Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.230
Ulises Daniel Serratos Hernandez, Jack Brookes, Samson Hall, Juliana K. Sporrer, Sajjad Zabbah, Dominik R. Bach
Understanding and characterising human movements is complex due to the diversity of human actions and their inherent inter, intra, and secular variability. Traditional marker-based, and more recently, some marker-less motion capture (MoCap) systems have demonstrated to be reliable tools for movement analysis. However, in complex experimental set ups involving virtual reality (VR) and free movements (as in [1]), accuracy and reliability tend to decrease due to occlusion, sensor blind spots, marker detachment, and other artifacts. Furthermore, when actions are less distinct, e.g., fast walk and slow run, current classification methods tend to fail when actions overlap, which is expected as even researchers struggle to manually label such actions. Can current marker-less MoCap systems, pose estimation (PE) algorithms, and advanced action classification (AC) methods: (1) accurately track participant movements in VR; (2) cluster participant actions. The experiment consisted of avoiding threats (Fig. 1A) whilst collecting fruit in VR environments (n=29 participants, 5x10m area), see [1]. The Unity® software [2], based on the Unity Experiment Framework [3], was used to create the VR experiment, which was streamed through an HTC vive pro (HTC Corporation) VR headset. Movements were recorded using 5 ELP cameras (1280×720 @120 Hz) synchronised with the Open Broadcaster Software® (OBS) [4]. Openpose [5] was employed for PE (Fig. 1B). Euclidean distances, and angular positions, velocities, and accelerations were derived from cartesian positions. Finally, Uniform Manifold Approximation and Projection (UMAP) was used to embed high-dimensional features into a low-dimensional space, and Hierarchical Density Based Spatial Clustering of Applications (HDBSCAN) was used for classification (see Fig. 1E), similar to B-SOiD [6]. Participants were virtually killed by the threat in 223 episodes, for which the participants’ last poses were estimated. After applying UMAP and HDBSCAN, 5 pose clusters were found (see Fig. 1C-D), which depict: (a) stand up, picking fruit with slow escape; (b) stand up, arms extended and slow escape; (c) long retreat at fast speed; (d) short retreat at medium speed; (e) crouching and picking fruit; (x) 4% unlabelled. Fig. 1. (A) VR-threat, (B) Participant estimated 3D-pose, (C) Pose clusters, (D) Cluster examples, (E) Methodology.Download : Download high-res image (176KB)Download : Download full-size image Marker-less MoCap and PE methods were mostly successful for participants’ last poses. However, in some cases, and during exploration, tracking was lost due to occlusion and sensor blind spots. The results from the AC methods are an indication of the potential use of unsupervised methods to find participant actions under threat in VR. Nevertheless, such clustering is rather general, and had some AC errors, which could not be quantified as further work is needed to understand and define where the threshold of overlapping actions occurs. The re
{"title":"Movement tracking and action classification for human behaviour under threat in virtual reality","authors":"Ulises Daniel Serratos Hernandez, Jack Brookes, Samson Hall, Juliana K. Sporrer, Sajjad Zabbah, Dominik R. Bach","doi":"10.1016/j.gaitpost.2023.07.230","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.230","url":null,"abstract":"Understanding and characterising human movements is complex due to the diversity of human actions and their inherent inter, intra, and secular variability. Traditional marker-based, and more recently, some marker-less motion capture (MoCap) systems have demonstrated to be reliable tools for movement analysis. However, in complex experimental set ups involving virtual reality (VR) and free movements (as in [1]), accuracy and reliability tend to decrease due to occlusion, sensor blind spots, marker detachment, and other artifacts. Furthermore, when actions are less distinct, e.g., fast walk and slow run, current classification methods tend to fail when actions overlap, which is expected as even researchers struggle to manually label such actions. Can current