Gait & posture最新文献_第2页

Interpreting common standing postural sway measures 解释常见的站立姿势摇摆措施。

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-14 DOI: 10.1016/j.gaitpost.2025.110084

Mark S. Redfern

Background

There are numerous measures of standing balance using force plates presented in the literature. Two of the most common measures are the root-mean-square (RMS) and mean velocity (MV) of the Center of Pressure (CoP). The purpose of this short communication is to promote a greater understanding of the implications of these two common metrics.

Research question

What aspects of postural control do the RMS and MV of the CoP measure?

Methods

CoP time series measured with a force plate and CoM calculated from motion capture during quiet standing in the AP and ML directions were analyzed. The RMS and MV of the CoP, Center of Mass (CoM), and the difference between the CoP and the CoM (CoP-CoM) were calculated. The relationships among these measures are presented.

Results

The CoP_RMS was highly correlated with the CoM_RMS (r > .96), indicating that CoP_RMS measures the amount of sway. The CoP_MV was highly correlated with (CoP-CoM)_RMS (r > .90). The (CoP-CoM)_RMS is related to the torque generation used to maintain stability; therefore CoP_MV is related to the control effort used. The AP and ML measures do have some different characteristics due to the mechanism by which stability is maintained.

Significance

The RMS and MV of CoP are effective at capturing two different fundamental aspects of stability: the amount of sway and control effort. This highlights the importance of reporting both CoP_RMS and CoP_MV to allow for a better understanding of standing balance.

背景：文献中提出了许多使用力板测量站立平衡的方法。最常用的两种测量方法是压力中心的均方根（RMS）和平均速度（MV）。这个简短交流的目的是促进对这两个通用度量的含义的更好理解。研究问题：CoP的RMS和MV测量的是姿势控制的哪些方面？方法：分析力板测得的CoP时间序列和动作捕捉计算的安静站立时AP和ML方向的CoM。计算了质心（CoM）、质心（CoP）和质心（CoP-CoM）的均方根（RMS）和质心（MV）以及质心与质心的差值（CoP-CoM）。给出了这些措施之间的关系。结果：CoPRMS与comms高度相关（r > ）。96)，表明CoPRMS测量的是摇摆量。CoPMV与（CoP-CoM）RMS高度相关（r > .90）。（CoP-CoM）RMS与用于保持稳定性的扭矩产生有关；因此，CoPMV与所使用的控制努力有关。由于维持稳定性的机制不同，AP和ML度量确实具有一些不同的特征。意义：CoP的均方根和均方根在捕获稳定性的两个不同基本方面是有效的：摇摆量和控制努力。这突出了报告CoPRMS和CoPMV的重要性，以便更好地理解站立平衡。

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引用次数: 0

Artificial intelligence in lower limb joint moment prediction during typically developed gait: A systematic review and multilevel random-effects meta-analysis 人工智能在典型发展步态中的下肢关节力矩预测：系统回顾和多水平随机效应荟萃分析。

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-13 DOI: 10.1016/j.gaitpost.2025.110083

Jianqi Pan , Zixiang Gao , Zhanyi Zhou , Diwei Chen , Fengping Li , Julien S. Baker , Yaodong Gu

Objective

Artificial intelligence (AI) methods have been widely applied in gait analysis, yet quantitative comparisons across models and their input–output specifications remain limited. This study aims to systematically review and synthesize the existing literature to evaluate the effectiveness of AI methods in predicting lower limb joint moments during typically developed (TD) gait.

Methods

Relevant studies published before July 1, 2025, were retrieved from five databases (PubMed, Scopus, IEEE Xplore, ScienceDirect, and Web of Science) using Boolean logic operations and were screened according to predefined criteria. Risk of bias and applicability were assessed with PROBAST. Meta-analyses were performed in R using a multilevel random-effects model to examine differences in predictive performance across AI model group, signal input type, and output joints.

Results

Eleven studies involving 371 TD participants met the inclusion criteria. Deep neural networks (DNN) showed the best performance for R² (0.88, 95 %CI 0.52–1.24), while traditional machine learning (ML) models demonstrated relative superiority for nRMSE (0.11, 95 %CI 0.06–0.29). Among input types, surface EMG (sEMG) achieved the highest R² (0.96, 95 %CI 0.04–1.89), whereas all inputs except “kinematic and speed and anthropometrics” performed well in the nRMSE analysis. For output joints, the ankle was significantly superior to both the knee (p < 0.001) and the hip (p < 0.001) in terms of R² and nRMSE.

