Pub Date : 2024-07-01Epub Date: 2024-05-08DOI: 10.1016/j.gaitpost.2024.05.006
Dalia Al Otti, Stijn Ghijselings, Filip Staes, Lennart Scheys
Background: Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling.
Methods: 8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches.
Results: Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (-2.8±2.7), hip extension (-2.2±3.8), hip abduction (-1.2±3.6) and external rotation (-13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip.
Significance: S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.
{"title":"How reliable are femoropelvic kinematics during deep squats? The influence of subject-specific skeletal modelling on measurement variability.","authors":"Dalia Al Otti, Stijn Ghijselings, Filip Staes, Lennart Scheys","doi":"10.1016/j.gaitpost.2024.05.006","DOIUrl":"10.1016/j.gaitpost.2024.05.006","url":null,"abstract":"<p><strong>Background: </strong>Biplanar radiography displays promising results in the production of subject-specific (S.specific) biomechanical models. However, the focus has predominantly centred on methodological investigations in gait analysis. Exploring the influence of such models on the analysis of high range of motion tasks linked to hip pathologies is warranted. The aim of this study is to investigate the effect of S.Specific modelling techniques on the reliability of deep squats kinematics in comparison to generic modelling.</p><p><strong>Methods: </strong>8 able-bodied male participants attended 5 motion capture sessions conducted by 3 observers and performed 5 deep squats in each. Prior to each session a biplanar scan was acquired with the reflective-markers attached. Inverse kinematics of pelvis and thigh segments were calculated based on S.specific and Generic model definition. Agreement between the two models femoropelvic orientation in standing was assessed with Bland-Altman plots and paired t- tests. Inter-trial, inter-session, inter-observer variability and observer/trial difference and ratio were calculated for squat kinematic data derived from the two modelling approaches.</p><p><strong>Results: </strong>Compared to the Generic model, the S.Specific model produced a calibration trial that is significantly offset into more posterior pelvis tilt (-2.8±2.7), hip extension (-2.2±3.8), hip abduction (-1.2±3.6) and external rotation (-13.8±11.4). The S.specific model produced significantly different squat kinematics in the sagittal plane of the pelvis (entire squat cycle) and hip (between 40 % and 60 % of the squat cycle). Variability analysis indicated that the error magnitude between the two models was comparable (difference<2°). The S.specific model exhibited a lower variability in the observer/trial ratio in the sagittal pelvis and hip, the frontal hip, but showed a higher variability in the transverse hip.</p><p><strong>Significance: </strong>S.specific modelling appears to introduce a calibration offset that primarily translates into an effect in the sagittal plane kinematics. However, the clinical added value of S.specific modelling in terms of reducing experimental sources of kinematic variability was limited.</p>","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"112 ","pages":"120-127"},"PeriodicalIF":0.0,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961272","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.244
Salvatore Tedesco, Colum Crowe, Marco Sica, Lorna Kenny, Brendan O'Flynn, David Scott Mueller, Suzanne Timmons, John Barton
Parkinson disease (PD), a well-known illness of motor dysfunction, is characterized by a high prevalence of sleep problems due to degenerative brain changes or comorbid conditions [1]. Wearable devices, in the form of actigraphy, have been shown to also be appropriate for monitoring sleep variables in PD patients [2,3] despite reports that current actigraphy algorithms may misinterpret dysfunctional motor activity, such as tremors, bradykinesia, dyskinesia, and limited arm movement while walking, as well as drug-induced hypermotility, thus making their use problematic in people with PD (PwPD) [4]. The ActiGraph GT3X (Pensacola, FL, USA) accelerometer is capable of recording accelerometry measurements for multiple days at 100 Hz, and has been adopted for massive population-level data collections [5]. In the last few years, Van Hees et al. have developed and made freely available open-source software to estimate sleep variables using data collected from similar off-the-shelf wearable inertial sensors [6]. The goal of this study is to investigate if the ActiGraph data, in combination with Van Hees et al.’s heuristic algorithm Distribution of Change in Z-Angle (HDCZA), can correctly estimate sleep variables in PD patients. To the best of the authors’ knowledge, it is the first study that adopts ActiGraph sensors and this methodology for sleep analysis in PwPD. For further comparison, a custom hardware prototype device named WESAA (Wearable Enabled Symptom Assessment Algorithms) developed at the Tyndall National Institute [8] and with the same capabilities as an ActiGraph device was adopted for additional analysis. Nineteen PD subjects took part in a data collection where participants wore the ActiGraph on their most affected wrist for a minimum of 24 hours and simultaneously filled out a sleep diary. Accelerometer data was collected at 100 Hz. Additionally, six subjects repeated the same data collection protocol while wearing the WESAA system. The heuristic algorithm described in [7] was implemented to detect periods of sleep and compared against the participant diaries. Results are shown in Table I and Figure I in the picture below. Accuracy reported on the subjects using the Actigraph was appropriate with an average 77.8±13.6%, even though results were quite variable across patients (between 31.6% and 91.2%). Less variability is shown with the WESAA device, even though only 6 subjects have carried out this data collection, with an average accuracy of 81.9±6.2% (71.8%-90.2%).Download : Download high-res image (157KB)Download : Download full-size image The present investigation shows that ActiGraph accelerometry data collected over 24 hours, in conjunction with the heuristic algorithm HDCZA for the detection of sleep periods, is an appropriate approach to estimate sleep duration even in PwPD. The same algorithm adopted on the WESAA hardware device shows even more promising results but further investigations with a larger sample size are required to c
