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A machine learning approach for the design optimization of a multiple magnetic and inertial sensors wearable system for the spine mobility assessment. 用于脊柱活动度评估的多磁场和惯性传感器可穿戴系统的机器学习优化设计方法。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-05 DOI: 10.1186/s12984-024-01484-w
Dalia Y Domínguez-Jiménez, Adriana Martínez-Hernández, Gustavo Pacheco-Santiago, Julio C Casasola-Vargas, Rubén Burgos-Vargas, Miguel A Padilla-Castañeda

Background: Recently, magnetic and inertial measurement units (MIMU) based systems have been applied in the spine mobility assessment; this evaluation is essential in the clinical practice for diagnosis and treatment evaluation. The available systems are limited in the number of sensors, and neither develops a methodology for the correct placement of the sensors, seeking the relevant mobility information of the spine.

Methods: This work presents a methodology for analyzing a system consisting of sixteen MIMUs to reduce the amount of information and obtain an optimal configuration that allows distinguishing between different body postures in a movement. Four machine learning algorithms were trained and assessed using data from the range of motion in three movements (Mov.1-Anterior hip flexion; Mov.2-Lateral trunk flexion; Mov.3-Axial trunk rotation) obtained from 12 patients with Ankylosing Spondylitis.

Results: The methodology identified the optimal minimal configuration for different movements. The configuration showed good accuracy in discriminating between different body postures. Specifically, it had an accuracy of 0.963 ± 0.021 for detecting when the subject is upright or bending in Mov.1, 0.944 ± 0.038 for identifying when the subject is flexed to the left or right in Mov.2, and 0.852 ± 0.097 for recognizing when the subject is rotated to the right or left in Mov.3.

Conclusions: Our results indicate that the methodology developed results in a feasible configuration for practical clinical studies and paves the way for designing specific IMU-based assessment instruments.

Trial registration: Study approved by the Local Ethics Committee of the General Hospital of Mexico "Dr. Eduardo Liceaga" (protocol code DI/03/17/471).

背景:最近,基于磁性和惯性测量单元(MIMU)的系统被应用于脊柱活动度评估;这种评估在临床实践中对诊断和治疗评估至关重要。现有系统的传感器数量有限,而且都没有制定正确放置传感器的方法,以寻求相关的脊柱活动度信息:本研究提出了一种分析由十六个多传感器单元组成的系统的方法,以减少信息量,获得最佳配置,从而区分运动中的不同身体姿势。利用从 12 名强直性脊柱炎患者处获得的三个动作(Mov.1-髋关节前屈;Mov.2-躯干侧屈;Mov.3-躯干轴向旋转)的运动范围数据,对四种机器学习算法进行了训练和评估:该方法确定了不同运动的最佳最小配置。该配置在区分不同身体姿势方面表现出良好的准确性。具体地说,在运动 1 中,该配置检测主体直立或弯曲的准确率为 0.963 ± 0.021;在运动 2 中,该配置识别主体向左或向右弯曲的准确率为 0.944 ± 0.038;在运动 3 中,该配置识别主体向右或向左旋转的准确率为 0.852 ± 0.097:我们的结果表明,所开发的方法为实际临床研究提供了可行的配置,并为设计基于 IMU 的特定评估工具铺平了道路:研究获得了墨西哥 "爱德华多-利萨加博士 "总医院地方伦理委员会的批准(方案代码 DI/03/17/471)。
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引用次数: 0
Detecting muscle fatigue among community-dwelling senior adults with shape features of the probability density function of sEMG. 利用 sEMG 概率密度函数的形状特征检测社区老年人的肌肉疲劳。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-04 DOI: 10.1186/s12984-024-01497-5
Jiarui Ou, Na Li, Haoru He, Jiayuan He, Le Zhang, Ning Jiang

Background: Physical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.

Methods: sEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.

Results: Statistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.

Conclusion: This study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG's PDF features in detecting muscle fatigue among the elderly.

