMARGO J, RAMANATHAN A, FLANAGAN W, et al. A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions[J]. Journal of Biomechanics, 2021, 119: 110320.
[35] LENCIONI T, CARPINELLA I, RABUFFETTI M, et al. Human kinematic, kinetic and EMG data during different walking and stair ascending and descending tasks[J]. Scientific data, 2019, 6(1): 309.
[36] KITAGAWA N, OGIHARA N. Estimation of foot trajectory during human walking by a wearable inertial measurement unit mounted to the foot[J]. Gait & posture, 2016, 45: 110-114.
[37] REFAI M I M, VAN BEIJNUM B J F, BUURKE J H, et al. Gait and dynamic balance sensing using wearable foot sensors[J]. IEEE transactions on neural systems and rehabilitation engineering, 2018, 27(2): 218-227.
[38] NG K D, MEHDIZADEH S, IABONI A, et al. Measuring gait variables using computer vision to assess mobility and fall risk in older adults with dementia[J]. IEEE journal of translational engineering in health and medicine, 2020, 8: 1-9.
[39] GUO Y, DELIGIANNI F, GU X, et al. 3-D canonical pose estimation and abnormal gait recognition with a single RGB-D camera[J]. IEEE Robotics and Automation letters, 2019, 4(4): 3617-3624.
[40] YAN Q, HUANG J, WU D, et al. Intelligent gait analysis and evaluation system based on cane robot[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2916-2926.
[41] WU F Y, ASADA H H. Implicit and intuitive grasp posture control for wearable robotic fingers: a data-driven method using partial least squares[J]. IEEE Transactions on Robotics, 2016, 32(1): 176-186.
[42] TOWNSEND M A. Biped gait stabilization via foot placement[J]. Journal of biomechanics, 1985, 18(1): 21-38.
[43] HURT C P, ROSENBLATT N, CRENSHAW J R, et al. Variation in trunk kinematics influences variation in step width during treadmill walking by older and younger adults[J]. Gait & posture, 2010, 31(4): 461-464.
[44] WANG Y, SRINIVASAN M. Stepping in the direction of the fall: the next foot placement can be predicted from current upper body state in steady-state walking[J]. Biology letters, 2014, 10(9): 20140405.
[45] CHEN X, ZHANG K, LIU H, et al. A probability distribution model-based approach for foot placement prediction in the early swing phase with a wearable imu sensor[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 2595-2604.
[46] ZHANG K, LIU H, FAN Z, et al. Foot placement prediction for assistive walking by fusing sequential 3D gaze and environmental context[J]. IEEE Robotics and Automation Letters, 2021, 6(2): 2509-2516.
[47] LIM B, KIM K, LEE J, et al. An event-driven control to achieve adaptive walking assist with gait primitives[C]//2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2015: 5870-5875.
[48] ZHANG J, FIERS P, WITTE K A, et al. Human-in-the-loop optimization of exoskeleton assistance during walking[J]. Science, 2017, 356(6344): 1280-1284.
[49] HOLGATE M A, SUGAR T G, BOHLER A W. A novel control algorithm for wearable robotics using phase plane invariants[C]//2009 IEEE International Conference on Robotics and Automation. IEEE, 2009: 3845-3850.
[50] MEDRANO R L, THOMAS G C, KEAIS C G, et al. Real-time gait phase and task estimation for controlling a powered ankle exoskeleton on extremely uneven terrain[J]. IEEE Transactions on Robotics, 2023.
[51] YOUNG A J, FOSS J, GANNON H, et al. Influence of power delivery timing on the energetics and biomechanics of humans wearing a hip exoskeleton[J]. Frontiers in bioengineering and biotechnology, 2017, 5: 4.
[52] GASPARRI G M, LUQUE J, LERNER Z F. Proportional joint-moment control for instantaneously adaptive ankle exoskeleton assistance[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(4): 751-759.
[53] BISHE S S P A, NGUYEN T, FANG Y, et al. Adaptive ankle exoskeleton control: Validation across diverse walking conditions[J]. IEEE Transactions on Medical Robotics and Bionics, 2021, 3(3): 801-812.
[54] TAN X, ZHANG B, LIU G, et al. A time-independent control system for natural human gait assistance with a soft exoskeleton[J]. IEEE Transactions on Robotics, 2022, 39(2): 1653-1667.