帕金森病(PD)是一种众所周知的运动功能障碍疾病，其特点是由于大脑退行性改变或合并症导致睡眠问题的高发[1]。活动记录仪形式的可穿戴设备也被证明适合监测PD患者的睡眠变量[2,3]，尽管有报道称，目前的活动记录仪算法可能会误解功能失调的运动活动，如震颤、运动迟缓、运动障碍、行走时手臂运动受限以及药物引起的运动亢进，从而使其在PD患者(PwPD)中的使用存在问题[4]。ActiGraph GT3X (Pensacola, FL, USA)加速度计能够在100 Hz下记录多天的加速度测量结果，并已被用于大规模的人口数据收集[5]。在过去的几年中，Van Hees等人开发并免费提供了开源软件，利用从类似的现成可穿戴惯性传感器收集的数据来估计睡眠变量[6]。本研究的目的是探讨ActiGraph数据结合Van Hees等人的启发式算法Distribution of Change in Z-Angle (HDCZA)是否能正确估计PD患者的睡眠变量。据作者所知，这是第一个采用ActiGraph传感器和这种方法对PwPD进行睡眠分析的研究。为了进一步比较，我们采用Tyndall National Institute[8]开发的自定义硬件原型设备WESAA (Wearable Enabled Symptom Assessment Algorithms，可穿戴症状评估算法)进行附加分析，该设备与ActiGraph设备具有相同的功能。19名PD受试者参加了一项数据收集，参与者在他们受影响最严重的手腕上佩戴ActiGraph至少24小时，同时填写睡眠日记。加速度计数据以100 Hz的频率收集。此外，六名受试者在佩戴WESAA系统时重复相同的数据收集方案。采用[7]中描述的启发式算法检测睡眠时间，并与参与者日记进行比较。结果如下图表1和图1所示。使用Actigraph的受试者报告的准确率是合适的，平均为77.8±13.6%，尽管不同患者的结果差异很大(31.6%至91.2%)。WESAA装置的变异性较小，尽管只有6名受试者进行了这项数据收集，平均准确率为81.9±6.2%(71.8%-90.2%)。目前的研究表明，在24小时内收集的ActiGraph加速度测量数据，结合启发式算法HDCZA来检测睡眠时间，即使在PwPD中也是一种估计睡眠时间的合适方法。WESAA硬件设备上采用的相同算法显示出更有希望的结果，但需要更大样本量的进一步调查来证实这一点。资金:这项工作由爱尔兰企业(EI)和艾伯维公司根据协议IP 2017 0625部分支持;部分由爱尔兰科学基金会资助，由欧洲区域发展基金在12/RC/2289-P2-INSIGHT下共同资助。
{"title":"Sleep analysis via wearable sensors in people with Parkinson’s disease","authors":"Salvatore Tedesco, Colum Crowe, Marco Sica, Lorna Kenny, Brendan O'Flynn, David Scott Mueller, Suzanne Timmons, John Barton","doi":"10.1016/j.gaitpost.2023.07.244","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.244","url":null,"abstract":"Parkinson disease (PD), a well-known illness of motor dysfunction, is characterized by a high prevalence of sleep problems due to degenerative brain changes or comorbid conditions [1]. Wearable devices, in the form of actigraphy, have been shown to also be appropriate for monitoring sleep variables in PD patients [2,3] despite reports that current actigraphy algorithms may misinterpret dysfunctional motor activity, such as tremors, bradykinesia, dyskinesia, and limited arm movement while walking, as well as drug-induced hypermotility, thus making their use problematic in people with PD (PwPD) [4]. The ActiGraph GT3X (Pensacola, FL, USA) accelerometer is capable of recording accelerometry measurements for multiple days at 100 Hz, and has been adopted for massive population-level data collections [5]. In the last few years, Van Hees et al. have developed and made freely available open-source software to estimate sleep variables using data collected from similar off-the-shelf wearable inertial sensors [6]. The goal of this study is to investigate if the ActiGraph data, in combination with Van Hees et al.’s heuristic algorithm Distribution of Change in Z-Angle (HDCZA), can correctly estimate sleep variables in PD patients. To the best of the authors’ knowledge, it is the first study that adopts ActiGraph sensors and this methodology for sleep analysis in PwPD. For further comparison, a custom hardware prototype device named WESAA (Wearable Enabled Symptom Assessment Algorithms) developed at the Tyndall National Institute [8] and with the same capabilities as an ActiGraph device was adopted for additional analysis. Nineteen PD subjects took part in a data collection where participants wore the ActiGraph on their most affected wrist for a minimum of 24 hours and simultaneously filled out a sleep diary. Accelerometer data was collected at 100 Hz. Additionally, six subjects repeated the same data collection protocol while wearing the WESAA system. The heuristic algorithm described in [7] was implemented to detect periods of sleep and compared against the participant diaries. Results are shown in Table I and Figure I in the picture below. Accuracy reported on the subjects using the Actigraph was appropriate with an average 77.8±13.6%, even though results were quite variable across patients (between 31.6% and 91.2%). Less variability is shown with the WESAA device, even though only 6 subjects have carried out this data collection, with an average accuracy of 81.9±6.2% (71.8%-90.2%).Download : Download high-res image (157KB)Download : Download full-size image The present investigation shows that ActiGraph accelerometry data collected over 24 hours, in conjunction with the heuristic algorithm HDCZA for the detection of sleep periods, is an appropriate approach to estimate sleep duration even in PwPD. The same algorithm adopted on the WESAA hardware device shows even more promising results but further investigations with a larger sample size are required to c","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.233
Lizeth Sloot, Elza van Duijnhoven, Merel A. Brehm, Tamaya Van Criekinge, Matthew Millard
The occurrence of falls and balance problems are common in persons of higher age or with neuromuscular disorders. While clinical balance scales are unable to accurately identify balance, biomechanical balance models (such as the extrapolated center-of-mass) need missing information on the base-of-support formed by the feet [1]. People can balance their body mass above this area formed by the feet without taking a compensatory step. Common impairments such as muscle degeneration likely decrease this support area. Therefore, we evaluated changes in the functional base-of-support (fBOS) resulting from ageing and neuromuscular disorders and the impact on gait balance analysis. We assessed the fBOS in 20 young persons (28±7 yrs), 7 with lower leg muscle weakness due to slowly progressive neuromuscular disorders (63±5 yrs; caption Fig. 1), 7 age-matched middle-aged (62±8 yrs) and 7 old persons (80±3 yrs). Ground forces and foot markers were recorded while participants slowly moved their center-of-pressure in as large circles as possible without moving their feet. The fBOS is modeled was the convex hull enclosing this circled area normalized to marker-based foot dimensions [2]. The effect of ageing of the fBOS on dynamic balance outcomes during walking at heel strike (anterior-posterior direction) was assessed in a dataset of 138 persons across the lifespan [3,4]. The fBOS was only 24% of the foot outline formed by markers for young persons (Fig. 1A) and is 84% smaller in patients with neuromuscular disorders (pttest<0.001). The fBOS decreased with age (pANOVA=0.003), with similar values in mid-age (-24%, pttest=0.11) and a 52% decrease in old age (pttest=0.002) compared to young (Fig. 1A). When taken the fBOS into account, dynamic balance shifts from inside to outside the support area. Extrapolating the age-reduction in fBOS, balance changes from increasing to decreasing with age. Fig. 1: Functional Base of Support (fBOS) for the different participant groups.Download : Download high-res image (333KB)Download : Download full-size image Studies overlook the base-of-support as part of dynamic balance analysis [1]. This study shows the importance of using an accurate model of the fBOS, as a single reference marker does not capture 1) the shape of the effective fBOS; 2) the effects of age and disorder; and 3) changes over the gait cycle. Use of the fBOS revealed reductions in balance in older persons, compared to safer margins without the fBOS. The large group variances indicate that individual fBOS measurements are needed for precise balance assessment. We provide the fBOS model per group and code to apply this to measured markers, so researchers can establish clinical meaningful differences in dynamic balance outcomes. As such, this study strives towards the integration of accurate biomechanical balance analysis in clinical gait analysis.