Conclusion

AI methods can effectively predict lower limb joint moments during TD gait, but differences exist across model group, input type, and output joints. DNN show advantages in fitting complex data, while traditional ML demonstrates greater robustness in small-sample settings. The sEMG, as a process-related input, exhibits high potential, and predictions for the ankle joint are generally superior. Future studies should expand sample size, explore multimodal inputs and advanced modeling strategies, and further validate the applicability of AI methods in pathological gait.

目的：人工智能（AI）方法已广泛应用于步态分析，但模型之间的定量比较及其输入输出规格仍然有限。本研究旨在系统回顾和综合现有文献，以评估人工智能方法在典型发育（TD）步态中预测下肢关节力矩的有效性。方法：使用布尔逻辑运算从PubMed、Scopus、IEEE Xplore、ScienceDirect和Web of Science 5个数据库中检索2025年7月1日前发表的相关研究，并根据预先设定的标准进行筛选。采用PROBAST评估偏倚风险和适用性。在R中使用多层随机效应模型进行meta分析，以检查AI模型组、信号输入类型和输出关节的预测性能差异。结果：11项涉及371名TD参与者的研究符合纳入标准。深度神经网络（DNN）在R2上表现最佳（0.88,95 %CI 0.52-1.24），而传统机器学习（ML）模型在nRMSE上表现出相对优势（0.11,95 %CI 0.06-0.29）。在输入类型中，表面肌电信号（sEMG）的R2最高（0.96,95 %CI 0.04-1.89），而除了“运动学、速度和人体测量学”之外的所有输入在nRMSE分析中都表现良好。对于输出关节，踝关节明显优于膝关节（p 2和nRMSE）。结论：AI方法可以有效预测TD步态时下肢关节力矩，但模型组、输入类型、输出关节之间存在差异。深度神经网络在拟合复杂数据方面表现出优势，而传统机器学习在小样本设置中表现出更强的鲁棒性。表面肌电信号作为一个过程相关的输入，显示出很高的潜力，对踝关节的预测通常是优越的。未来的研究应扩大样本量，探索多模态输入和先进的建模策略，进一步验证人工智能方法在病理步态中的适用性。

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引用次数: 0

Association between bone mineral density and ground reaction force in male and female runners 男女跑步者骨密度与地面反作用力之间的关系。

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-11 DOI: 10.1016/j.gaitpost.2025.110082

Maxwell D. Smith, Rebecca L. Wong, Derek N. Pamukoff

Background

Bone responds to loading by accruing areal bone mineral density (BMD). Distance runners experience a ground reaction force (GRF) during exercise which contributes to bone loading. Sex differences in BMD reflect that males and females respond differently to running-imposed GRF.

Research question

What is the relationship between GRF and BMD in male and female runners?

Methods

Forty participants (20 male; age=25.1 ± 4.5 years; height=1.7 ± 0.1 m; mass=67.2 ± 11.5 kg) who routinely participated in distance running (44.0 ± 26.1 km/week over 4.5 ± 1.5 weekly sessions for the past 6.9 ± 5.2 years) underwent dual x-ray absorptiometry to calculate BMD and ran on a force-instrumented treadmill to measure vertical GRF characteristics at a self-selected (SS) and at a standardized pace (SP; 3.33 m/s). Independent samples t-tests compared outcomes between males and females. Pearson correlation examined associations between GRF and BMD separately based on sex.

Results

Absolute GRF and BMD outcomes were consistently lower in females compared with males (all p < 0.05). At SS, greater BMD in some sites was associated with greater vertical GRF (r = 0.582–0.793, p < 0.001–0.007), vertical loading rate (r = 0.459–0.626, p = 0.003–0.042), and vertical impulse (r = 0.518–0.759, p < 0.001–0.019) in males. Greater BMD in some sites was also associated with greater vertical GRF (r = 0.550–0.736, p < 0.001–0.012), vertical loading rate (r = 0.495–0.718, p < 0.001–0.026), and vertical impulse (r = 0.478–0.755, p < 0.001–0.033) in males at SP. There were no associations between BMD and GRF in females at either pace (r = -0.095–0.360, p = 0.130–0.983).

Significance

The associations between GRF and BMD in runners differ between males and females. Supplemental training methods may be necessary for female runners to influence BMD.

