潜在用于单侧膝受伤患者的传感引导步态同步下肢外骨骼

王东海

doi:10.1631/FITEE.2000465

Your Location：

Home >

Browse articles >

潜在用于单侧膝受伤患者的传感引导步态同步下肢外骨骼

常规文章 | Updated：2022-07-11

- 潜在用于单侧膝受伤患者的传感引导步态同步下肢外骨骼
- Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people
- 信息与电子工程前沿(英文版) 2022年23卷第6期页码：920-936
- Affiliations：
  
  1.State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan430074, China
  2.Guangdong Sygole Intelligent Technology Co., Ltd., Dongguan523808, China
- Author bio：
  
  E-mail: donghaiwang@hust.edu.cn
- Funds：
  
  the National Natural Science Foundation of China(51705167);the Dongguan Introduction Program of Leading Innovative and Entrepreneurial Talents
- DOI：10.1631/FITEE.2000465
  中图分类号： TP242.6
- 纸质出版日期：2022-06-0 ，
  
  收稿日期：2020-09-09，
  
  修回日期：2021-10-08，
- Accepted：
Scan QR Code
王东海. 潜在用于单侧膝受伤患者的传感引导步态同步下肢外骨骼[J]. 信息与电子工程前沿(英文版), 2022,23(6):920-936.

DONGHAI WANG. Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people. [J]. Frontiers of information technology & electronic engineering, 2022, 23(6): 920-936.
王东海. 潜在用于单侧膝受伤患者的传感引导步态同步下肢外骨骼[J]. 信息与电子工程前沿(英文版), 2022,23(6):920-936. DOI： 10.1631/FITEE.2000465.

DONGHAI WANG. Sensor-guided gait-synchronization lower-extremity-exoskeleton for potential application on unilateral knee-injured people. [J]. Frontiers of information technology & electronic engineering, 2022, 23(6): 920-936. DOI： 10.1631/FITEE.2000465.

摘要

本文展示了一种可潜在帮助单侧膝受伤患者正常行走的传感引导步态同步下肢外骨骼系统。外骨骼能够减轻人体体重对受伤膝下肢的负载，并维持与健康侧下肢行走步态摆动相同步。传感引导步态同步系统集成了人体传感网络，它能感知健康侧下肢的运动步态。基于测量的关节角度轨迹引导，安装电机的髋关节在行走中提起腿杆，并将膝受伤步态和健康步态以半周期延时进行同步。实验验证了下肢外骨骼的效果。本文比较了健康腿和膝受伤腿的测量关节角度轨迹、仿真的膝受力、人机交互力等方面，结果说明髋关节安装电机受控制的下肢外骨骼能够将受伤腿和体重支撑外骨骼的融合步态与健康腿步态进行同步。

Abstract

This paper presents a sensor-guided gait-synchronization system to help potential unilateral knee-injured people walk normally with a weight-supported lower-extremity-exoskeleton (LEE). This relieves the body weight loading on the knee-injured leg and synchronizes its motion with that of the healthy leg during the swing phase of walking. The sensor-guided gait-synchronization system is integrated with a body sensor network designed to sense the motion/gait of the healthy leg. Guided by the measured joint-angle trajectories

the motorized hip joint lifts the links during walking and synchronizes the knee-injured gait with the healthy gait by a half-cycle delay. The effectiveness of the LEE is illustrated experimentally. We compare the measured joint-angle trajectories between the healthy and knee-injured legs

the simulated knee forces

and the human-exoskeleton interaction forces. The results indicate that the motorized hip-controlled LEE can synchronize the motion/gait of the combined body-weight-supported LEE and injured leg with that of the healthy leg.

关键词

传感引导下肢外骨骼人体传感网络步态同步体重支撑

Keywords

Sensor-guidedLower-extremity-exoskeletonBody sensor networkGait synchronizationWeight-support

references

Barton G, Lisboa P, Lees A, et al., 2007. Gait quality assessment using self-organising artificial neural networks. Gait Post, 25(3):374-379. https://doi.org/10.1016/j.gaitpost.2006.05.003https://doi.org/10.1016/j.gaitpost.2006.05.003

Brophy R, Silvers HJ, Gonzales T, et al., 2010. Gender influences: the role of leg dominance in ACL injury among soccer players. Br J Sports Med, 44(10):694-697. https://doi.org/10.1136/bjsm.2008.051243https://doi.org/10.1136/bjsm.2008.051243

Chen B, Zhong CH, Zhao X, et al., 2019. Reference joint trajectories generation of CUHK-EXO exoskeleton for system balance in walking assistance. IEEE Access, 7:33809-33821. https://doi.org/10.1109/ACCESS.2019.2904296https://doi.org/10.1109/ACCESS.2019.2904296

