Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled pe...Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.展开更多
This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Co...This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.展开更多
This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuropla...This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury.For the exoskeleton to be easily accepted by patients who suffer from a neurological injury,we used the ISO9241-210:2010 as a methodology design process.As the first steps of the design process,design requirements were collected from previous usability tests and literature.Then,as a second step,a technological solution is proposed,and as a third step,the system was evaluated through performance and user testing.As part of the technological solution and to allow patient participation during the rehabilitation process,we have proposed a hybrid admittance control whose input is load cell or electromyography signals.The hybrid admittance control is intended for active therapy exercises,is easily implemented,and does not need musculoskeletal modeling to work.Furthermore,electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.展开更多
In this paper,a Novel Compliant Actuator(NCA)-driven Upper-Limb Exoskeleton(ULE)with force controllable,impact resistance,and back drivability is designed to ensure the safety of the subject during Human-Robot Interac...In this paper,a Novel Compliant Actuator(NCA)-driven Upper-Limb Exoskeleton(ULE)with force controllable,impact resistance,and back drivability is designed to ensure the safety of the subject during Human-Robot Interaction(HRI)processing.Based on the designed NCA-driven ULE,this paper constructs a Model Predictive Control Scheme(MPCS)for force trajectory tracking,which minimises future tracking errors by solving an optimal control problem with inequality constraints.In addition,an Error-Accumulation Improved Newton Algorithm(EAINA)is proposed to solve the MPCS for suppressing various noises and external disturbances.The proposed EAINA is theoretically proved to have small steady state for noise conditions and stability of the EAINA using Lyapunov method.Finally,experimental results verify that the proposed MPCS solved by the EAINA in the NCA-driven ULE achieves robustness,fast convergence,strong tolerance and stability for trajectory rehabilitation task.展开更多
目的探讨连续被动运动(continuous passive motion,CPM)联合作业疗法(occupational therapy,OT)对脑卒中后上肢运动功能恢复的临床效果,为优化综合康复干预方案提供依据。方法选取2024年8月至2025年8月在杭州市萧山区中医院康复科接受...目的探讨连续被动运动(continuous passive motion,CPM)联合作业疗法(occupational therapy,OT)对脑卒中后上肢运动功能恢复的临床效果,为优化综合康复干预方案提供依据。方法选取2024年8月至2025年8月在杭州市萧山区中医院康复科接受治疗的脑卒中患者70例,随机分为对照组和观察组,每组各35例。对照组接受常规康复联合CPM被动训练,观察组在此基础上增加OT,连续治疗6周。分别于治疗前和治疗后评估两组患者的上肢运动功能[Fugl-Meyer评定量表上肢部分(Fugl-Meyer assessment for upper extremity,FMA-UE)]、肌张力[改良Ashworth分级(modified Ashworth scale,MAS)]、关节活动度(range of motion,ROM)、手功能与精细操作能力[Wolf运动功能测试(Wolf motor function test,WMFT)、动作研究臂测试(action research arm test,ARAT)]及日常生活活动能力(activity of daily living,ADL),并比较两组疗效差异。结果治疗后,两组患者各项指标较治疗前均显著改善(均P<0.05),且观察组在FMA-UE各维度及总分、MAS评分、ROM、WMFT与ARAT功能评分以及改良Barthel指数(modified Barthel index,MBI)总分等方面的改善幅度均显著优于对照组(均P<0.05)。结论CPM联合OT能有效促进脑卒中患者上肢运动功能恢复,改善痉挛状态与关节活动受限,增强手部精细运动能力与生活独立性。该方法安全、可行且可临床推广,可为脑卒中后上肢功能障碍的系统康复提供有效的临床路径。展开更多