{"title":"The importance of the functional base-of-support for clinical biomechanical balance analysis","authors":"Lizeth Sloot, Elza van Duijnhoven, Merel A. Brehm, Tamaya Van Criekinge, Matthew Millard","doi":"10.1016/j.gaitpost.2023.07.233","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.233","url":null,"abstract":"The occurrence of falls and balance problems are common in persons of higher age or with neuromuscular disorders. While clinical balance scales are unable to accurately identify balance, biomechanical balance models (such as the extrapolated center-of-mass) need missing information on the base-of-support formed by the feet [1]. People can balance their body mass above this area formed by the feet without taking a compensatory step. Common impairments such as muscle degeneration likely decrease this support area. Therefore, we evaluated changes in the functional base-of-support (fBOS) resulting from ageing and neuromuscular disorders and the impact on gait balance analysis. We assessed the fBOS in 20 young persons (28±7 yrs), 7 with lower leg muscle weakness due to slowly progressive neuromuscular disorders (63±5 yrs; caption Fig. 1), 7 age-matched middle-aged (62±8 yrs) and 7 old persons (80±3 yrs). Ground forces and foot markers were recorded while participants slowly moved their center-of-pressure in as large circles as possible without moving their feet. The fBOS is modeled was the convex hull enclosing this circled area normalized to marker-based foot dimensions [2]. The effect of ageing of the fBOS on dynamic balance outcomes during walking at heel strike (anterior-posterior direction) was assessed in a dataset of 138 persons across the lifespan [3,4]. The fBOS was only 24% of the foot outline formed by markers for young persons (Fig. 1A) and is 84% smaller in patients with neuromuscular disorders (pttest<0.001). The fBOS decreased with age (pANOVA=0.003), with similar values in mid-age (-24%, pttest=0.11) and a 52% decrease in old age (pttest=0.002) compared to young (Fig. 1A). When taken the fBOS into account, dynamic balance shifts from inside to outside the support area. Extrapolating the age-reduction in fBOS, balance changes from increasing to decreasing with age. Fig. 1: Functional Base of Support (fBOS) for the different participant groups.Download : Download high-res image (333KB)Download : Download full-size image Studies overlook the base-of-support as part of dynamic balance analysis [1]. This study shows the importance of using an accurate model of the fBOS, as a single reference marker does not capture 1) the shape of the effective fBOS; 2) the effects of age and disorder; and 3) changes over the gait cycle. Use of the fBOS revealed reductions in balance in older persons, compared to safer margins without the fBOS. The large group variances indicate that individual fBOS measurements are needed for precise balance assessment. We provide the fBOS model per group and code to apply this to measured markers, so researchers can establish clinical meaningful differences in dynamic balance outcomes. As such, this study strives towards the integration of accurate biomechanical balance analysis in clinical gait analysis.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.222
Bradley Scott, Edward Chadwick, Mhairi McInnes, Dimitra Blana
In a recent scoping review (Scott et al., 2022) we discussed how single camera markerless motion capture (SCMoCap) may help to facilitate motion analysis in situations where it would otherwise not be possible, such as at-home rehabilitation for children with cerebral palsy (Kidziński et al., 2020), and more frequent data collection. However, few studies reported error of measurement in a clinically interpretable manner and there is little evidence assessing SCMoCap during upper limb activities of daily living. Presenting a comprehensive validation of SCMoCap, alongside clinically meaningful evaluation of results would be invaluable for clinicians and future researchers who are interested in implementing upper limb movement analysis into clinical practice (Philp et al., 2021). Are state-of-the-art single camera markerless motion capture methods suitable for measuring joint angles during a typical upper-limb functional assessment? Study participants were instructed to perform a compressive collection of physiological and functional movements that are typically part of an upper limb functional assessment. Movements were repeated 3 times for both the frontal and sagittal planes. Movements were recorded simultaneously with a 10-camera OptiTrack Prime 13 W marker-based motion capture setup (NaturalPoint, USA) and Azure Kinect camera (Microsoft, USA). An eSync2 synchronization device (NaturalPoint, USA) was used to avoid exposure interference between systems. Marker-based bony landmarks and joint centers were collected as recommended by the International Society of Biomechanics (Wu et al., 2005). Marker-based trajectories were processed using MotionMonitor xGen (Innovative Sports Training, USA), where a 20 Hz lowpass Butterworth filter was applied to marker positions. Markerless joint center positions were calculated using Azure Kinect body tracking. Markerless positions were filtered using a 10 Hz lowpass Butterworth filter, then upsampled to 120 Hz matching the OptiTrack recording frequency. Signals were time synchronized using cross correlation. Joint angles were calculated by solving inverse kinematics in OpenSim using Hamner’s model (Hamner, Seth & Delp, 2010). Here we present preliminary results of elbow flexion agreement from one participant during a cup drinking task (see figure1). The agreement between markerless and marker-based methods was evaluated in RStudio using, Bland-Altman analysis (mean difference = -7.49 °, upper limits of agreement 20.87 °, lower limits of agreement -35.85 °); intra-class correlation coefficient (ICC = 0.91 °); and root mean squared error (RMSE = 16.30 °). Fig. 1: Elbow flexion angle during a cup drinking taskDownload : Download high-res image (95KB)Download : Download full-size image Our preliminary results suggest good agreement between markerless and marker-based motion capture for elbow flexion while performing a cup drinking task. The Kinect underestimates joint angles at local maxima and minima (see Fig. 1), a