Chen G, Qi P, Guo Z, et al., 2017. Gait-event-based synchronization method for gait rehabilitation robots via a bioinspired adaptive oscillator. IEEE Trans Biomed Eng, 64(6):1345-1356. https://doi.org/10.1109/TBME.2016.2604340https://doi.org/10.1109/TBME.2016.2604340

Dollar AM, Herr H, 2008. Lower extremity exoskeletons and active orthoses: challenges and state-of-the-art. IEEE Trans Robot, 24(1):144-158. https://doi.org/10.1109/TRO.2008.915453https://doi.org/10.1109/TRO.2008.915453

Gupta R, Khanna T, Masih GD, et al., 2016. Acute anterior cruciate ligament injuries in multisport elite players: demography, association, and pattern in different sports. J Clin Orthop Trauma, 7(3):187-192. https://doi.org/10.1016/j.jcot.2016.03.005https://doi.org/10.1016/j.jcot.2016.03.005

He Y, Li N, Wang C, et al., 2019. Development of a novel autonomous lower extremity exoskeleton robot for walking assistance. Front Inform Technol Electron Eng, 20(3):318-329. https://doi.org/10.1631/FITEE.1800561https://doi.org/10.1631/FITEE.1800561

Herzog W, Read LJ, 1993. Lines of action and moment arms of the major force-carrying structures crossing the human knee joint. J Anat, 182(2):213-230. https://doi.org/10.1002/ar.1092350415https://doi.org/10.1002/ar.1092350415

Hootman JM, Dick R, Agel J, 2007. Epidemiology of collegiate injuries for 15 sports: summary and recommendations for injury prevention initiatives. J Athl Train, 42(2):311-319.

Kang I, Kunapuli P, Young AJ, 2020. Real-time neural network-based gait phase estimation using a robotic hip exoskeleton. IEEE Trans Med Robot Bion, 2(1):28-37. https://doi.org/10.1109/TMRB.2019.2961749https://doi.org/10.1109/TMRB.2019.2961749

Kellis E, 2001. Tibiofemoral joint forces during maximal isokinetic eccentric and concentric efforts of the knee flexors. Clin Biomech, 16(3):229-236. https://doi.org/10.1016/s0268-0033(00)00084-xhttps://doi.org/10.1016/s0268-0033(00)00084-x

Kim H, Shin YJ, Kim J, 2017. Design and locomotion control of a hydraulic lower extremity exoskeleton for mobility augmentation. Mechatronics, 46:32-45. https://doi.org/10.1016/j.mechatronics.2017.06.009https://doi.org/10.1016/j.mechatronics.2017.06.009

Lee KM, Wang DH, 2015. Design analysis of a passive weight-support lower-extremity-exoskeleton with compliant knee-joint. Proc IEEE Int Conf on Robotics and Automation, p.5572-5577. https://doi.org/10.1109/ICRA.2015.7139978https://doi.org/10.1109/ICRA.2015.7139978

Li GY, Liu T, Yi JG, et al., 2016. The lower limbs kinematics analysis by wearable sensor shoes. IEEE Sens J, 16(8):2627-2638. https://doi.org/10.1109/JSEN.2016.2515101https://doi.org/10.1109/JSEN.2016.2515101

Li ZJ, Ren Z, Zhao KK, et al., 2020. Human-cooperative control design of a walking exoskeleton for body weight support. IEEE Trans Ind Inform, 16(5):2985-2996. https://doi.org/10.1109/TII.2019.2900121https://doi.org/10.1109/TII.2019.2900121

Lin F, Wang AS, Zhuang Y, et al., 2016. Smart insole: a wearable sensor device for unobtrusive gait monitoring in daily life. IEEE Trans Ind Inform, 12(6):2281-2291. https://doi.org/10.1109/TII.2016.2585643https://doi.org/10.1109/TII.2016.2585643

Liu Q, Qian GM, Meng W, et al., 2019. A new IMMU-based data glove for hand motion capture with optimized sensor layout. Int J Intell Robot Appl, 3:19-32. https://doi.org/10.1007/s41315-019-00085-4https://doi.org/10.1007/s41315-019-00085-4

Liu XH, Wang QN, 2020. Real-time locomotion mode recognition and assistive torque control for unilateral knee exoskeleton on different terrains. IEEE/ASME Trans Mechatron, 25(6):2722-2732. https://doi.org/10.1109/TMECH.2020.2990668https://doi.org/10.1109/TMECH.2020.2990668