marker-less MoCap systems, pose estimation (PE) algorithms, and advanced action classification (AC) methods: (1) accurately track participant movements in VR; (2) cluster participant actions. The experiment consisted of avoiding threats (Fig. 1A) whilst collecting fruit in VR environments (n=29 participants, 5x10m area), see [1]. The Unity® software [2], based on the Unity Experiment Framework [3], was used to create the VR experiment, which was streamed through an HTC vive pro (HTC Corporation) VR headset. Movements were recorded using 5 ELP cameras (1280×720 @120 Hz) synchronised with the Open Broadcaster Software® (OBS) [4]. Openpose [5] was employed for PE (Fig. 1B). Euclidean distances, and angular positions, velocities, and accelerations were derived from cartesian positions. Finally, Uniform Manifold Approximation and Projection (UMAP) was used to embed high-dimensional features into a low-dimensional space, and Hierarchical Density Based Spatial Clustering of Applications (HDBSCAN) was used for classification (see Fig. 1E), similar to B-SOiD [6]. Participants were virtually killed by the threat in 223 episodes, for which the participants’ last poses were estimated. After applying UMAP and HDBSCAN, 5 pose clusters were found (see Fig. 1C-D), which depict: (a) stand up, picking fruit with slow escape; (b) stand up, arms extended and slow escape; (c) long retreat at fast speed; (d) short retreat at medium speed; (e) crouching and picking fruit; (x) 4% unlabelled. Fig. 1. (A) VR-threat, (B) Participant estimated 3D-pose, (C) Pose clusters, (D) Cluster examples, (E) Methodology.Download : Download high-res image (176KB)Download : Download full-size image Marker-less MoCap and PE methods were mostly successful for participants’ last poses. However, in some cases, and during exploration, tracking was lost due to occlusion and sensor blind spots. The results from the AC methods are an indication of the potential use of unsupervised methods to find participant actions under threat in VR. Nevertheless, such clustering is rather general, and had some AC errors, which could not be quantified as further work is needed to understand and define where the threshold of overlapping actions occurs. The re","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298544","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.2023.07.201
George Plakoutsis, Dimitrios Zapantis, Eirini-Maria Panagiotopoulou, Eleftherios Paraskevopoulos, Maria Papandreou
Physical fitness is of great importance to several sports and also, in the context of public health. Several training methods such as plyometric jump training are routinely used by athletes for promoting performance. The countermovement jump (CMJ) is one of the most implemented method for testing lower limb mechanical abilities. The purpose of the present study was to examine the validity and reliability of the KForce plates system with the concurrent use of 'My Jump 2' application for measuring CMJ. Is KForce plates system a valid and reliable tool for measuring CMJ? Thirty-four collegiate athletes, twenty-two males and twelve females (age=21.6±5.7), volunteered to participate in the present study. Each participant performed three maximal CMJs while standing on a portable force platform. The jumps were recorded with a portable KForce plates system and a concurrent validated application ‘My Jump 2’ through iPhone 13 at the same time. Each participant repeated the testing procedure after seven days in order to assess the reliability of the measurements (ICC). Systematic bias between sessions and tools was evaluated using paired t-test and Bland-Altman analysis. High test-retest reliability (ICC > 0.87) was observed for all measures (jump height and jump time) in-between conditions. Very large correlations in the sample were observed between KForce plates system and My Jump 2 app for CMJ (jump height, r = 1.000, p = 0.001) and CMJ (jump time, r = 0.999, p = 0.001). The Bland-Altman’s plot illustrates limits of agreement between KForce plates system and My Jump 2 app where the majority of the data are within the 95% CIs. The results of the current study suggest that the KForce plates system was proven a valid and reliable tool for measuring jump performance in physically active adults.