背景：骨通过累积面骨矿物质密度（BMD）来响应负荷。长跑运动员在运动过程中会经历地面反作用力（GRF），这会增加骨骼负荷。骨密度的性别差异反映了男性和女性对跑步施加的GRF的不同反应。研究问题：男性和女性跑步者的GRF和BMD之间的关系是什么？方法:40参与者(20男,年龄= 25.1 ± 4.5年;身高= 1.7 ±0.1  m;质量= 67.2 ±11.5  公斤)他经常参加长跑(44.0 ±26.1  公里/星期超过4.5 ±1.5 每周会议过去6.9 ± 5.2年)接受双x线吸收仪计算弹道导弹防御,跑一个force-instrumented跑步机上测量垂直平在自我选择的特点(SS)和速度标准化(SP; 3.33 m / s)。独立样本t检验比较了男性和女性的结果。Pearson相关性研究了GRF和BMD之间基于性别的相关性。结果：与男性相比，女性的绝对GRF和骨密度结果始终较低（均p ）。意义：跑步者中GRF和骨密度之间的关联在男性和女性之间存在差异。女性跑步者可能需要补充训练方法来影响骨密度。

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引用次数: 0

Predicting the distance between the center of mass and center of pressure during walking in the young and elder population based on ground reaction forces using artificial neural network 基于地面反作用力的人工神经网络预测中老年人群行走过程中质心与压力中心的距离

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-06 DOI: 10.1016/j.gaitpost.2025.110077

Chu-Fen Chang , Anne Dixie M. Lim , Shih-Wun Hong , Tung-Wu Lu , Kelly I.-Rong Lee

Introduction

Assessing the interaction between the body’s center of mass (COM) and center of pressure (COP) during gait provides valuable insights into dynamic balance control. However, traditional motion capture methods are costly and impractical in clinical settings, highlighting the need for accessible alternatives. This study utilizes force plate data to explore the use of ANNs in predicting the motion of the COM relative to the COP during walking.

Materials and Methods

Eighty-four healthy adults (50 young, 34 older) performed gait trials on a walkway with motion capture and force plate data collection. Ground reaction forces (GRFs) and COM-COP distances (dCOM) were computed for the stance phase. Multi-layer perceptron (MLP) artificial neural network (ANN) models were developed to predict dCOM in the anteroposterior (AP) and mediolateral (ML) directions as well as COM height based on GRFs and anthropometric inputs. Model performance was evaluated using root mean squared error (RMSE) and coefficient of determination (R²).

Results

ANN models achieved high predictive accuracy with optimal hidden layer sizes of 5 (AP, COM height) and 9 (ML). RMSE values ranged from 0.89 % to 7.77 %LL, with R² values between 0.74 and 0.92. Significant age-related differences were observed in ML direction and COM height (p < 0.05), but not in AP.

Conclusion

This study highlights ANN models as accurate, low-cost tools for dynamic balance assessment, reliably predicting dCOM across directions. The consistency between calculated and predicted group differences supports their application in fall risk evaluation and personalized intervention planning for older adult populations.

在步态中评估身体的质心（COM）和压力中心（COP）之间的相互作用为动态平衡控制提供了有价值的见解。然而，传统的动作捕捉方法在临床环境中是昂贵和不切实际的，突出了对可获得替代方案的需求。本研究利用测力板数据来探索人工神经网络在预测行走过程中COM相对于COP的运动中的应用。材料和方法84名健康成人（50名年轻人，34名老年人）在人行道上进行步态试验，采用运动捕捉和力板数据收集。计算了姿态阶段的地面反作用力（GRFs）和COM-COP距离（dCOM）。基于GRFs和人体测量输入，建立多层感知器（MLP）人工神经网络（ANN）模型，用于预测正前方（AP）和中外侧（ML）方向的dCOM以及COM高度。采用均方根误差（RMSE）和决定系数（R2）评价模型的性能。结果sann模型的最优隐层大小分别为5 （AP， COM高度）和9 (ML)，具有较高的预测精度。RMSE值为0.89 % ~ 7.77 %LL， R2值为0.74 ~ 0.92。在ML方向和COM高度上观察到显著的年龄相关差异（p <； 0.05），但在ap上没有。结论：本研究强调了ANN模型是准确、低成本的动态平衡评估工具，可靠地预测了跨方向的dCOM。计算和预测的群体差异之间的一致性支持其在老年人跌倒风险评估和个性化干预计划中的应用。

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引用次数: 0

Adaptation to repeated backward surface translations in healthy young adults: A kinematic elucidation of trip-based perturbation training for preventing falls 健康年轻人对重复向后表面平移的适应：预防跌倒的基于旅行的扰动训练的运动学解释。

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-05 DOI: 10.1016/j.gaitpost.2025.110066

Sara Mahmoudzadeh Khalili, Diané Brown, Caroline Simpkins, Feng Yang

Background

Perturbation-based training has emerged as a promising intervention to enhance balance recovery strategies.