目的:探讨一款便携式柔性手部康复机器人在脑卒中患者临床与居家上肢康复中的可用性。方法:招募13例脑卒中偏瘫患者进行手部康复机器的可用性测试。患者首先在医院接受为期2周的学习与训练,随后将设备带回家进行为期6周的居家自主康复训...目的:探讨一款便携式柔性手部康复机器人在脑卒中患者临床与居家上肢康复中的可用性。方法:招募13例脑卒中偏瘫患者进行手部康复机器的可用性测试。患者首先在医院接受为期2周的学习与训练,随后将设备带回家进行为期6周的居家自主康复训练,每次33 min,每天2次。训练结束后采用系统可用性量表(system usability scale,SUS)及半结构式访谈评估机器的可用性。此外,在干预前后及训练期间通过Fugl-Meyer运动功能量表上肢部分(Fugl-Meyer assessment upper extremity,FMA-UE)、行动研究手臂测试(action research arm test,ARAT)、日常生活活动量表(activities of daily living,ADL)评估患者的上肢运动功能和日常生活自理能力。结果:13例患者SUS为(85.8±10.5)分,处于“Excellent”水平。半结构式访谈结果显示,该设备操作简便、便于携带,但在连接、硬件稳定性及训练内容丰富度等方面仍有改进空间。干预结束后,部分患者FMA-UE、ARAT、ADL评分较前有所改善,达到最小临床重要差异(mini clinical important difference,MCID)。结论:柔性手部康复机器人在脑卒中偏瘫患者的临床及居家康复中具有良好的可用性和安全性,可作为脑卒中手部康复的一种潜在有效手段。展开更多
目的:低频重复经颅磁刺激可显著改善脑卒中患者的上肢运动功能,但该技术改善脑卒中患者上肢肌肉痉挛的效果仍存在争议。因此,此次研究系统评价低频重复经颅磁刺激对脑卒中患者肢体运动功能与上肢肌肉痉挛的影响。方法:通过计算机检索Pub...目的:低频重复经颅磁刺激可显著改善脑卒中患者的上肢运动功能,但该技术改善脑卒中患者上肢肌肉痉挛的效果仍存在争议。因此,此次研究系统评价低频重复经颅磁刺激对脑卒中患者肢体运动功能与上肢肌肉痉挛的影响。方法:通过计算机检索PubMed、EMBASE、ScienceDirect、Cochrane Library、Web of Science、中国期刊全文数据库、维普全文数据库、万方数据库以及中国生物医学文献数据库,筛选出低频重复经颅磁刺激治疗脑卒中患者的随机对照试验,试验组进行低频重复经颅磁刺激治疗或低频重复经颅磁刺激联合物理治疗/常规康复治疗,对照组进行常规康复治疗、假刺激或物理治疗,采用RevMan 5.3统计软件进行Meta分析。结果:共纳入8篇高质量研究,涉及502例患者。Meta分析结果显示,与对照组相比,试验组Brunnstrom手运动功能评分、Fugl-Meyer运动功能量表评分、改良Barthel指数及Berg平衡量表评分均高于对照组[MD=1.51,95%CI(1.22,1.80),P<0.00001;MD=5.69,95%CI(3.18,8.20),P<0.00001;MD=8.55,95%CI(3.27,13.84),P=0.002;MD=5.72,95%CI(3.13,8.32),P<0.0001],两组改良Ashworth评定评分比较差异无显著性意义[MD=-0.11,95%CI(-0.17,0.85),P=0.82]。结论:低频重复经颅磁刺激能有效改善脑卒中患者的肢体运动功能,尤其是提高手运动功能、肢体整体运动功能、日常生活活动能力和平衡能力方面的效果显著,然而在改善上肢肌肉痉挛方面未显出明显优势。该结论仍需要更多具有较高方法学质量、更长干预时间的研究和随访来进一步验证。展开更多
基金funded by the King Salman Center For Disability Research,through Research Group No.KSRG-2024-468。
文摘Disability is defined as a condition that makes it difficult for a person to perform certain vital activities.In recent years,the integration of the concepts of intelligence in solving various problems for disabled persons has become more frequent.However,controlling an exoskeleton for rehabilitation presents challenges due to their nonlinear characteristics and external disturbances caused by the structure itself or the patient wearing the exoskeleton.To remedy these problems,this paper presents a novel adaptive control strategy for upper-limb rehabilitation exoskeletons,addressing the challenges of nonlinear dynamics and external disturbances.The proposed controller integrated a Radial Basis Function Neural Network(RBFNN)with a disturbance observer and employed a high-dimensional integral Lyapunov function to guarantee system stability and trajectory tracking performance.In the control system,the role of the RBFNN was to estimate uncertain signals in the dynamic model,while the disturbance observer tackled external disturbances during trajectory tracking.Artificially created scenarios for Human-Robot interactive experiments and periodically repeated reference trajectory experiments validated the controller’s performance,demonstrating efficient tracking.The proposed controller is found to achieve superior tracking accuracy with Root-Mean-Squared(RMS)errors of 0.022-0.026 rad for all joints,outperforming conventional Proportional-Integral-Derivative(PID)by 73%and Neural-Fuzzy Adaptive Control(NFAC)by 389.47%lower error.These results suggested that the RBFNN adaptive controller,coupled with disturbance compensation,could serve as an effective rehabilitation tool for upper-limb exoskeletons.These results demonstrate the superiority of the proposed method in enhancing rehabilitation accuracy and robustness,offering a promising solution for the control of upper-limb assistive devices.Based on the obtained results and due to their high robustness,the proposed control schemes can be extended to other motor disabilities,including lower limb exoskeletons.