背景:体育锻炼是老年人身心健康的重要方法。然而,过度劳累会导致跌倒风险增加、严重受伤和生活质量下降。因此,简单有效的运动疲劳监测方法非常必要,尤其是在社区环境中。本研究的目的是探索在社区环境中使用表面肌电图(sEMG)特征(包括概率密度函数(PDF)的峰度和偏度)实时检测运动引起的疲劳的可能性,以解决常用 sEMG 特征灵敏度低和计算复杂度高的问题。从 25 秒的非疲劳稳定阶段和 25 秒的疲劳数据中计算出了 sEMG 的 PDF 形状特征,即峰度和偏度,以进行比较。我们进行了统计检验,以比较和检验这些特征的显著性。我们进一步提出了一种新的疲劳指标,即信道平均峰度的时均峰度(TMK),用于检测疲劳,其计算复杂度相对较低,在社区环境中具有足够的灵敏度。对 TMK 的性能进行了方差分析和事后分析:结果:非疲劳期和疲劳期的峰度和偏度在统计学上存在明显差异,当接近疲劳期时,峰度和偏度值会增加。与样本熵相比,TMK 的计算复杂度更低,在时间方面对疲劳的检测也更灵敏:本研究调查了社区实验中老年人在进行手握运动时通过 sEMG 信号的 PDF 形状特征来识别肌肉疲劳的情况。结果表明,疲劳时峰度发生了明显变化,这表明在社区实验中,PDF 形状特征是合适、方便的肌肉疲劳检测器。所提出的指标 TMK 显示了在社区环境中以有限的计算复杂性跟踪肌肉疲劳随时间变化的潜在灵敏度,突出了 sEMG 的 PDF 特征在检测老年人肌肉疲劳方面的前景。
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引用次数: 0
Immersive virtual reality for learning exoskeleton-like virtual walking: a feasibility study. 沉浸式虚拟现实技术用于学习类似外骨骼的虚拟行走:一项可行性研究。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-01 DOI: 10.1186/s12984-024-01482-y
Antonio Rodríguez-Fernández, Alex van den Berg, Salvatore Luca Cucinella, Joan Lobo-Prat, Josep M Font-Llagunes, Laura Marchal-Crespo

Purpose: Virtual Reality (VR) has proven to be an effective tool for motor (re)learning. Furthermore, with the current commercialization of low-cost head-mounted displays (HMDs), immersive virtual reality (IVR) has become a viable rehabilitation tool. Nonetheless, it is still an open question how immersive virtual environments should be designed to enhance motor learning, especially to support the learning of complex motor tasks. An example of such a complex task is triggering steps while wearing lower-limb exoskeletons as it requires the learning of several sub-tasks, e.g., shifting the weight from one leg to the other, keeping the trunk upright, and initiating steps. This study aims to find the necessary elements in VR to promote motor learning of complex virtual gait tasks.

Methods: In this study, we developed an HMD-IVR-based system for training to control wearable lower-limb exoskeletons for people with sensorimotor disorders. The system simulates a virtual walking task of an avatar resembling the sub-tasks needed to trigger steps with an exoskeleton. We ran an experiment with forty healthy participants to investigate the effects of first- (1PP) vs. third-person perspective (3PP) and the provision (or not) of concurrent visual feedback of participants' movements on the walking performance - namely number of steps, trunk inclination, and stride length -, as well as the effects on embodiment, usability, cybersickness, and perceived workload.

Results: We found that all participants learned to execute the virtual walking task. However, no clear interaction of perspective and visual feedback improved the learning of all sub-tasks concurrently. Instead, the key seems to lie in selecting the appropriate perspective and visual feedback for each sub-task. Notably, participants embodied the avatar across all training modalities with low cybersickness levels. Still, participants' cognitive load remained high, leading to marginally acceptable usability scores.

Conclusions: Our findings suggest that to maximize learning, users should train sub-tasks sequentially using the most suitable combination of person's perspective and visual feedback for each sub-task. This research offers valuable insights for future developments in IVR to support individuals with sensorimotor disorders in improving the learning of walking with wearable exoskeletons.

目的:虚拟现实(VR)已被证明是运动(再)学习的有效工具。此外,随着目前低成本头戴式显示器(HMD)的商业化,沉浸式虚拟现实(IVR)已成为一种可行的康复工具。然而,如何设计沉浸式虚拟环境来加强运动学习,尤其是支持复杂运动任务的学习,仍然是一个未决问题。穿戴下肢外骨骼时触发台阶就是此类复杂任务的一个例子,因为它需要学习多个子任务,如将重心从一条腿转移到另一条腿、保持躯干直立和启动台阶。本研究旨在找到促进复杂虚拟步态任务的运动学习所需的虚拟现实元素:在这项研究中,我们开发了一个基于 HMD-IVR 的系统,用于训练感知运动障碍患者控制可穿戴式下肢外骨骼。该系统模拟了一个虚拟化身的行走任务,类似于使用外骨骼触发步法所需的子任务。我们对 40 名健康参与者进行了实验,以研究第一人称视角(1PP)与第三人称视角(3PP)以及提供(或不提供)参与者动作的并行视觉反馈对步行表现(即步数、躯干倾斜度和步长)的影响,以及对体现、可用性、晕机和感知工作量的影响:我们发现,所有参与者都学会了执行虚拟行走任务。结果:我们发现,所有参与者都学会了执行虚拟行走任务。然而,视角和视觉反馈之间并没有明显的交互作用,无法同时提高所有子任务的学习效果。相反,关键似乎在于为每个子任务选择适当的视角和视觉反馈。值得注意的是,在所有训练模式中,参与者都化身为化身,晕机程度很低。尽管如此,参与者的认知负荷仍然很高,导致可用性得分勉强可以接受:我们的研究结果表明,为了最大限度地提高学习效果,用户应该使用最合适的个人视角和视觉反馈组合,对每个子任务进行顺序训练。这项研究为 IVR 的未来发展提供了有价值的见解,可帮助患有感知运动障碍的人提高使用可穿戴外骨骼行走的学习能力。
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引用次数: 0
Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking. 比较协同外推法和静态优化法,以估计步行过程中多个未测量的肌肉激活。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-11-01 DOI: 10.1186/s12984-024-01490-y
Di Ao, Benjamin J Fregly