身体健康对一些运动非常重要，在公共卫生方面也是如此。有几种训练方法，如增强式跳跃训练，是运动员为了提高成绩而经常使用的。反向跳跃是目前应用最广泛的下肢机械能力测试方法之一。本研究的目的是检验KForce板系统的有效性和可靠性，并同时使用“我的跳跃2”应用程序测量CMJ。KForce板系统是测量CMJ的有效和可靠的工具吗?34名大学生运动员，男22名，女12名，年龄=21.6±5.7岁。每个参与者站在一个便携式受力平台上进行了三个最大的CMJs。通过iPhone 13同时使用便携式KForce板系统和并发验证应用程序“My Jump 2”记录这些跳跃。每个参与者在7天后重复测试程序，以评估测量的可靠性(ICC)。使用配对t检验和Bland-Altman分析评估会话和工具之间的系统偏差。在中间条件下，所有测量(跳跃高度和跳跃时间)的重测信度均较高(ICC > 0.87)。在样本中，KForce平板系统和My Jump 2应用程序在CMJ(跳跃高度，r = 1.000, p = 0.001)和CMJ(跳跃时间，r = 0.999, p = 0.001)方面存在非常大的相关性。Bland-Altman的图表说明了KForce板块系统和《我的跳跃2》应用之间的一致性限制，其中大部分数据都在95% ci内。目前的研究结果表明，KForce钢板系统被证明是一种有效和可靠的工具，用于测量身体活跃的成年人的跳跃表现。
{"title":"Validity and reliability of the portable Kforce plates system with the use of a smartphone application for measuring countermovement jump","authors":"George Plakoutsis, Dimitrios Zapantis, Eirini-Maria Panagiotopoulou, Eleftherios Paraskevopoulos, Maria Papandreou","doi":"10.1016/j.gaitpost.2023.07.201","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.201","url":null,"abstract":"Physical fitness is of great importance to several sports and also, in the context of public health. Several training methods such as plyometric jump training are routinely used by athletes for promoting performance. The countermovement jump (CMJ) is one of the most implemented method for testing lower limb mechanical abilities. The purpose of the present study was to examine the validity and reliability of the KForce plates system with the concurrent use of 'My Jump 2' application for measuring CMJ. Is KForce plates system a valid and reliable tool for measuring CMJ? Thirty-four collegiate athletes, twenty-two males and twelve females (age=21.6±5.7), volunteered to participate in the present study. Each participant performed three maximal CMJs while standing on a portable force platform. The jumps were recorded with a portable KForce plates system and a concurrent validated application ‘My Jump 2’ through iPhone 13 at the same time. Each participant repeated the testing procedure after seven days in order to assess the reliability of the measurements (ICC). Systematic bias between sessions and tools was evaluated using paired t-test and Bland-Altman analysis. High test-retest reliability (ICC > 0.87) was observed for all measures (jump height and jump time) in-between conditions. Very large correlations in the sample were observed between KForce plates system and My Jump 2 app for CMJ (jump height, r = 1.000, p = 0.001) and CMJ (jump time, r = 0.999, p = 0.001). The Bland-Altman’s plot illustrates limits of agreement between KForce plates system and My Jump 2 app where the majority of the data are within the 95% CIs. The results of the current study suggest that the KForce plates system was proven a valid and reliable tool for measuring jump performance in physically active adults.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298710","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.2023.07.126
Eylül Pınar Kısa, Gökçe Leblebici, Ela Tarakcı, Özgür Kasapçopur
{"title":"Effects of two different exercise programs on gait in children with scoliosis diagnosed Juvenile Idiopathic Arthritis","authors":"Eylül Pınar Kısa, Gökçe Leblebici, Ela Tarakcı, Özgür Kasapçopur","doi":"10.1016/j.gaitpost.2023.07.126","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.126","url":null,"abstract":"","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298716","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}
Falls occur frequently in wheelchair basketball games [1]. A fall during a game not only increases the risk of injury but can also delay the player's participation in the next play, which will affect the outcome of the game. This study aimed to explore the relationship between falls and winning or losing in wheelchair basketball games, and to clarify the importance of fall prevention. Is there a relationship between the number or the situation of falls occurring in wheelchair basketball competitions and the winning/losing of games? This study was a cross-sectional video analysis study. We watched official match videos of the Tokyo 2020 Paralympic wheelchair basketball final tournament and analyzed the occurrence of falls in a total of 20 games [2]. The analysis items included the number of falls, the classification of the faller, playing time when falling, playing phase, contact with another player, foul judgement, location of the fall, shooting motion, ball retention, and time passing after a fall. Then, we classified the falls into two groups: falls that occurred in the winning team and the losing team. The number of falls was compared between the winning and losing teams, and the analysis items were compared between the groups using chi-square tests and cross-tabulation tables. The significance level was set at 0.05. Table 1 showed the results of the comparison of fall situation characteristics in winning teams and losing teams. A total of 326 falls were observed, of which 138 occurred on the winning teams and 188 on the losing teams. There was a significant difference between winning and losing teams in the classification of fallers (p=0.005). Also, a significant difference was found in the playing time of the game when falls occurred (p=0.024). There were no significant differences between the winning and losing teams in the other items related to fall situation. This study is the first report to clarify the relationship between the occurrence of falls in wheelchair basketball and the winning and losing of a game. Falls of 4-4.5 players, with relatively good trunk control [3], occurred twice as often in the losing team as in the winning team. Then, the number of falls of the losing team increased in the latter half of the game. The occurrence of many falls in the losing team may be related to their lack of chair work skills in the 4-4.5 classification to avoid falls, and physical factors such as fatigue. While falls need to be prevented in all players and situations, this study indicated the need to address fall prevention to win games, especially in the 4-4.5 classification and in the latter half of the game.