Research Question

Can healthy young adults quickly adapt to repeated backward surface translations while standing on a treadmill?

Methods

In this cross-sectional study, 20 young adults completed five treadmill-induced backward surface translations during standing. The primary outcome was dynamic gait stability at perturbation onset (ON), recovery step liftoff (LO), and touchdown (TD). Secondary outcomes included center of mass (COM) position and velocity, recovery step performance (latency, duration, length, and speed), belt displacement at LO, trunk movements (angle and angular velocity), ankle angle at ON, and integrated electromyography (EMG) of bilateral leg muscles (tibialis anterior, TA, gastrocnemius, GA, rectus femoris, RF, and biceps femoris, BF).

Results

Participants were more stable at ON (p = 0.004), LO (p ≤ 0.001), and TD (p = 0.047) on later trials than on earlier trials. Recovery step latency (p ≤ 0.001), duration (p ≤ 0.001), speed (p = 0.006), and length (p = 0.015) improved across trials. Other secondary outcomes also showed significant improvements across trials: less forward-leaning and rotating trunk at LO and TD (p ≤ 0.001), increased integrated EMG for TA, GA, and RF on the stepping leg (p ≤ 0.017) and all muscles on the standing leg (p ≤ 0.033) before ON, and reduced integrated EMG for TA, RF, and BF on both legs (p ≤ 0.047) between ON and TD.

Significance

Repeated treadmill-induced backward surface translations during standing enhanced lower-limb muscle activation, recovery step performance, and trunk movement control, thereby improving dynamic stability proactively and reactively. The findings may elucidate mechanisms for balance recovery after a trip-like postural perturbation.

背景：基于扰动的训练已经成为一种有希望的干预措施，以增强平衡恢复策略。研究问题：健康的年轻人能否迅速适应站在跑步机上重复的向后表面平移？方法：在这项横断面研究中，20名年轻人在站立时完成了5次跑步机诱导的向后表面平移。主要结果是摄动开始时的动态步态稳定性（ON），恢复步升（LO）和着地（TD）。次要结果包括质心（COM）的位置和速度、恢复步的表现（潜伏期、持续时间、长度和速度）、LO处的皮带位移、躯干运动（角度和角速度）、ON处的踝关节角度，以及双侧腿部肌肉（胫前肌、TA、腓肠肌、GA、股直肌、RF和股二头肌）的综合肌电图（EMG）。结果：在后期试验中，受试者在ON （p = 0.004）、LO （p ≤ 0.001）和TD （p = 0.047）上比在早期试验中更稳定。恢复步骤延迟（p ≤ 0.001）、持续时间（p ≤ 0.001）、速度（p = 0.006）和长度（p = 0.015）在各试验中均有所改善。其他次要结果在试验中也显示出显著的改善：下肢和下肢的躯干前倾和旋转减少（p ≤ 0.001），在on前，踏步腿上TA、GA和RF的综合肌电信号增加（p ≤ 0.017），站立腿上所有肌肉的综合肌电信号增加（p ≤ 0.033），在on和TD之间，双腿TA、RF和BF的综合肌电信号减少（p ≤ 0.047）。意义：站立时反复跑步机诱导的后表面平移增强了下肢肌肉的激活、恢复步的表现和躯干运动的控制，从而主动和被动地提高了动态稳定性。研究结果可能阐明了在类似绊倒的姿势扰动后平衡恢复的机制。

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引用次数: 0

Development and validity of composite scores for the instrumented-modified Clinical Test of Sensory Interaction in Balance in inpatients with subacute stroke 亚急性脑卒中住院患者平衡感觉相互作用仪器改良临床测试综合评分的研制与有效性。

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-04 DOI: 10.1016/j.gaitpost.2025.110067

Tatsuya Igarashi , Shota Hayashi , Shingo Hirano , Kazusa Saisu , Hironobu Kakima , Yuta Tani , Hiroyuki Inooka

Background

The instrumented modified Clinical Test of Sensory Interaction on Balance (i-mCTSIB) assesses sensory contributions to postural control using Center of Pressure (CoP) data. Existing composite scores do not reflect task difficulty or sensory weighting across conditions.