Background: Calibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown.

Methods: This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces.

Results: On average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 0.53 vs. 0.07). SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to "gold standard" calibrated model parameter values.

Conclusions: These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

背景:校准肌电图(EMG)驱动的肌肉骨骼模型可以让人们深入了解难以或无法通过实验测量的内部数量(如肌肉力量)。然而,由于需要所有相关肌肉的肌电图数据,这对肌电图驱动建模方法的广泛应用构成了重大障碍。协同外推法(SynX)是一种计算方法,它能在 EMG 驱动模型校准过程中以合理的精度估算单个缺失的 EMG 信号,但它在估算大量缺失的 EMG 信号时的性能仍是未知数:本研究评估了 SynX 使用八个测量到的肌电信号估算同一条腿在行走过程中与八个缺失肌电信号相关的肌肉激活和力的准确性,同时进行肌电驱动模型校准。从两个中风后的人身上收集的实验步态数据(包括每条腿 16 个通道的 EMG 数据)被用于校准 EMG 驱动的肌肉骨骼模型,为评估目的提供 "黄金标准 "肌肉激活和力量。然后使用 SynX 预测与八个缺失的 EMG 信号相关的肌肉激活和力量,同时校准 EMG 驱动的模型参数值。由于静态优化(SO)的广泛应用,它也被用于按比例通用肌肉骨骼模型,以估算相同的肌肉激活度和力。使用均方根误差(RMSE)来量化振幅误差,使用相关系数 r 值来量化形状相似性,评估 SynX 和 SO 的估计精度,每种精度都是根据 "黄金标准 "肌肉激活度和力计算得出的:平均而言,与 SO 相比,同步校准模型的 SynX 对未测量的肌肉激活(RMSE 0.08 vs. 0.15,r 值 0.55 vs. 0.12)和力(RMSE 101.3 N vs. 174.4 N,r 值 0.53 vs. 0.07)产生的振幅和形状估计更准确。SynX 校准了所有肌肉的希尔型肌肉-肌腱模型参数值,测量肌肉的激活动态模型参数值与 "金标准 "校准模型参数值相似:这些研究结果表明,SynX 可以为所有重要的下肢肌肉校准 EMG 驱动的肌肉骨骼模型,只需精心选择 8 个 EMG 信号,并最终为设计针对行动障碍的个性化康复和手术干预措施做出贡献。
{"title":"Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking.","authors":"Di Ao, Benjamin J Fregly","doi":"10.1186/s12984-024-01490-y","DOIUrl":"10.1186/s12984-024-01490-y","url":null,"abstract":"<p><strong>Background: </strong>Calibrated electromyography (EMG)-driven musculoskeletal models can provide insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unknown.</p><p><strong>Methods: </strong>This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing \"gold standard\" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) applied to a scaled generic musculoskeletal model was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to \"gold standard\" muscle activations and forces.</p><p><strong>Results: </strong>On average, compared to SO, SynX with simultaneous model calibration produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15, r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4 N, r value 0.53 vs. 0.07). SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to \"gold standard\" calibrated model parameter values.</p><p><strong>Conclusions: </strong>These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.</p>","PeriodicalId":16384,"journal":{"name":"Journal of NeuroEngineering and Rehabilitation","volume":"21 1","pages":"194"},"PeriodicalIF":5.2,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11529311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142558072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Rest the brain to learn new gait patterns after stroke. 让大脑休息,以便在中风后学习新的步态。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-29 DOI: 10.1186/s12984-024-01494-8
Chandramouli Krishnan, Thomas E Augenstein, Edward S Claflin, Courtney R Hemsley, Edward P Washabaugh, Rajiv Ranganathan

Background: The ability to relearn a lost skill is critical to motor recovery after a stroke. Previous studies indicate that stroke typically affects the processes underlying motor control and execution but not the learning of those skills. However, these studies could be confounded by the presence of significant motor impairments. Furthermore, prior research involving the upper extremity indicates that stroke survivors have an advantage in offline motor learning when compared with controls. However, this has not been examined using motor acuity tasks (i.e., tasks focusing on the quality of executed actions) that have direct functional relevance to rehabilitation.