{"title":"Association between the occurrence of falls and winning and losing in the final tournament of wheelchair basketball at Paralympic games","authors":"Rami Mizuta, Noriaki Maeda, Junpei Sasadai, Reia Shimizu, Akira Suzuki, Makoto Komiya, Kazuki Fukui, Tsubasa Tashiro, Shogo Tsutsumi, Yukio Urabe","doi":"10.1016/j.gaitpost.2023.07.158","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.158","url":null,"abstract":"Falls occur frequently in wheelchair basketball games [1]. A fall during a game not only increases the risk of injury but can also delay the player's participation in the next play, which will affect the outcome of the game. This study aimed to explore the relationship between falls and winning or losing in wheelchair basketball games, and to clarify the importance of fall prevention. Is there a relationship between the number or the situation of falls occurring in wheelchair basketball competitions and the winning/losing of games? This study was a cross-sectional video analysis study. We watched official match videos of the Tokyo 2020 Paralympic wheelchair basketball final tournament and analyzed the occurrence of falls in a total of 20 games [2]. The analysis items included the number of falls, the classification of the faller, playing time when falling, playing phase, contact with another player, foul judgement, location of the fall, shooting motion, ball retention, and time passing after a fall. Then, we classified the falls into two groups: falls that occurred in the winning team and the losing team. The number of falls was compared between the winning and losing teams, and the analysis items were compared between the groups using chi-square tests and cross-tabulation tables. The significance level was set at 0.05. Table 1 showed the results of the comparison of fall situation characteristics in winning teams and losing teams. A total of 326 falls were observed, of which 138 occurred on the winning teams and 188 on the losing teams. There was a significant difference between winning and losing teams in the classification of fallers (p=0.005). Also, a significant difference was found in the playing time of the game when falls occurred (p=0.024). There were no significant differences between the winning and losing teams in the other items related to fall situation. This study is the first report to clarify the relationship between the occurrence of falls in wheelchair basketball and the winning and losing of a game. Falls of 4-4.5 players, with relatively good trunk control [3], occurred twice as often in the losing team as in the winning team. Then, the number of falls of the losing team increased in the latter half of the game. The occurrence of many falls in the losing team may be related to their lack of chair work skills in the 4-4.5 classification to avoid falls, and physical factors such as fatigue. While falls need to be prevented in all players and situations, this study indicated the need to address fall prevention to win games, especially in the 4-4.5 classification and in the latter half of the game.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298717","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.2023.07.187
Ariana Ortigas Vasquez, William R. Taylor, Barbara Postolka, Pascal Schütz, Allan Maas, Matthias Woiczinski, Thomas M. Grupp, Adrian Sauer
Kinematic analysis involves calculating signals from optical/inertial datapoints to represent the relative movement of joint segments. The exact choice of local segment frame orientation and position has been shown to drastically influence the shape and magnitude of the associated kinematic signals, making the consistent interpretation of the underlying motion a challenge [1,2]. Despite attempts to standardise the reporting of these signals [3], a lack of consensus around joint coordinate frame definitions remains. An approach capable of accommodating different analytical methods and reconciling these differences in frame alignment, while ensuring consistent interpretations, is therefore crucial. Given sets of kinematic data, can mathematical optimisation be leveraged to achieve a consistent interpretation of the underlying movement patterns, independent of joint axis definitions? Here, we assess a REference FRame Alignment MEthod (REFRAME) on the in vivo moving-fluoroscopy-based knee kinematics of 10 healthy subjects (5 trials of stair descent each) [4]. Using three methods of defining the flexion/extension axis (cylindrical axis (CA), functional flexion axis (FFA), and transepicondylar axis (TEA)), three different femoral frames were defined for each trial, in addition to a single tibial frame [1]. Rotations of the tibia relative to the femur were calculated, alongside translational