[55] WU Q, WANG X, DU F, et al. Design and control of a powered hip exoskeleton for walking assistance[J]. International Journal of Advanced Robotic Systems, 2015, 12(3): 18.
[56] MOLINARO D D, KANG I, CAMARGO J, et al. Subject-independent, biological hip moment estimation during multimodal overground ambulation using deep learning[J]. IEEE Transactions on Medical Robotics and Bionics, 2022, 4(1): 219-229.
[57] GIOVACCHINI F, VANNETTI F, FANTOZZI M, et al. A light-weight active orthosis for hip movement assistance[J]. Robotics and Autonomous Systems, 2015, 73: 123-134.
[58] 深圳作为科技有限公司. 智能助行机器人[EB/OL]. http://www.zuowei.com/article/295/41.html, 2021.
[59] SHIMADA H, KIMURA Y, SUZUKI T, et al. The use of positron emission tomography and
[18F]fluorodeoxyglucose for functional imaging of muscular activity during exercise with a stride assistance system[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2007, 15(3): 442-448.
[60] BUESING C, FISCH G, O’DONNELL M, et al. Effects of a wearable exoskeleton stride management assist system (SMA®) on spatiotemporal gait characteristics in individuals after stroke: a randomized controlled trial[J]. Journal of neuroengineering and rehabilitation, 2015, 12: 1-14.
[61] REBULA J R, OJEDA L V, ADAMCZYK P G, et al. Measurement of foot placement and its variability with inertial sensors[J]. Gait & posture, 2013, 38(4): 974-980.
[62] HAO M, CHEN K, FU C. Smoother-based 3-D foot trajectory estimation using inertial sensors[J]. IEEE Transactions on Biomedical engineering, 2019, 66(12): 3534-3542.
[63] QI W, WANG N, SU H, et al. DCNN based human activity recognition framework with depth vision guiding[J]. Neurocomputing, 2022, 486: 261-271.
[64] BAO T, ZAIDI S A R, XIE S, et al. Inter-subject domain adaptation for CNN-based wrist kinematics estimation using sEMG[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021, 29: 1068-1078.
[65] HUANG C, XIAO Y, XU G. Predicting human intention-behavior through EEG signal analysis using multi-scale CNN[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2020, 18(5): 1722-1729.
[66] TELEA A. An image inpainting technique based on the fast marching method[J]. Journal of graphics tools, 2004, 9(1): 23-34.
[67] NEUMANN D A. Kinesiology of the musculoskeletal system-e-book: kinesiology of the musculoskeletal system-e-book[M]. Elsevier Health Sciences, 2016.
[64] Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants.
[68] KAWAI H, OBUCHI S, WATANABE Y, et al. Association between daily living walking speed and walking speed in laboratory settings in healthy older adults[J]. International journal of environmental research and public health, 2020, 17(8): 2707.
[69] SLADE P, KOCHENDERFER M J, DELP S L, et al. Personalizing exoskeleton assistance while walking in the real world[J]. Nature, 2022, 610(7931): 277-282.
[70] CAMARGO J, RAMANATHAN A, FLANAGAN W, et al. A comprehensive, open-source dataset of lower limb biomechanics in multiple conditions of stairs, ramps, and level-ground ambulation and transitions[J]. Journal of Biomechanics, 2021, 119: 110320.
[71] ANTONELLIS P, MOHAMMADZADEH GONABADI A, MYERS S A, et al. Metabolically efficient walking assistance using optimized timed forces at the waist[J]. Science robotics, 2022, 7(64): eabh1925.
[72] MOLINARO D D, KANG I, YOUNG A J. Estimating human joint moments unifies exoskeleton control, reducing user effort[J]. Science Robotics, 2024, 9(88): eadi8852.
[73] QIAN Y, CHEN C, XIONG J, et al. Terrain-adaptive exoskeleton control with predictive gait mode recognition: a pilot study during level walking and stair ascent[J]. IEEE Transactions on Medical Robotics and Bionics, 2024.
[74] QIAN Y, YU H, FU C. Adaptive oscillator-based assistive torque control for gait asymmetry correction with a nsea-driven hip exoskeleton[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022, 30: 2906-2915.
[75] AGUIRRE-OLLINGER G, NARAYAN A, YU H. Phase-synchronized assistive torque control for the correction of kinematic anomalies in the gait cycle[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2019, 27(11): 2305-2314.