Objectives

This study developed a regression-based new composite score and examined its ability to discriminate walking independence and its correlation with standard balance measures in subacute stroke.

Methods

This study included 54 patients with first-ever stroke. Postural control was evaluated under four i-mCTSIB conditions combining visual input and surface type. Total CoP path length was recorded, and a composite score was derived using regression analysis. Discriminative ability for walking independence was assessed via receiver operating characteristic analysis, and area under the curve (AUC) values were compared. Measures with AUC > 0.80 were further tested for construct validity using the Mini-Balance Evaluation Systems Test (Mini-BESTest).

Results

The i-mCTSIB composite score demonstrated an AUC of 0.84 (95 % CI: 0.69–0.98), exceeding the threshold of 0.80 and outperforming all individual test conditions, which showed AUCs below 0.80. The composite score also showed a moderate correlation with the Mini-BESTest (r̄ = 0.48, 95 % CI: 0.24–0.67, p < 0.001).

Conclusions

This study developed a novel composite score for the i-mCTSIB that quantitatively reflects sensory integration in postural control and demonstrated its superior ability to discriminate walking independence in patients with subacute stroke. Further investigations are required to evaluate whether these findings generalize to additional CoP parameters.

背景：仪器改良的感觉相互作用平衡临床测试（i-mCTSIB）通过压力中心（CoP）数据评估感觉对姿势控制的贡献。现有的综合分数不能反映任务难度或感觉权重。目的：本研究开发了一种基于回归的新综合评分，并检验了其区分亚急性卒中患者行走独立性的能力及其与标准平衡测量的相关性。方法：本研究纳入54例首次脑卒中患者。在结合视觉输入和表面类型的四种i-mCTSIB条件下评估姿势控制。记录总CoP路径长度，采用回归分析得出综合评分。通过受试者工作特征分析评估行走独立的判别能力，并比较曲线下面积（AUC）值。AUC为> 0.80的测量方法使用Mini-Balance评估系统测试（mini - best）进一步检验结构效度。结果：i-mCTSIB综合评分的AUC为0.84(95 % CI: 0.69-0.98)，超过了0.80的阈值，优于所有单项测试条件的AUC均低于0.80。该综合评分与Mini-BESTest也显示出中度相关性（r = 0.48, 95 % CI: 0.24-0.67, p ）。结论：本研究为i-mCTSIB开发了一种新的综合评分，定量反映了姿势控制中的感觉统合，并证明了其在区分亚急性卒中患者行走独立性方面的卓越能力。需要进一步的调查来评估这些发现是否可以推广到其他CoP参数。

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引用次数: 0

Medio-lateral forefoot segmentation for clinical gait analysis based on metatarsal subunit rigidity and angular motion 基于跖骨亚单位刚度和角运动的中外侧前足分割用于临床步态分析。

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-04 DOI: 10.1016/j.gaitpost.2025.110070

Amy B. Zavatsky , Po-Hsiang Chan , Julie Stebbins

Background

Although there is general agreement about the longitudinal division of the foot into segments for clinical gait analysis, there is limited evidence on which to base decisions about mediolateral segmentation, particularly in the metatarsal region.

Research question

We investigate how best to divide the metatarsals mediolaterally by considering both segment rigidity and angular motion.

Methods

Motion capture data were collected on 45 healthy adults during barefoot walking. The rigidities of ten subunits of adjacent metatarsals were quantified. Segment axes were defined for a selection of subunits and their three-dimensional angular motions calculated relative to an Oxford Foot Model (OFM) hindfoot segment.

Results

Subunits of metatarsals 2–3 and 3–4 were equally the most rigid, followed by subunit 2–3–4. Medial metatarsal groups were more rigid than lateral groups. Model A (metatarsal subunits 1–2–3 & 4–5), Model B (1–2 & 3–4–5), and Model C (1& 2–3–4 & 5) all had angular motion significantly different from the OFM forefoot for most of the gait cycle. There were significant differences between the motions of the medial and lateral subunits of Models A and B. The central subunit of Model C moved more like the medial subunits in dorsiflexion and more like the lateral subunits in adduction.