Objective: Investigate how stroke affects leg motor skill learning during walking in stroke survivors.

Methods: Twenty-five participants (10 stroke; 15 controls) were recruited for this prospective, case-control study. Participants learned a novel foot-trajectory tracking task on two consecutive days while walking on a treadmill. The task necessitated greater hip and knee flexion during the swing phase of the gait. Online learning was measured by comparing tracking error at the beginning and end of each practice session, offline (rest-driven) learning was measured by comparing the end of the first practice session to the beginning of the second, and retention was measured by comparing the beginning of the first practice session to the beginning of the second. Online learning, offline learning, and retention were compared between the stroke survivors and uninjured controls.

Results: Stroke survivors improved their tracking performance on the first day (p = 0.033); however, the amount of learning in stroke survivors was lower in comparison with the control group on both days (p ≤ 0.05). Interestingly, stroke survivors showed higher offline learning gains when compared with uninjured controls (p = 0.011).

Conclusions: Even stroke survivors with no perceivable motor impairments have difficulty acquiring new motor skills related to walking, which may be related to the underlying neural damage caused at the time of stroke. Furthermore, stroke survivors may require longer training with adequate rest to acquire new motor skills.

背景:重新学习失去的技能的能力对中风后的运动恢复至关重要。以往的研究表明,中风通常会影响运动控制和执行的基础过程,但不会影响这些技能的学习。然而,这些研究可能因存在明显的运动障碍而受到混淆。此外,先前涉及上肢的研究表明,与对照组相比,中风幸存者在离线运动学习方面具有优势。但是,还没有使用与康复有直接功能相关性的运动敏锐度任务(即侧重于执行动作质量的任务)对这一点进行研究:调查中风如何影响中风幸存者在行走过程中的腿部运动技能学习:这项前瞻性病例对照研究招募了 25 名参与者(10 名中风患者;15 名对照组患者)。参与者连续两天在跑步机上行走时学习一项新颖的脚部轨迹跟踪任务。这项任务要求参与者在步态的摆动阶段加大髋关节和膝关节的屈曲幅度。在线学习通过比较每次练习开始和结束时的跟踪误差进行测量,离线(休息驱动)学习通过比较第一次练习结束和第二次练习开始时的跟踪误差进行测量,而保持则通过比较第一次练习开始和第二次练习开始时的跟踪误差进行测量。比较了中风幸存者和未受伤对照组的在线学习、离线学习和保持率:结果:中风幸存者在第一天的追踪表现有所改善(p = 0.033);然而,与对照组相比,中风幸存者两天的学习量都较低(p ≤ 0.05)。有趣的是,与未受伤的对照组相比,中风幸存者的离线学习收益更高(p = 0.011):结论:即使没有明显运动障碍的中风幸存者也很难获得与行走相关的新运动技能,这可能与中风时造成的潜在神经损伤有关。此外,中风幸存者可能需要更长时间的训练和充分的休息才能掌握新的运动技能。
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引用次数: 0
Instrumented assessment of lower and upper motor neuron signs in amyotrophic lateral sclerosis using robotic manipulation: an explorative study. 利用机器人操作对肌萎缩性脊髓侧索硬化症患者的下运动神经元和上运动神经元体征进行仪器评估:一项探索性研究。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-29 DOI: 10.1186/s12984-024-01485-9
D J L Stikvoort García, B T H M Sleutjes, W Mugge, J J Plouvier, H S Goedee, A C Schouten, F C T van der Helm, L H van den Berg

Background: Amyotrophic lateral sclerosis (ALS) is a lethal progressive neurodegenerative disease characterized by upper motor neuron (UMN) and lower motor neuron (LMN) involvement. Their varying degree of involvement results in a clinical heterogenous picture, making clinical assessments of UMN signs in patients with ALS often challenging. We therefore explored whether instrumented assessment using robotic manipulation could potentially be a valuable tool to study signs of UMN involvement.