在最近的范围审查(Scott et al.， 2022)中，我们讨论了单摄像头无标记运动捕捉(SCMoCap)如何有助于在不可能的情况下促进运动分析，例如脑瘫儿童的家庭康复(Kidziński et al.， 2020)，以及更频繁的数据收集。然而，很少有研究报告以临床可解释的方式测量误差，并且很少有证据评估SCMoCap在上肢日常生活活动中的作用。对SCMoCap进行全面验证，并对结果进行有临床意义的评估，对于有兴趣将上肢运动分析应用于临床实践的临床医生和未来的研究人员来说，将是非常宝贵的(Philp et al, 2021)。最先进的单摄像头无标记运动捕捉方法是否适合在典型的上肢功能评估中测量关节角度?研究参与者被指示进行生理和功能运动的压缩集合，这是上肢功能评估的典型组成部分。额、矢状面重复运动3次。使用10个摄像头OptiTrack Prime 13w基于标记的动作捕捉装置(NaturalPoint，美国)和Azure Kinect摄像头(Microsoft，美国)同时记录运动。采用eSync2同步设备(NaturalPoint, USA)避免系统间的暴露干扰。根据国际生物力学学会(International Society of Biomechanics)的建议，收集基于标记物的骨骼地标和关节中心(Wu et al.， 2005)。基于标记的轨迹使用MotionMonitor xGen (Innovative Sports Training, USA)进行处理，其中20 Hz低通巴特沃斯滤波器应用于标记位置。使用Azure Kinect身体跟踪计算无标记关节中心位置。使用10hz低通巴特沃斯滤波器对无标记位置进行滤波，然后上采样到120hz，与OptiTrack记录频率相匹配。信号使用互相关进行时间同步。利用Hamner的模型(Hamner, Seth & Delp, 2010)在OpenSim中求解逆运动学计算关节角。在这里，我们提出了一个参与者在喝杯任务期间肘关节弯曲协议的初步结果(见图1)。在RStudio中使用Bland-Altman分析评估无标记法和基于标记法的一致性(平均差= -7.49°，一致性上限20.87°，一致性下限-35.85°);类内相关系数(ICC = 0.91°);均方根误差(RMSE = 16.30°)。我们的初步结果表明，无标记和基于标记的动作捕捉在完成一杯饮料任务时肘关节屈曲的效果之间有很好的一致性。Kinect在局部最大值和最小值处低估了关节角度(见图1)，平均差值为-7.49°。Azure Kinect身体跟踪返回的标记位置也会受到极端运动的突然变化的影响，这并不代表运动。
{"title":"Assessing single camera markerless motion capture during upper limb activities of daily living","authors":"Bradley Scott, Edward Chadwick, Mhairi McInnes, Dimitra Blana","doi":"10.1016/j.gaitpost.2023.07.222","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.222","url":null,"abstract":"In a recent scoping review (Scott et al., 2022) we discussed how single camera markerless motion capture (SCMoCap) may help to facilitate motion analysis in situations where it would otherwise not be possible, such as at-home rehabilitation for children with cerebral palsy (Kidziński et al., 2020), and more frequent data collection. However, few studies reported error of measurement in a clinically interpretable manner and there is little evidence assessing SCMoCap during upper limb activities of daily living. Presenting a comprehensive validation of SCMoCap, alongside clinically meaningful evaluation of results would be invaluable for clinicians and future researchers who are interested in implementing upper limb movement analysis into clinical practice (Philp et al., 2021). Are state-of-the-art single camera markerless motion capture methods suitable for measuring joint angles during a typical upper-limb functional assessment? Study participants were instructed to perform a compressive collection of physiological and functional movements that are typically part of an upper limb functional assessment. Movements were repeated 3 times for both the frontal and sagittal planes. Movements were recorded simultaneously with a 10-camera OptiTrack Prime 13 W marker-based motion capture setup (NaturalPoint, USA) and Azure Kinect camera (Microsoft, USA). An eSync2 synchronization device (NaturalPoint, USA) was used to avoid exposure interference between systems. Marker-based bony landmarks and joint centers were collected as recommended by the International Society of Biomechanics (Wu et al., 2005). Marker-based trajectories were processed using MotionMonitor xGen (Innovative Sports Training, USA), where a 20 Hz lowpass Butterworth filter was applied to marker positions. Markerless joint center positions were calculated using Azure Kinect body tracking. Markerless positions were filtered using a 10 Hz lowpass Butterworth filter, then upsampled to 120 Hz matching the OptiTrack recording frequency. Signals were time synchronized using cross correlation. Joint angles were calculated by solving inverse kinematics in OpenSim using Hamner’s model (Hamner, Seth & Delp, 2010). Here we present preliminary results of elbow flexion agreement from one participant during a cup drinking task (see figure1). The agreement between markerless and marker-based methods was evaluated in RStudio using, Bland-Altman analysis (mean difference = -7.49 °, upper limits of agreement 20.87 °, lower limits of agreement -35.85 °); intra-class correlation coefficient (ICC = 0.91 °); and root mean squared error (RMSE = 16.30 °). Fig. 1: Elbow flexion angle during a cup drinking taskDownload : Download high-res image (95KB)Download : Download full-size image Our preliminary results suggest good agreement between markerless and marker-based motion capture for elbow flexion while performing a cup drinking task. The Kinect underestimates joint angles at local maxima and minima (see Fig. 1), a","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cerebral palsy is a group of different disorders that affect mobility, muscle tone and erectile structure. This condition is usually caused by damage to the brain during growth and development, usually before birth [1]. Houwen et al. [2] evaluated the effect of Botolinum Toxin treatment on the patterns of muscle activation of the rectus femoris and this study showed that BTX-A injection did not improve lower limb muscle activation patterns during walking. Muthusamy et al. [3] examined the effect of rectus femoris surgery on thirty-eight patients with CP and Patients had a significant improvement in postoperative KROM when preoperative KROM was less than 80% normal.Tedroff et al. [4] was studied in 94 children with cerebral palsy who received BoNT-A injection and results showed that BoNT-A could be effective in reducing muscle tone over a longer period of time. "How does the combination of rectus femoris transfer and botulinum toxin affect gait kinematics, range of motion, and muscle activation patterns in patients with cerebral palsy, and how do the effects compare to each treatment alone?" The study involved a motion data of patient with cerebral palsy and a normal child.a simulation model was created using the inverse dynamics method to analyze the joint angles and muscle forces during walking in opensim. The forward dynamic method was then used to simulate the effects of rectus femoris transfer and Botulinum Toxin injection on muscle weakness and surgery.Download : Download high-res image (149KB)Download : Download full-size