Significance

The forefoot models examined represent the minimum complexity required to capture metatarsal motion during walking. A mediolateral division of the forefoot at or adjacent to the third metatarsal is one option. The alternative is a three-segment model with a central subunit and separate first and fifth metatarsals.

背景：尽管在临床步态分析中对足的纵向划分有普遍的共识，但关于中外侧分割的基础决策的证据有限，特别是在跖骨区域。研究问题：我们通过考虑节段刚度和角运动来研究如何最好地向中外侧划分跖骨。方法：收集45名健康成人赤脚行走时的运动捕捉数据。对相邻跖骨的十个亚单位的刚度进行了量化。为选定的子单元定义了段轴，并计算了相对于牛津足模型（OFM）后足段的三维角运动。结果：跖骨2-3和3-4亚基最僵硬，其次是2-3-4亚基。内侧跖骨组较外侧跖骨组僵硬。模型A（跖骨亚单位1-2-3和4-5）、模型B（1-2和3-4-5）和模型C（1和2-3-4和5）在大多数步态周期中都与OFM前足有明显不同的角运动。模型A和模型b的内侧亚基和外侧亚基的运动有显著差异。模型C的中心亚基在背屈时更像内侧亚基，内收时更像外侧亚基。意义：研究的前足模型代表了在行走过程中捕获跖骨运动所需的最小复杂性。在第三跖骨或其附近进行前足的中外侧分离是一种选择。另一种选择是三节段模型，具有中心亚基和分离的第一和第五跖骨。

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引用次数: 0

Recognizing intentions with body segmental cues of gait cycles before direction change during continuous walking 在连续行走过程中，在方向改变之前，用步态周期的身体片段线索识别意图

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-12-04 DOI: 10.1016/j.gaitpost.2025.110069

Yu-Lin Yen, Yun-Ju Lee

Background

Gait intention is typically detected using electroencephalogram (EEG) and primarily focuses on recognizing the initiation of walking. Recently, wearable sensors have been extensively used to classify different walking patterns.

Research questions

The study was aimed to investigate the effectiveness of Inertial Measurement Units (IMUs) in recognizing directional change intentions during gait, hypothesizing that distinct patterns exist that indicate the initiation of turning before actual turning movements occur.

Methods

Twenty healthy participants performed one to two gait cycles before directional changes during straight walking, a 45-degree right turn, and a 90-degree right turn. Seven IMUs were attached to various body segments, and a matching network combined with sliding window bidirectional gated recurrent units was used to recognize turning intentions. The EEG data analysis was used by EEGLAB to determine the event-related desynchronization in the alpha (8–13 Hz) and beta (14–30 Hz) frequency bands.

Results

The results showed that the model using different combinations of IMU sensors achieved an optimal accuracy of 96.80 %, with several combinations exceeding 95 %. Subtle movements in different body segments were sufficient to predict upcoming turning intentions. The accuracy dropped to 83.79 % in the model that excluded data from the head segment, while the other models that included the head segment achieved over 94 % accuracy. Furthermore, angular acceleration data was notably more accurate, at 93.46 %, compared to 88.43 % for acceleration data alone.

Significance

Successfully highlighted the crucial role of sensing contralateral body and head segments, providing insights into the model's interpretability. The use of IMUs for gait turning intention recognition shows promise in replacing devices that rely on sensing brain waves. Furthermore, this approach opens possibilities for applications such as controlling exoskeletons, including assisting walking robots with strategically placed sensors.

步态意图通常是通过脑电图（EEG）来检测的，主要集中在识别步行的开始。近年来，可穿戴传感器被广泛用于对不同的行走模式进行分类。研究问题本研究的目的是研究惯性测量单元（imu）在识别步态中方向变化意图方面的有效性，假设在实际转向运动发生之前存在不同的模式，表明转向开始。方法20名健康受试者在直线行走、向右转45度和向右转90度方向改变前进行1 - 2个步态周期。将7个imu连接到不同的车身节段上，采用滑动窗双向门控循环单元相结合的匹配网络进行转弯意图识别。EEG数据分析采用EEGLAB来确定α（8-13 Hz）和β（14-30 Hz）频段的事件相关非同步。结果不同IMU传感器组合的模型精度达到96.80 %，个别组合精度超过95 %。不同身体部位的细微动作足以预测即将到来的转弯意图。在排除头部段数据的模型中，准确率下降到83.79 %，而包括头部段的其他模型的准确率超过94 %。此外，角加速度数据的准确性明显更高，为93.46 %，而单独的加速度数据为88.43 %。成功地强调了感知对侧身体和头部部分的关键作用，为模型的可解释性提供了见解。使用imu进行步态转向意图识别，有望取代依赖感知脑电波的设备。此外，这种方法为控制外骨骼等应用提供了可能性，包括通过策略性放置的传感器辅助行走机器人。