Methods: We examined the dynamics of the wrist joint of 15 patients with ALS and 15 healthy controls using a Wristalyzer single-axis robotic manipulator and electromyography (EMG) recordings in the flexor and extensor muscles in the forearm. Multi-sinusoidal torque perturbations were applied, during which participants were asked to either relax, comply or resist. A neuromuscular model was used to study muscle viscoelasticity, e.g. stiffness (k) and viscosity (b), and reflexive properties, such as velocity, position and force feedback gains (kv, kp and kf, respectively) that dominated the responses. We further obtained clinical signs of LMN (muscle strength) and UMN (e.g. reflexes, spasticity) dysfunction, and evaluated their relation with the estimated neuromuscular model parameters.

Results: Only force feedback gains (kf) were elevated in patients (p = 0.033) compared to controls. Higher kf, as well as the resulting reflexive torque (Tref), were both associated with more severe UMN dysfunction in the examined arm (p = 0.040 and p < 0.001). Patients with UMN symptoms in the examined arm had increased kf and Tref compared to controls (both p = 0.037). Neither of these measures was related to muscle strength, but muscle stiffness (k) was lower in weaker patients (p = 0.012). All these findings were obtained from the relaxed test. No differences were observed during the instructions comply and resist.

Conclusions: This findings are proof-of-concept that instrumented assessment using robotic manipulation is a feasible technique in ALS, which may provide quantitative, operator-independent measures relating to UMN symptoms. Elevated force feedback gains, driving larger reflexive muscle torques, appear to be particularly indicative of clinically established levels of UMN dysfunction in the examined arm.

背景:肌萎缩侧索硬化症(ALS)是一种致命的进行性神经退行性疾病,其特征是上运动神经元(UMN)和下运动神经元(LMN)受累。上运动神经元和下运动神经元受累程度不同,临床表现也不尽相同,因此对 ALS 患者上运动神经元体征的临床评估往往具有挑战性。因此,我们探讨了使用机器人操作进行仪器评估是否有可能成为研究 UMN 受累迹象的重要工具:我们使用 Wristalyzer 单轴机器人操纵器和前臂屈肌和伸肌的肌电图(EMG)记录,检查了 15 名 ALS 患者和 15 名健康对照者的腕关节动态。研究人员施加了多正弦力矩扰动,在此期间要求参与者放松、顺从或抵抗。我们使用神经肌肉模型来研究肌肉的粘弹性,如硬度(k)和粘度(b),以及反射特性,如主导反应的速度、位置和力反馈增益(分别为 kv、kp 和 kf)。我们进一步获得了 LMN(肌力)和 UMN(如反射、痉挛)功能障碍的临床表现,并评估了它们与估计的神经肌肉模型参数之间的关系:结果:与对照组相比,患者只有力反馈增益(kf)升高(p = 0.033)。较高的 kf 以及由此产生的反射力矩(Tref)都与受检手臂更严重的 UMN 功能障碍有关(p = 0.040 和 p 结论:这一研究结果证明了一个概念,即患者的 UMN 功能障碍比对照组更严重:这一研究结果证明,使用机器人操作进行仪器评估是一种可行的 ALS 技术,可提供与 UMN 症状相关的独立于操作者的定量测量。力反馈增益的升高会驱动更大的反射性肌肉力矩,这似乎特别表明受检手臂的UMN功能障碍已达到临床确定的水平。
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引用次数: 0
Effects of virtual reality rehabilitation after spinal cord injury: a systematic review and meta-analysis. 脊髓损伤后虚拟现实康复的效果:系统回顾和荟萃分析。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-28 DOI: 10.1186/s12984-024-01492-w
Likun Wang, Hong Zhang, Haibo Ai, Yuxi Liu

Background: Spinal cord injury (SCI) is a common neurological condition marked by damage to the spinal cord. In the field of neurological rehabilitation, virtual reality (VR) is increasingly employed for evaluating and addressing the physical limitations caused by SCI. This study aimed to describe and calculate the effect sizes of virtual reality intervention (VR) on the functional performance of SCI.

Methods: We searched PubMed, Embase, Web of Science, and Cochrane Library to identify articles published before October 30, 2023, that addressed the intervention of SCI using virtual reality technology. We excluded from the meta-analysis articles that did not provide enough data to evaluate the association between virtual reality intervention and spinal cord injury. The RevMan 5.4 statistical software was used for data analysis.

Results: We included 16 articles in the systematic review and pooled 9 for the meta-analysis, which were 5 randomized controlled trials (RCTs) and 4 non-RCTs, including 248 subjects. The outcome measure of the walking index for spinal cord injury, limits of stability testing and berg balance scale scores improved in non-RCTs.