image using SPSS V.19 software (ANOVA) and output data obtained from modeling. For right hip flexion, the Transferring group is significantly different from the Botolinum toxin group (P<0.001) and can be due to the weakness of the thigh extensor muscles in the Botulinum Toxin group. For right knee flexion, the surgical group is significantly different from the Botolinum Toxin group (P<0.001) and the patient's initial model and it can be concluded that rectus femoris surgery can cause initial relative improvement in the patient and strengthening the extensor knee muscles can help improve the patient's movement. For left hip flexion, the surgical group is significantly different from the Botolinum Toxin group (P<0.001) and can be due to the weakness of the extensor thigh muscles in the Botolinum Toxin group. For left knee flexion,the surgical group is significantly different from Botolinum Toxin group (P<0.001) and the patient's initial model and it can be concluded that rectus femoris Transferring surgery can cause initial relative improvement in the patient The results show that therapeutic interventions including surgery in the first stage are more effective than botulinum toxin and muscle weakness by botulinum toxin injection in the short term may not be effective and require scheduled studies over long periods of time.
{"title":"Analyzing the Impacts of Rectus Femoris Transferring and Botulinum Toxin on Cerebral Palsy: a Case study","authors":"Sadegh Madadi, Mostafa Rostami, Afshin Taheri Azam","doi":"10.1016/j.gaitpost.2023.07.143","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.143","url":null,"abstract":"Cerebral palsy is a group of different disorders that affect mobility, muscle tone and erectile structure. This condition is usually caused by damage to the brain during growth and development, usually before birth [1]. Houwen et al. [2] evaluated the effect of Botolinum Toxin treatment on the patterns of muscle activation of the rectus femoris and this study showed that BTX-A injection did not improve lower limb muscle activation patterns during walking. Muthusamy et al. [3] examined the effect of rectus femoris surgery on thirty-eight patients with CP and Patients had a significant improvement in postoperative KROM when preoperative KROM was less than 80% normal.Tedroff et al. [4] was studied in 94 children with cerebral palsy who received BoNT-A injection and results showed that BoNT-A could be effective in reducing muscle tone over a longer period of time. \"How does the combination of rectus femoris transfer and botulinum toxin affect gait kinematics, range of motion, and muscle activation patterns in patients with cerebral palsy, and how do the effects compare to each treatment alone?\" The study involved a motion data of patient with cerebral palsy and a normal child.a simulation model was created using the inverse dynamics method to analyze the joint angles and muscle forces during walking in opensim. The forward dynamic method was then used to simulate the effects of rectus femoris transfer and Botulinum Toxin injection on muscle weakness and surgery.Download : Download high-res image (149KB)Download : Download full-size image using SPSS V.19 software (ANOVA) and output data obtained from modeling. For right hip flexion, the Transferring group is significantly different from the Botolinum toxin group (P<0.001) and can be due to the weakness of the thigh extensor muscles in the Botulinum Toxin group. For right knee flexion, the surgical group is significantly different from the Botolinum Toxin group (P<0.001) and the patient's initial model and it can be concluded that rectus femoris surgery can cause initial relative improvement in the patient and strengthening the extensor knee muscles can help improve the patient's movement. For left hip flexion, the surgical group is significantly different from the Botolinum Toxin group (P<0.001) and can be due to the weakness of the extensor thigh muscles in the Botolinum Toxin group. For left knee flexion,the surgical group is significantly different from Botolinum Toxin group (P<0.001) and the patient's initial model and it can be concluded that rectus femoris Transferring surgery can cause initial relative improvement in the patient The results show that therapeutic interventions including surgery in the first stage are more effective than botulinum toxin and muscle weakness by botulinum toxin injection in the short term may not be effective and require scheduled studies over long periods of time.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.133
Annika Kruse, Andreas Habersack, Bernhard Guggenberger, Markus Tilp, Martin Svehlik
{"title":"Gastrocnemius medialis Muscle-tendon unit Properties do not differ between Children with unilateral and bilateral spastic Cerebral Palsy","authors":"Annika Kruse, Andreas Habersack, Bernhard Guggenberger, Markus Tilp, Martin Svehlik","doi":"10.1016/j.gaitpost.2023.07.133","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.133","url":null,"abstract":"","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The phantom sensation is a feeling on an amputated limb. The features of the phantom sensation can be variable from person to person. It may accompany the person continuously, be present occasionally or disappear completely. This sensation may be accompanied by pain, in which case it is called phantom pain. Although the effects of phantom pain on many functions are widely known, the effects of phantom sensation on gait was not been adequately clarified yet (1). How does the presence of phantom sensation during gait affect gait characteristics? Three unilateral transtibial amputees and one healthy individual were included in the study. Three questions of the Prosthesis Evaluation questionnaire were asked to amputees to assess the frequency, severity, and degree of discomfort caused by the phantom sensation over the past four weeks. The amputees who had additional health issues and experienced phantom pain or other disturbing phantom sensations were excluded. The gait of individuals was evaluated with a sensor-based gait analysis system (RehaGait-Pro) at the neutral