基金supported in part by the National Natural Science Foundation of China (62173182,61773212)the Intergovernmental International Science and Technology Innovation Cooperation Key Project of Chinese National Key R&D Program (2021YFE0102700)。
文摘This paper proposes an adaptive neural network sliding mode control based on fractional-order ultra-local model for n-DOF upper-limb exoskeleton in presence of uncertainties,external disturbances and input deadzone.Considering the model complexity and input deadzone,a fractional-order ultra-local model is proposed to formulate the original dynamic system for simple controller design.Firstly,the control gain of ultra-local model is considered as a constant.The fractional-order sliding mode technique is designed to stabilize the closed-loop system,while fractional-order time-delay estimation is combined with neural network to estimate the lumped disturbance.Correspondingly,a fractional-order ultra-local model-based neural network sliding mode controller(FO-NNSMC) is proposed.Secondly,to avoid disadvantageous effect of improper gain selection on the control performance,the control gain of ultra-local model is considered as an unknown parameter.Then,the Nussbaum technique is introduced into the FO-NNSMC to deal with the stability problem with unknown gain.Correspondingly,a fractional-order ultra-local model-based adaptive neural network sliding mode controller(FO-ANNSMC) is proposed.Moreover,the stability analysis of the closed-loop system with the proposed method is presented by using the Lyapunov theory.Finally,with the co-simulations on virtual prototype of 7-DOF iReHave upper-limb exoskeleton and experiments on 2-DOF upper-limb exoskeleton,the obtained compared results illustrate the effectiveness and superiority of the proposed method.
文摘This paper presents an upper limb exoskeleton that allows cognitive(through electromyography signals)and physical user interaction(through load cells sensors)for passive and active exercises that can activate neuroplasticity in the rehabilitation process of people who suffer from a neurological injury.For the exoskeleton to be easily accepted by patients who suffer from a neurological injury,we used the ISO9241-210:2010 as a methodology design process.As the first steps of the design process,design requirements were collected from previous usability tests and literature.Then,as a second step,a technological solution is proposed,and as a third step,the system was evaluated through performance and user testing.As part of the technological solution and to allow patient participation during the rehabilitation process,we have proposed a hybrid admittance control whose input is load cell or electromyography signals.The hybrid admittance control is intended for active therapy exercises,is easily implemented,and does not need musculoskeletal modeling to work.Furthermore,electromyography signals classification models and features were evaluated to identify the best settings for the cognitive human–robot interaction.
基金supported by the National Natural Science Foundation of China(Nos.62373065,61873304,62173048,and 62106023)the Key Science and Technology Projects of Jilin Province,China(No.20230204081YY).
文摘In this paper,a Novel Compliant Actuator(NCA)-driven Upper-Limb Exoskeleton(ULE)with force controllable,impact resistance,and back drivability is designed to ensure the safety of the subject during Human-Robot Interaction(HRI)processing.Based on the designed NCA-driven ULE,this paper constructs a Model Predictive Control Scheme(MPCS)for force trajectory tracking,which minimises future tracking errors by solving an optimal control problem with inequality constraints.In addition,an Error-Accumulation Improved Newton Algorithm(EAINA)is proposed to solve the MPCS for suppressing various noises and external disturbances.The proposed EAINA is theoretically proved to have small steady state for noise conditions and stability of the EAINA using Lyapunov method.Finally,experimental results verify that the proposed MPCS solved by the EAINA in the NCA-driven ULE achieves robustness,fast convergence,strong tolerance and stability for trajectory rehabilitation task.