Long Y, Du ZJ, Wang WD, et al., 2018. Physical human-robot interaction estimation based control scheme for a hydraulically actuated exoskeleton designed for power amplification. Front Inform Technol Electron Eng, 19(9):1076-1085. https://doi.org/10.1631/FITEE.1601667https://doi.org/10.1631/FITEE.1601667

Lugade V, Lin V, Farley A, et al., 2014. An artificial neural network estimation of gait balance control in the elderly using clinical evaluations. PLoS ONE, 9(5):e97595. https://doi.org/10.1371/journal.pone.0097595https://doi.org/10.1371/journal.pone.0097595

Malcolm P, Galle S, van den Berghe P, et al., 2018. Exoskeleton assistance symmetry matters: unilateral assistance reduces metabolic cost, but relatively less than bilateral assistance. J Neuroeng Rehabil, 15(1):74. https://doi.org/10.1186/s12984-018-0381-zhttps://doi.org/10.1186/s12984-018-0381-z

Mizukami N, Takeuchi S, Tetsuya M, et al., 2018. Effect of the synchronization-based control of a wearable robot having a non-exoskeletal structure on the hemiplegic gait of stroke patients. IEEE Trans Neur Syst Rehabil Eng, 26(5):1011-1016. https://doi.org/10.1109/TNSRE.2018.2817647https://doi.org/10.1109/TNSRE.2018.2817647

Thambyah A, Pereira BP, Wyss U, 2005. Estimation of bone-on-bone contact forces in the tibiofemoral joint during walking. Knee, 12(5):383-388. https://doi.org/10.1016/j.knee.2004.12.005https://doi.org/10.1016/j.knee.2004.12.005

Tsukahara A, Hasegawa Y, Eguchi K, et al., 2015. Restoration of gait for spinal cord injury patients using HAL with intention estimator for preferable swing speed. IEEE Trans Neur Syst Rehabil Eng, 23(2):308-318. https://doi.org/10.1109/TNSRE.2014.2364618https://doi.org/10.1109/TNSRE.2014.2364618

Uddin MZ, Hassan MM, Alsanad A, et al., 2020. A body sensor data fusion and deep recurrent neural network-based behavior recognition approach for robust healthcare. Inform Fus, 55:105-115. https://doi.org/10.1016/j.inffus.2019.08.004https://doi.org/10.1016/j.inffus.2019.08.004

Wang DH, Lee KM, Ji JJ, 2016. A passive gait-based weight- support lower extremity exoskeleton with compliant joints. IEEE Trans Robot, 32(4):933-942. https://doi.org/10.1109/TRO.2016.2572692https://doi.org/10.1109/TRO.2016.2572692

Wang TM, Pei X, Hou TG, et al., 2020. An untethered cable-driven ankle exoskeleton with plantarflexion-dorsiflexion bidirectional movement assistance. Front Inform Technol Electron Eng, 21(5):723-739. https://doi.org/10.1631/FITEE.1900455https://doi.org/10.1631/FITEE.1900455

Wang ZL, Zhao HY, Qiu S, et al., 2015. Stance-phase detection for ZUPT-aided foot-mounted pedestrian navigation system. IEEE Trans Mechatron, 20(6):3170-3181. https://doi.org/10.1109/TMECH.2015.2430357https://doi.org/10.1109/TMECH.2015.2430357

Yu H, Wang DH, Yang CJ, et al., 2010. A walking monitoring shoe system for simultaneous plantar-force measurement and gait-phase detection. Proc IEEE/ASME Int Conf on Advanced Intelligent Mechatronics, p.207-212. https://doi.org/10.1109/AIM.2010.5695868https://doi.org/10.1109/AIM.2010.5695868

Zhang C, Liu GF, Li CL, et al., 2016. Development of a lower limb rehabilitation exoskeleton based on real-time gait detection and gait tracking. Adv Mech Eng, 8(1):1-9. https://doi.org/10.1177/1687814015627982https://doi.org/10.1177/1687814015627982

Zhang T, Tran M, Huang H, 2018. Design and experimental verification of hip exoskeleton with balance capacities for walking assistance. IEEE/ASME Trans Mechatron, 23(1):274-285. https://doi.org/10.1109/TMECH.2018.2790358https://doi.org/10.1109/TMECH.2018.2790358

Zheng NQ, Fleisig GS, Escamilla RF, et al., 1998. An analytical model of the knee for estimation of internal forces during exercise. J Biomech, 31(10):963-967. https://doi.org/10.1016/S0021-9290(98)00056-6https://doi.org/10.1016/S0021-9290(98)00056-6

浏览量

Downloads

CSCD

文章被引用时，请邮件提醒。

Submit

工具集

关联资源

暂无数据