and %5 perturbated treadmill (ReaxRun-Pro). Gait parameters were analyzed and all variables were compared with Perry’s normal expected values (2). The change in gait characteristics of individuals to adapt to the perturbated ground was classified as decrease/increase by taking the gait characteristics on flat ground as a reference, and these changes were evaluated according to their similarity to a healthy individual. Individuals were as follows: Case 1 had phantom sensation during walking, Case 2; had phantom sensation only during resting, Case 3; had no phantom sensation, and Case 4 was a healthy individual. The individual who showed the most similarity with the healthy individual in adaptation to perturbation was the individual who felt phantom sensation during walking (Case 1). Case 1 followed a similar strategy for seven gait parameters. Case 2 gave similar adaptive responses with the healthy individual in 6 gait parameters. The individual without phantom sensation showed adaptive responses similar to the healthy individual in 3 different parameters (Table).Download : Download high-res image (164KB)Download : Download full-size image These results showed that phantom sensation may be a functional sensation and that maintaining the holistic body schema of an amputee may contribute to the nature of gait (1). It is recommended that further research be conducted in large groups. Acknowledgements: This research was funded by The Scientific and Technological Research Council of Turkey (Project number: S219S809).
{"title":"Effect of feeling the phantom sensation during gait on spatiotemporal gait characteristics in individuals with transtibial amputation","authors":"Nimet Sermenli Aydın, İlke Kurt, Halit Selçuk, Sinem Salar, Sezer Ulukaya, Hilal Keklicek","doi":"10.1016/j.gaitpost.2023.07.228","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.228","url":null,"abstract":"The phantom sensation is a feeling on an amputated limb. The features of the phantom sensation can be variable from person to person. It may accompany the person continuously, be present occasionally or disappear completely. This sensation may be accompanied by pain, in which case it is called phantom pain. Although the effects of phantom pain on many functions are widely known, the effects of phantom sensation on gait was not been adequately clarified yet (1). How does the presence of phantom sensation during gait affect gait characteristics? Three unilateral transtibial amputees and one healthy individual were included in the study. Three questions of the Prosthesis Evaluation questionnaire were asked to amputees to assess the frequency, severity, and degree of discomfort caused by the phantom sensation over the past four weeks. The amputees who had additional health issues and experienced phantom pain or other disturbing phantom sensations were excluded. The gait of individuals was evaluated with a sensor-based gait analysis system (RehaGait-Pro) at the neutral and %5 perturbated treadmill (ReaxRun-Pro). Gait parameters were analyzed and all variables were compared with Perry’s normal expected values (2). The change in gait characteristics of individuals to adapt to the perturbated ground was classified as decrease/increase by taking the gait characteristics on flat ground as a reference, and these changes were evaluated according to their similarity to a healthy individual. Individuals were as follows: Case 1 had phantom sensation during walking, Case 2; had phantom sensation only during resting, Case 3; had no phantom sensation, and Case 4 was a healthy individual. The individual who showed the most similarity with the healthy individual in adaptation to perturbation was the individual who felt phantom sensation during walking (Case 1). Case 1 followed a similar strategy for seven gait parameters. Case 2 gave similar adaptive responses with the healthy individual in 6 gait parameters. The individual without phantom sensation showed adaptive responses similar to the healthy individual in 3 different parameters (Table).Download : Download high-res image (164KB)Download : Download full-size image These results showed that phantom sensation may be a functional sensation and that maintaining the holistic body schema of an amputee may contribute to the nature of gait (1). It is recommended that further research be conducted in large groups. Acknowledgements: This research was funded by The Scientific and Technological Research Council of Turkey (Project number: S219S809).","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.200
Salvador Pitarch-Corresa, Helios De Rosario - Martínez, Juan López - Pascual, Rosa Porcar - Seder, Ana Ruescas - Nicolau, Fermín Basso - Della Vedova
4D scanners (4DSC) are innovative photogrammetry-based 3D/4D capture and analysis systems for anthropometric static and dynamic measurements. Recent research studies have been carried out to demonstrate its validity for kinematic gait assessment [1] and to evaluate the effects of technical marker location on traditional kinematic analysis [2]. Compared to 3D systems, 4DSC allow to capture more detail of human motion, including precise volumes and shapes of body segments that can be used to make more accurate calculations [3]. 4DSC also provides a 3D dynamic avatar reconstruction to visual analysis in 360º vision and information of anthropometric measures in motion. Due to these unique features, 4DSC have set a new direction in motion analysis, especially related with pathological conditions of the nervous system [4]. Can “4D scans” provide significant information related to dynamic soft tissue behavior to improve clinical understanding in neurological disorders gait motion analysis? A case study was conducted with 16-year-old male participant diagnosed of cerebellum ataxia with hypoplasia associated to motor alteration, but able to walk without assistance. Parents’ written consent was obtained. Participant performed consecutive gait repetitions (3 for each limb) at self-selected speed at IBV Human Analysis Laboratory. Tests were recorded with Move4D scanner and Dinascan/IBV force plate. Kinematic and dynamic gait parameters were calculated from the data recorded using AMHPlus/IBV software. Additionally, changes in the calf shape during gait were calculated from the Move4D data using custom developed Python algorithms. Leg calf surface was determined as the posterior area of the mesh at each leg, between tibial tuberosity projection and midpoint of Achilles tendon. At each instant of the gait cycle, the positions of the vertices of those areas were rotated and translated keeping their relative distances, in order to match their positions in the reference posture as closely as possible. Deformation of the skin was measured as the field of 3D distances between the reference points and their displaced positions. That amount of deformation at each instant was quantified for both legs, as the sum of the eigenvectors of that field of deformations (in mm). 