Conclusion: VR has shown promise in enhancing walking ability and balance function in individuals with SCI. However, the existing evidence for VR interventions in SCI patients remains limited, highlighting the necessity for future studies in this area.

背景:脊髓损伤(SCI)是一种以脊髓损伤为特征的常见神经系统疾病。在神经康复领域,虚拟现实(VR)被越来越多地用于评估和解决 SCI 造成的身体限制。本研究旨在描述和计算虚拟现实干预(VR)对 SCI 功能表现的影响大小:我们对 PubMed、Embase、Web of Science 和 Cochrane Library 进行了检索,以找出 2023 年 10 月 30 日之前发表的使用虚拟现实技术干预 SCI 的文章。我们在荟萃分析中排除了那些没有提供足够数据来评估虚拟现实干预与脊髓损伤之间关系的文章。数据分析采用 RevMan 5.4 统计软件:我们在系统综述中纳入了 16 篇文章,并汇总了 9 篇文章进行荟萃分析,其中包括 5 项随机对照试验(RCT)和 4 项非 RCT,共纳入 248 名受试者。在非随机对照试验中,脊髓损伤行走指数、稳定性极限测试和伯格平衡量表评分的结果均有所改善:结论:虚拟现实技术有望提高脊髓损伤患者的行走能力和平衡功能。然而,对 SCI 患者进行 VR 干预的现有证据仍然有限,这凸显了未来在这一领域开展研究的必要性。
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引用次数: 0
Dynamical network-based evaluation for neuromuscular dysfunction in stroke-induced hemiplegia during standing. 基于动态网络评估中风偏瘫患者站立时的神经肌肉功能障碍
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-24 DOI: 10.1186/s12984-024-01488-6
Jinping Li, Na Zhang, Ying Xu, Juan Wang, Xianglian Kang, Runing Ji, Ke Li, Ying Hou

Background: A given movement requires precise coordination of multiple muscles under the control of center nervous system. However, detailed knowledge about the changing characteristics of neuromuscular control for multi-muscle coordination in post-stroke hemiplegic patients during standing is still lacking. This study aimed to investigate the hemiplegia-linked neuromuscular dysfunction during standing from the perspective of multi-muscle dynamical coordination by utilizing a novel network approach - weighted recurrence network (WRN).

Methods: Ten male hemiplegic patients with first-ever stroke and 10 age-matched healthy male adults were instructed to stand on a platform quietly for 30 s with eyes opened and eyes closed, respectively. The WRN was constructed based on the surface electromyography signals of 16 muscles from trunk, hips, thighs and calves. Relevant topological parameters, including clustering coefficient (C) and average shortest path length (L), were extracted to evaluate the dynamical coordination of multiple muscles. A measure of node centrality in network theory, degree of centrality (DC), was innovatively introduced to assess the contribution of single muscle in the multi-muscle dynamical coordination. The standing-related assessment metric, center of pressure (COP), was provided by the platform directly.

Results: Results showed that the post-stroke hemiplegic patients stood with remarkably higher similarity of muscle activation and more coupled intermuscular dynamics, characterized by higher C and lower L than the healthy subjects (p < 0.05). The DC values and rankings of back, hip and calf muscles on the affected side were significantly decreased, whereas those on the unaffected side were significantly increased in hemiplegia group compared with the healthy group (p < 0.05). Without visual feedback, subjects exhibited enhanced muscle coordination and increased muscle involvement (p < 0.05). A decrease in C and an increase in L of WRN were observed with decreased COP areas (p < 0.05).

Conclusions: These findings revealed that stroke-induced hemiplegia could significantly influence the neuromuscular control, which was manifested as more coupled intermuscular dynamics, abnormal deactivation of muscles on affected side and compensation of muscles on unaffected side from the perspective of multi-muscle coordination. Enhanced multi-muscle dynamical coordination was strongly associated with impaired postural control. This study provides a novel analytical tool for evaluation of neuromuscular dysfunction and specification of responsible muscles for impaired postural control in stroke-induced hemiplegic patients, and could be potentially applied in clinical practice.