文摘目的探讨连续被动运动(continuous passive motion,CPM)联合作业疗法(occupational therapy,OT)对脑卒中后上肢运动功能恢复的临床效果,为优化综合康复干预方案提供依据。方法选取2024年8月至2025年8月在杭州市萧山区中医院康复科接受治疗的脑卒中患者70例,随机分为对照组和观察组,每组各35例。对照组接受常规康复联合CPM被动训练,观察组在此基础上增加OT,连续治疗6周。分别于治疗前和治疗后评估两组患者的上肢运动功能[Fugl-Meyer评定量表上肢部分(Fugl-Meyer assessment for upper extremity,FMA-UE)]、肌张力[改良Ashworth分级(modified Ashworth scale,MAS)]、关节活动度(range of motion,ROM)、手功能与精细操作能力[Wolf运动功能测试(Wolf motor function test,WMFT)、动作研究臂测试(action research arm test,ARAT)]及日常生活活动能力(activity of daily living,ADL),并比较两组疗效差异。结果治疗后,两组患者各项指标较治疗前均显著改善(均P<0.05),且观察组在FMA-UE各维度及总分、MAS评分、ROM、WMFT与ARAT功能评分以及改良Barthel指数(modified Barthel index,MBI)总分等方面的改善幅度均显著优于对照组(均P<0.05)。结论CPM联合OT能有效促进脑卒中患者上肢运动功能恢复,改善痉挛状态与关节活动受限,增强手部精细运动能力与生活独立性。该方法安全、可行且可临床推广,可为脑卒中后上肢功能障碍的系统康复提供有效的临床路径。
文摘目的:探讨一款便携式柔性手部康复机器人在脑卒中患者临床与居家上肢康复中的可用性。方法:招募13例脑卒中偏瘫患者进行手部康复机器的可用性测试。患者首先在医院接受为期2周的学习与训练,随后将设备带回家进行为期6周的居家自主康复训练,每次33 min,每天2次。训练结束后采用系统可用性量表(system usability scale,SUS)及半结构式访谈评估机器的可用性。此外,在干预前后及训练期间通过Fugl-Meyer运动功能量表上肢部分(Fugl-Meyer assessment upper extremity,FMA-UE)、行动研究手臂测试(action research arm test,ARAT)、日常生活活动量表(activities of daily living,ADL)评估患者的上肢运动功能和日常生活自理能力。结果:13例患者SUS为(85.8±10.5)分,处于“Excellent”水平。半结构式访谈结果显示,该设备操作简便、便于携带,但在连接、硬件稳定性及训练内容丰富度等方面仍有改进空间。干预结束后,部分患者FMA-UE、ARAT、ADL评分较前有所改善,达到最小临床重要差异(mini clinical important difference,MCID)。结论:柔性手部康复机器人在脑卒中偏瘫患者的临床及居家康复中具有良好的可用性和安全性,可作为脑卒中手部康复的一种潜在有效手段。
文摘目的:低频重复经颅磁刺激可显著改善脑卒中患者的上肢运动功能,但该技术改善脑卒中患者上肢肌肉痉挛的效果仍存在争议。因此,此次研究系统评价低频重复经颅磁刺激对脑卒中患者肢体运动功能与上肢肌肉痉挛的影响。方法:通过计算机检索PubMed、EMBASE、ScienceDirect、Cochrane Library、Web of Science、中国期刊全文数据库、维普全文数据库、万方数据库以及中国生物医学文献数据库,筛选出低频重复经颅磁刺激治疗脑卒中患者的随机对照试验,试验组进行低频重复经颅磁刺激治疗或低频重复经颅磁刺激联合物理治疗/常规康复治疗,对照组进行常规康复治疗、假刺激或物理治疗,采用RevMan 5.3统计软件进行Meta分析。结果:共纳入8篇高质量研究,涉及502例患者。Meta分析结果显示,与对照组相比,试验组Brunnstrom手运动功能评分、Fugl-Meyer运动功能量表评分、改良Barthel指数及Berg平衡量表评分均高于对照组[MD=1.51,95%CI(1.22,1.80),P<0.00001;MD=5.69,95%CI(3.18,8.20),P<0.00001;MD=8.55,95%CI(3.27,13.84),P=0.002;MD=5.72,95%CI(3.13,8.32),P<0.0001],两组改良Ashworth评定评分比较差异无显著性意义[MD=-0.11,95%CI(-0.17,0.85),P=0.82]。结论:低频重复经颅磁刺激能有效改善脑卒中患者的肢体运动功能,尤其是提高手运动功能、肢体整体运动功能、日常生活活动能力和平衡能力方面的效果显著,然而在改善上肢肌肉痉挛方面未显出明显优势。该结论仍需要更多具有较高方法学质量、更长干预时间的研究和随访来进一步验证。