4DSC results allowed to objectify gait kinetic and kinematic alterations and a different pattern in soft tissue deformation between legs (see Figure), which were consistent with the clinical impression. Figure. Differences in calf surface deformation and reaction forces between limbs during single leg support. Representation of mesh extracted from Move4D data during gait on top.Download : Download high-res image (105KB)Download : Download full-size image Information extracted from Move4D allows to eliminate remaining limitations of traditional gait motion analysis systems. Recent studies propose methodologies to predict human muscle activity from skin surface behavior [5,6]. Single system solution for
{"title":"Innovative use of 4D scanner for gait analysis of neurological disorders: A case study","authors":"Salvador Pitarch-Corresa, Helios De Rosario - Martínez, Juan López - Pascual, Rosa Porcar - Seder, Ana Ruescas - Nicolau, Fermín Basso - Della Vedova","doi":"10.1016/j.gaitpost.2023.07.200","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.200","url":null,"abstract":"4D scanners (4DSC) are innovative photogrammetry-based 3D/4D capture and analysis systems for anthropometric static and dynamic measurements. Recent research studies have been carried out to demonstrate its validity for kinematic gait assessment [1] and to evaluate the effects of technical marker location on traditional kinematic analysis [2]. Compared to 3D systems, 4DSC allow to capture more detail of human motion, including precise volumes and shapes of body segments that can be used to make more accurate calculations [3]. 4DSC also provides a 3D dynamic avatar reconstruction to visual analysis in 360º vision and information of anthropometric measures in motion. Due to these unique features, 4DSC have set a new direction in motion analysis, especially related with pathological conditions of the nervous system [4]. Can “4D scans” provide significant information related to dynamic soft tissue behavior to improve clinical understanding in neurological disorders gait motion analysis? A case study was conducted with 16-year-old male participant diagnosed of cerebellum ataxia with hypoplasia associated to motor alteration, but able to walk without assistance. Parents’ written consent was obtained. Participant performed consecutive gait repetitions (3 for each limb) at self-selected speed at IBV Human Analysis Laboratory. Tests were recorded with Move4D scanner and Dinascan/IBV force plate. Kinematic and dynamic gait parameters were calculated from the data recorded using AMHPlus/IBV software. Additionally, changes in the calf shape during gait were calculated from the Move4D data using custom developed Python algorithms. Leg calf surface was determined as the posterior area of the mesh at each leg, between tibial tuberosity projection and midpoint of Achilles tendon. At each instant of the gait cycle, the positions of the vertices of those areas were rotated and translated keeping their relative distances, in order to match their positions in the reference posture as closely as possible. Deformation of the skin was measured as the field of 3D distances between the reference points and their displaced positions. That amount of deformation at each instant was quantified for both legs, as the sum of the eigenvectors of that field of deformations (in mm). 4DSC results allowed to objectify gait kinetic and kinematic alterations and a different pattern in soft tissue deformation between legs (see Figure), which were consistent with the clinical impression. Figure. Differences in calf surface deformation and reaction forces between limbs during single leg support. Representation of mesh extracted from Move4D data during gait on top.Download : Download high-res image (105KB)Download : Download full-size image Information extracted from Move4D allows to eliminate remaining limitations of traditional gait motion analysis systems. Recent studies propose methodologies to predict human muscle activity from skin surface behavior [5,6]. Single system solution for ","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Predictive and functional calibration methods can be used to estimate joint centre and axis localisation in 3D motion analysis (1-6). The method of Harrington and the geometric sphere fit method are implemented in Human Body Model (HBM-II) as they are the most accurate predictive and functional calibration method respectively (1-6). The effect of calibration methods on kinematics is less researched although relevant for clinical interpretations. Does the Harrington predictive and the combined functional knee and hip calibration method in 3D gait analysis produce comparable 3D joint kinematics? Gait of 12 healthy subjects (11 F, mean(SD) age 26.4 (9.3)years, BMI 24.6 (2.8)kg/m2) was measured at Computer Assisted Rehabilitation ENvironment using HBM-II. Subjects started with a 6 minutes familiarisation period. Afterwards, a static model initialization was done (5 s standing in Tpose) using the predictive method of Harrington (1) followed by a measurement of three minutes walking at 1.1 m/s. Next, the system was reset and a combined functional knee (performing knee extension/flexion movements) and hip (performing starARc movement (6)) calibration was done using the geometric sphere