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引用次数: 0

Does transferred medial gastrocnemius flap help with knee extension during gait following proximal tibial endoprosthesis? 转移腓肠肌内侧皮瓣是否有助于胫骨近端假体术后步态中的膝关节伸展？

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-11-29 DOI: 10.1016/j.gaitpost.2025.110063

Dyuti Deepta Rano , Love Kapoor , Venkatesan Sampath Kumar , Shamim Ahmed Shamim , Shah Alam Khan

Introduction

Patients undergoing proximal tibial endoprosthesis for bone tumors usually have a medial gastrocnemius flap for wound coverage, supposed to help in knee extension instead of normal flexor of the knee.

Objectives

1. Does medial gastrocnemius flap participates in knee extension during normal gait? 2. What are the gait changes following proximal tibial endoprosthesis?

Materials & methods

This cross-sectional observational study included 52 patients who had proximal tibial endoprosthesis between January 2008 to January 2020 for bone tumours and regained maximum function with independent ambulatory capacity. Instrumented gait analysis was done according to Helen-Hayes protocol with a special surface EMG probe placed over the flap. Tc 99 m three-phase bone scan was used to evaluate flap viability.

Results

Mean age was 27.92 ± 12.88 years and mean follow-up duration was 23.1 ± 4.2 months. Patients walked slower with mean velocity of 0.74 ± 0.23 m/s and mean cadence of 87.9 ± 10.3 steps/min. Mean knee flexion on the operated side was significantly decreased(89.42 ± 14.87° vs 125.38 ± 6.01°, p < 0.001). Mean swing time was significantly increased on the operated limb(0.56 ± 0.08 sec vs 0.46 ± 0.07 sec, p < 0.001) with consequent increase in mean single support phase on the normal limb(operated vs normal limb, 33.71 ± 5.05 % vs 40.81 ± 4.03 %, p < 0.001). Peak knee flexion in swing, total sagittal plane excursion, peak flexion loading response, peak knee extensor moment at early stance and peak ankle plantarflexion moment at stance decreased significantly on operated side. Electrical activity in the knee extensors decreased over the operated side. Bone scan confirmed flap viability in all.

Conclusion

The medial gastrocnemius flap primarily provides successful soft tissue coverage but does not consistently adopt a functional role in knee extension during gait. Patients' walking patterns remain slower and asymmetrical but well compensated post-reconstruction, adopting a stiff-knee gait pattern with features of quadriceps avoidance.

导读：接受胫骨近端骨肿瘤内假体手术的患者通常有一个内侧腓肠肌瓣来覆盖伤口，应该有助于膝关节伸展而不是正常的膝关节屈肌。目的:1。内侧腓肠肌瓣是否参与正常步态下的膝关节伸展？2. 胫骨近端人工植入术后步态有什么变化？材料与方法：本横断面观察性研究包括52例2008年1月至2020年1月间因骨肿瘤行胫骨近端内假体手术并恢复最大功能并具有独立行走能力的患者。根据Helen-Hayes方案，在皮瓣上放置一个特殊的表面肌电图探针，进行仪器步态分析。Tc 99 m三相骨扫描评估皮瓣存活能力。结果：平均年龄27.92 ± 12.88岁，平均随访时间23.1 ± 4.2个月。患者步行速度减慢，平均速度为0.74 ± 0.23 m/s，平均步频为87.9 ± 10.3步/min。手术侧平均膝关节屈曲度显著降低（89.42 ± 14.87°vs 125.38 ± 6.01°，p ）。结论：内侧腓骨肌瓣主要提供成功的软组织覆盖，但在步态中不能始终发挥膝关节伸展的功能作用。患者的行走模式仍然较慢且不对称，但重建后补偿良好，采用以股四头肌回避为特征的硬膝步态模式。