背景:一个特定的动作需要在中枢神经系统的控制下精确协调多块肌肉。然而,有关中风后偏瘫患者站立时多肌肉协调的神经肌肉控制变化特征的详细知识仍然缺乏。本研究旨在利用新型网络方法--加权复发网络(WRN),从多肌肉动态协调的角度研究站立时与偏瘫相关的神经肌肉功能障碍:方法:让 10 名首次中风的男性偏瘫患者和 10 名年龄匹配的健康男性成年人分别睁眼和闭眼在平台上安静站立 30 秒。根据躯干、臀部、大腿和小腿 16 块肌肉的表面肌电信号构建 WRN。提取了相关的拓扑参数,包括聚类系数(C)和平均最短路径长度(L),以评估多块肌肉的动态协调性。创新性地引入了网络理论中的节点中心度(DC)来评估单块肌肉在多块肌肉动态协调中的贡献。平台直接提供了与站立相关的评估指标--压力中心(COP):结果表明,与健康人相比,中风后偏瘫患者站立时肌肉激活的相似性明显更高,肌肉间动力学耦合性更强,表现为更高的 C 值和更低的 L 值(p 结论:中风后偏瘫患者站立时肌肉激活的相似性明显更高,肌肉间动力学耦合性更强,表现为更高的 C 值和更低的 L 值:这些发现揭示了脑卒中引起的偏瘫会显著影响神经肌肉控制,从多肌肉协调的角度来看,表现为更多的耦合肌间动力学、患侧肌肉异常失活和未受影响侧肌肉的代偿。多肌肉动态协调的增强与姿势控制受损密切相关。这项研究为评估中风偏瘫患者的神经肌肉功能障碍和确定姿势控制受损的责任肌肉提供了一种新的分析工具,并有可能应用于临床实践。
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引用次数: 0
Human factors considerations of Interaction between wearers and intelligent lower-limb prostheses: a prospective discussion. 佩戴者与智能下肢假肢之间互动的人为因素考虑:前瞻性讨论。
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-22 DOI: 10.1186/s12984-024-01475-x
Xiaolu Bai, Jing Yuan, Ming Liu, He Huang, Jing Feng

Compared to traditional lower-limb prostheses (LLPs), intelligent LLPs are more versatile devices with emerging technologies, such as microcontrollers and user-controlled interfaces (UCIs). As emerging technologies allow a higher level of automation and more involvement from wearers in the LLP setting adjustments, the previous framework established to study human factors elements that affect wearer-LLP interaction may not be sufficient to understand the new elements (e.g., transparency) and dynamics in this interaction. In addition, the increased complexity of interaction amplifies the limitations of the traditional evaluation approaches of wearer-LLP interaction. Therefore, to ensure wearer acceptance and adoption, from a human factors perspective, we propose a new framework to introduce elements and usability requirements for the wearer-LLP interaction. This paper organizes human factors elements that appear with the development of intelligent LLP technologies into three aspects: wearer, device, and task by using a classic model of the human-machine systems. By adopting Nielsen's five usability requirements, we introduce learnability, efficiency, memorability, use error, and satisfaction into the evaluation of wearer-LLP interaction. We identify two types of wearer-LLP interaction. The first type, direct interaction, occurs when the wearer continuously interacts with the intelligent LLP (primarily when the LLP is in action); the second type, indirect interaction, occurs when the wearer initiates communication with the LLP usually through a UCI to address the current or foreseeable challenges. For each type of interaction, we highlight new elements, such as device transparency and prior knowledge of the wearer with the UCI. In addition, we redefine the usability goals of two types of wearer-LLP interaction with Nelson's five usability requirements and review methods to evaluate the interaction. Researchers and designers for intelligent LLPs should consider the new device elements that may additionally influence wearers' acceptance and the need to interpret findings within the constraints of the specific wearer and task characteristics. The proposed framework can also be used to organize literature and identify gaps for future directions. By adopting the holistic usability requirements, findings across empirical studies can be more comparable. At the end of this paper, we discuss research trends and future directions in the human factors design of intelligent LLPs.