fit method (2). A similar gait measurement was done. Data of 3D joint angles were extrapolated to strides (0-100%). For each subject, the difference in joint angle between the methods was calculated for each instant of the gait cycle. Mean differences were calculated and statistical parametric mapping (paired t-test) was used for group comparisons. Although the waveform patterns were comparable for the methods (Fig. 1A), significant differences in amplitude were observed for sagittal hip, knee and ankle angles and transverse hip angle (Fig. 1C), with maximum mean differences ranging from 3.6° to 7.4° (Fig. 1B). Mean differences in sagittal trunk and pelvis angles and frontal plane angles were smaller (range 0.0°–1.1°) and non-significant. The kinematic differences between methods varied among subjects (e.g. maximum knee flexion difference range: 1.9°-12.5°, Fig. 1D). Download : Download high-res image (457KB)Download : Download full-size image 3D gait analysis using the Harrington predictive or combined functional knee and hip calibration method results in different sagittal hip, knee, ankle angles and transverse hip angle. Differences are clinically relevant as they exceed 5°, corresponding to the measurement error for 3D gait kinematics (7). The difference of 1° in other joint angles indicates no critically interfere of the calibration method. The choice for a calibration method should be consistent in a lab and should be based on the context (4, 6). The functional method is more reliable as it is independent on marker placement, but is sensitive for measurement artefacts and quality of movements (6). This reduces repeatability and limits its use in patients having restricted range of motion. The predictive method is sensitive for marker placement and anthropometric mea
预测和功能校准方法可用于估计三维运动分析中的关节中心和轴定位(1-6)。Harrington方法和几何球拟合方法分别是最准确的预测校准方法和功能校准方法，因此在Human Body Model (HBM-II)中实现(1-6)。校准方法对运动学的影响虽然与临床解释相关，但研究较少。在三维步态分析中，哈林顿预测和联合功能膝关节和髋关节校准方法是否产生可比较的三维关节运动学?采用HBM-II在计算机辅助康复环境下测量12名健康受试者(11名F，平均(SD)年龄26.4(9.3)岁，BMI 24.6 (2.8)kg/m2)的步态。受试者开始有6分钟的熟悉期。然后，使用Harrington(1)的预测方法进行静态模型初始化(在Tpose中站立5 s)，然后测量以1.1 m/s的速度行走3分钟。接下来，对系统进行复位，并使用几何球体拟合方法(2)对膝关节(进行膝关节伸展/屈曲运动)和髋关节(进行starARc运动)进行联合功能校准。三维关节角度数据外推至步长(0-100%)。对于每个受试者，在步态周期的每个瞬间计算两种方法之间的关节角度差异。计算均数差异，采用统计参数映射(配对t检验)进行组间比较。尽管两种方法的波形模式具有可比性(图1A)，但在髋矢状角、膝关节角和踝关节角以及髋横角的振幅上观察到显著差异(图1C)，最大平均差异范围为3.6°至7.4°(图1B)。躯干和骨盆矢状角和额平面角的平均差异较小(范围为0.0°-1.1°)，无统计学意义。不同方法的运动学差异因受试者而异(例如，最大膝关节屈曲差异范围:1.9°-12.5°，图1D)。下载:下载全尺寸图像3D步态分析，使用哈林顿预测或结合功能的膝关节和髋关节校准方法，得到不同的髋矢状、膝关节、踝关节角度和髋横角。差异超过5°时具有临床相关性，对应于三维步态运动学的测量误差(7)。其他关节角度相差1°表明校准方法没有严重干扰。校准方法的选择应在实验室中保持一致，并应基于环境(4,6)。功能方法更可靠，因为它独立于标记物的放置，但对测量伪像和运动质量敏感(6)。这降低了可重复性，限制了其在运动范围有限的患者中的使用。预测方法对标记位置和人体测量很敏感(8)，因此需要经验丰富的操作人员。然而，这种方法对患者来说是实用可行的，因此在荷兰被广泛使用。
{"title":"The predictive and functional calibration method in 3D gait analysis using Human Body Model-II produce different 3D joint angles","authors":"Rachel Senden, Rik Marcellis, Reinhard Claeys, Kenneth Meijer, Marianne Witlox, Paul Willems","doi":"10.1016/j.gaitpost.2023.07.227","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.227","url":null,"abstract":"Predictive and functional calibration methods can be used to estimate joint centre and axis localisation in 3D motion analysis (1-6). The method of Harrington and the geometric sphere fit method are implemented in Human Body Model (HBM-II) as they are the most accurate predictive and functional calibration method respectively (1-6). The effect of calibration methods on kinematics is less researched although relevant for clinical interpretations. Does the Harrington predictive and the combined functional knee and hip calibration method in 3D gait analysis produce comparable 3D joint kinematics? Gait of 12 healthy subjects (11 F, mean(SD) age 26.4 (9.3)years, BMI 24.6 (2.8)kg/m2) was measured at Computer Assisted Rehabilitation ENvironment using HBM-II. Subjects started with a 6 minutes familiarisation period. Afterwards, a static model initialization was done (5 s standing in Tpose) using the predictive method of Harrington (1) followed by a measurement of three minutes walking at 1.1 m/s. Next, the system was reset and a combined functional knee (performing knee extension/flexion movements) and hip (performing starARc movement (6)) calibration was done using the geometric sphere fit method (2). A similar gait measurement was done. Data of 3D joint angles were extrapolated to strides (0-100%). For each subject, the difference in joint angle between the methods was calculated for each instant of the gait cycle. Mean differences were calculated and statistical parametric mapping (paired t-test) was used for group comparisons. Although the waveform patterns were comparable for the methods (Fig. 1A), significant differences in amplitude were observed for sagittal hip, knee and ankle angles and transverse hip angle (Fig. 1C), with maximum mean differences ranging from 3.6° to 7.4° (Fig. 1B). Mean differences in sagittal trunk and pelvis angles and frontal plane angles were smaller (range 0.0°–1.1°) and non-significant. The kinematic differences between methods varied among subjects (e.g. maximum knee flexion difference range: 1.9°-12.5°, Fig. 1D). Download : Download high-res image (457KB)Download : Download full-size image 3D gait analysis using the Harrington predictive or combined functional knee and hip calibration method results in different sagittal hip, knee, ankle angles and transverse hip angle. Differences are clinically relevant as they exceed 5°, corresponding to the measurement error for 3D gait kinematics (7). The difference of 1° in other joint angles indicates no critically interfere of the calibration method. The choice for a calibration method should be consistent in a lab and should be based on the context (4, 6). The functional method is more reliable as it is independent on marker placement, but is sensitive for measurement artefacts and quality of movements (6). This reduces repeatability and limits its use in patients having restricted range of motion. The predictive method is sensitive for marker placement and anthropometric mea","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号