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引用次数: 0

Fatigue-induced changes in motor unit behavior and intermuscular coherence across knee joint positions 疲劳引起的运动单位行为和跨膝关节位置的肌间一致性的变化

IF 2.4 3区医学 Q3 NEUROSCIENCES

Gait & posture

Pub Date : 2025-11-29 DOI: 10.1016/j.gaitpost.2025.110068

Hyeon-Deok Jo , Maeng-Kyu Kim

Background

Neuromuscular fatigue impairs force steadiness and alters motor unit (MU) behavior. However, it remains unclear how joint angle modulates the neuromuscular response to fatigue, especially in terms of MU characteristics and intermuscular coherence. This study examined the effects of fatigue and knee joint angle on MU behavior, force steadiness, and intermuscular coherence between the soleus (SOL) and gastrocnemius medialis (GM) during isometric plantarflexion.

Methods

Sixteen healthy males performed isometric plantarflexion at 30, 50, and 70 % of maximal voluntary isometric contraction (MVIC) in pre-and post-fatigue, under two knee joint conditions: straight leg (0°, STR) and bent leg (90°, BENT) positions, in a randomized order. Fatigue was induced via repeated sustained contractions at 60 % MVIC in each position. High-density surface electromyography from SOL and GM was decomposed to quantify MU parameters, including MU action potential amplitude (MUAP), firing rate (FR), recruitment threshold (ReTHR), and decruitment threshold (DeTHR). Intermuscular coherence between MU spike trains from SOL and GM was analyzed across frequency bands.

Results

Post-fatigue, MIVC decreased significantly in both the STR and BENT conditions (p < 0.01). Force steadiness significantly worsened post-fatigue in the BENT condition at 50 % (p < 0.01) and 70 % (p < 0.05). In SOL, MUAP and FR increased post-fatigue across all conditions, while GM showed more selective increases at higher intensities. SOL exhibited higher MUAP in the BENT condition, whereas GM showed greater activity in the STR condition. ReTHR in SOL and DeTHR in both muscles showed significant changes related to posture and fatigue. Notably, beta-band intermuscular coherence showed significant interaction effects at 50 % and 70 % (p < 0.05), with increased coherence in the BENT condition post-fatigue (p < 0.05).

Conclusions

Fatigue induces muscle-specific and posture-dependent neuromuscular adaptations, characterized by changes in MU properties and intermuscular coherence, particularly in the bent knee position.

背景：神经肌肉疲劳损害力量稳定性并改变运动单元（MU）行为。然而，目前尚不清楚关节角度如何调节神经肌肉对疲劳的反应，特别是在MU特征和肌间一致性方面。本研究考察了疲劳和膝关节角度对跖屈等距屈伸时的MU行为、力稳定性和比目鱼肌（SOL）和腓肠肌内侧肌（GM）之间的肌间一致性的影响。方法16名健康男性在疲劳前和疲劳后，在直腿（0°，STR）和屈腿（90°，bent）两种膝关节状态下，按随机顺序，分别以30%、50%和70% %的最大自愿等距收缩（MVIC）进行等距跖屈。通过在每个体位以60 % MVIC重复持续收缩来诱导疲劳。对SOL和GM的高密度表面肌电图进行分解，量化MU参数，包括MU动作电位振幅（MUAP）、放电速率（FR）、复吸阈值（ReTHR）和复吸阈值（DeTHR）。分析了来自SOL和GM的MU尖峰序列在不同频段间的肌间相干性。结果疲劳后，在STR和弯曲条件下，MIVC均显著降低（p <； 0.01）。弯曲状态下疲劳后的力稳定性显著恶化，分别为50% % （p <； 0.01）和70% % （p <； 0.05）。在SOL中，MUAP和FR在所有条件下都增加了疲劳后的强度，而GM在高强度下表现出更多的选择性增加。SOL在BENT条件下表现出较高的MUAP，而GM在STR条件下表现出较高的活性。SOL中的ReTHR和两组肌肉中的DeTHR表现出与姿势和疲劳相关的显著变化。值得注意的是，β -带肌间相干性在50 %和70 % （p <； 0.05）时显示出显著的相互作用效应，疲劳后弯曲状态下的相干性增加（p <； 0.05）。结论疲劳引起肌肉特异性和姿势依赖性的神经肌肉适应，其特征是MU特性和肌间一致性的改变，特别是在弯曲的膝关节位置。

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引用次数: 0