与传统的下肢假肢(LLP)相比,智能 LLP 是采用微控制器和用户控制界面(UCI)等新兴技术的多功能设备。由于新兴技术可实现更高水平的自动化,佩戴者可更多地参与 LLP 的设置调整,以往为研究影响佩戴者与 LLP 互动的人为因素而建立的框架可能不足以理解这种互动中的新因素(如透明度)和动态变化。此外,交互复杂性的增加也扩大了佩戴者与 LLP 交互的传统评估方法的局限性。因此,为了确保佩戴者的接受度和采用率,我们从人为因素的角度出发,提出了一个新的框架,以引入佩戴者-LLP 交互的要素和可用性要求。本文通过使用经典的人机系统模型,将随着智能 LLP 技术的发展而出现的人为因素分为三个方面:佩戴者、设备和任务。通过采用尼尔森的五个可用性要求,我们将可学习性、效率、可记忆性、使用错误和满意度引入到穿戴者与 LLP 交互的评估中。我们确定了佩戴者与 LLP 交互的两种类型。第一种是直接交互,即穿戴者与智能龙8娱乐long8持续交互(主要是在龙8娱乐long8运行时);第二种是间接交互,即穿戴者与龙8娱乐long8启动通信,通常是通过一个UCI来解决当前或可预见的挑战。对于每种类型的交互,我们都强调了新的要素,如设备的透明度和佩戴者对 UCI 的事先了解。此外,我们还根据纳尔逊的五项可用性要求重新定义了两种穿戴者-LLP交互的可用性目标,并回顾了评估交互的方法。智能LLP的研究人员和设计人员应该考虑到可能会额外影响穿戴者接受度的新设备元素,以及在特定穿戴者和任务特征的限制下解释研究结果的必要性。建议的框架还可用于整理文献,找出未来发展方向的差距。通过采用整体可用性要求,实证研究的结果更具可比性。在本文的最后,我们讨论了智能 LLP 人因设计的研究趋势和未来方向。
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引用次数: 0
Brain-movement relationship during upper-limb functional movements in chronic post-stroke patients. 慢性中风后患者上肢功能运动时的脑动关系
IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL Pub Date : 2024-10-22 DOI: 10.1186/s12984-024-01461-3
C O Muller, G Faity, M Muthalib, S Perrey, G Dray, B Xu, J Froger, D Mottet, I Laffont, M Delorme, K Bakhti

Background: Following a stroke, brain activation reorganisation, movement compensatory strategies, motor performance and their evolution through rehabilitation are matters of importance for clinicians. Two non-invasive neuroimaging methods allow for recording task-related brain activation: functional near-infrared spectroscopy (fNIRS) and electroencephalography (fEEG), respectively based on hemodynamic response and neuronal electrical activity. Their simultaneous measurement during movements could allow a better spatiotemporal mapping of brain activation, and when associated to kinematic parameters could unveil underlying mechanisms of functional upper limb (UL) recovery. This study aims to depict the motor cortical activity patterns using combined fNIRS-fEEG and their relationship to motor performance and strategies during UL functional tasks in chronic post-stroke patients.

Methods: Twenty-one healthy old adults and 21 chronic post-stroke patients were recruited and completed two standardised functional tasks of the UL: a paced-reaching task where they had to reach a target in front of them and a circular steering task where they had to displace a target using a hand-held stylus, as fast as possible inside a circular track projected on a computer screen. The activity of the bilateral motor cortices and motor performance were recorded simultaneously utilizing a fNIRS-fEEG and kinematics platform.

Results and conclusions: Kinematic analysis revealed that post-stroke patients performed worse in the circular steering task and used more trunk compensation in both tasks. Brain analysis of bilateral motor cortices revealed that stroke individuals over-activated during the paretic UL reaching task, which was associated with more trunk usage and a higher level of impairment (clinical scores). This work opens up avenues for using such combined methods to better track and understand brain-movement evolution through stroke rehabilitation.

背景:中风后,大脑激活重组、运动补偿策略、运动表现及其在康复过程中的演变对临床医生来说非常重要。有两种非侵入性神经成像方法可以记录与任务相关的大脑激活:功能性近红外光谱(fNIRS)和脑电图(fEEG),它们分别基于血液动力学反应和神经元电活动。在运动过程中同时测量这两项技术可以更好地绘制大脑激活的时空图谱,并与运动参数相关联,从而揭示上肢功能恢复的内在机制。本研究旨在利用 fNIRS-fEEG 组合描绘运动皮层活动模式及其与慢性中风后患者在 UL 功能任务中的运动表现和策略之间的关系:研究招募了 21 名健康的老年人和 21 名慢性中风后遗症患者,让他们完成两项标准化的 UL 功能任务:一项是有节奏的前伸任务,要求他们够到前方的目标;另一项是环形转向任务,要求他们用手持式触控笔以最快的速度在投影在电脑屏幕上的环形轨道内移动目标。利用 fNIRS-fEEG 和运动学平台同时记录了双侧运动皮层的活动和运动表现:运动学分析表明,脑卒中后患者在环形转向任务中表现较差,在这两项任务中都使用了更多的躯干补偿。对双侧运动皮层的大脑分析显示,中风患者在瘫痪UL伸手任务中过度激活,这与更多的躯干使用和更高的损伤程度(临床评分)有关。这项研究为使用这种综合方法更好地跟踪和了解中风康复过程中大脑运动的演变开辟了道路。
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引用次数: 0
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Journal of NeuroEngineering and Rehabilitation
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