positions of the femoral origins in the tibial frame. By implementing REFRAME (as a constrained nonlinear minimisation of ab/adduction and int/external rotation root-mean-square, in addition to all translation variances), local frames were repositioned and reorientated, to derive a set of "REFRAMEd" signals. Fig. 1 - Knee kinematics (rotations[°]: tibia relative to femur; translations[mm]: femur relative to tibia) during a sample stair descent trial, using three different primary axes, before (raw) and after REFRAME. (CA and FFA partially covered by TEA) Download : Download high-res image (294KB)Download : Download full-size image Across all subjects and trials, before REFRAME implementation, the maximum absolute differences between kinematic signals representing the same underlying movement, but derived using different joint axis approaches, reached 1.61° for flexion/extension, 12.00° for ab/adduction, and 12.02° for int/external rotation, in addition to 2.28 mm for mediolateral, 10.60 mm for anteroposterior, and 12.23 mm for proximodistal translations. After REFRAME, maximum differences peaked at 0.78°, 0.08° and 0.08° for flexion/extension, ab/adduction and int/external rotation, respectively; For translations, values peaked at 0.24 mm, 0.10 mm and 0.13 mm in the mediolateral, anteroposterior and proximodistal directions. Moreover, the three signals converged after REFRAME optimisation (Fig1). For each underlying movement pattern, the analysis approach (method of axis definition) affected the characteristics of the kinematic signals. By implementing REFRAME, tibi
{"title":"A reference frame alignment method for the consistent interpretation of kinematic signals","authors":"Ariana Ortigas Vasquez, William R. Taylor, Barbara Postolka, Pascal Schütz, Allan Maas, Matthias Woiczinski, Thomas M. Grupp, Adrian Sauer","doi":"10.1016/j.gaitpost.2023.07.187","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.187","url":null,"abstract":"Kinematic analysis involves calculating signals from optical/inertial datapoints to represent the relative movement of joint segments. The exact choice of local segment frame orientation and position has been shown to drastically influence the shape and magnitude of the associated kinematic signals, making the consistent interpretation of the underlying motion a challenge [1,2]. Despite attempts to standardise the reporting of these signals [3], a lack of consensus around joint coordinate frame definitions remains. An approach capable of accommodating different analytical methods and reconciling these differences in frame alignment, while ensuring consistent interpretations, is therefore crucial. Given sets of kinematic data, can mathematical optimisation be leveraged to achieve a consistent interpretation of the underlying movement patterns, independent of joint axis definitions? Here, we assess a REference FRame Alignment MEthod (REFRAME) on the in vivo moving-fluoroscopy-based knee kinematics of 10 healthy subjects (5 trials of stair descent each) [4]. Using three methods of defining the flexion/extension axis (cylindrical axis (CA), functional flexion axis (FFA), and transepicondylar axis (TEA)), three different femoral frames were defined for each trial, in addition to a single tibial frame [1]. Rotations of the tibia relative to the femur were calculated, alongside translational positions of the femoral origins in the tibial frame. By implementing REFRAME (as a constrained nonlinear minimisation of ab/adduction and int/external rotation root-mean-square, in addition to all translation variances), local frames were repositioned and reorientated, to derive a set of \"REFRAMEd\" signals. Fig. 1 - Knee kinematics (rotations[°]: tibia relative to femur; translations[mm]: femur relative to tibia) during a sample stair descent trial, using three different primary axes, before (raw) and after REFRAME. (CA and FFA partially covered by TEA) Download : Download high-res image (294KB)Download : Download full-size image Across all subjects and trials, before REFRAME implementation, the maximum absolute differences between kinematic signals representing the same underlying movement, but derived using different joint axis approaches, reached 1.61° for flexion/extension, 12.00° for ab/adduction, and 12.02° for int/external rotation, in addition to 2.28 mm for mediolateral, 10.60 mm for anteroposterior, and 12.23 mm for proximodistal translations. After REFRAME, maximum differences peaked at 0.78°, 0.08° and 0.08° for flexion/extension, ab/adduction and int/external rotation, respectively; For translations, values peaked at 0.24 mm, 0.10 mm and 0.13 mm in the mediolateral, anteroposterior and proximodistal directions. Moreover, the three signals converged after REFRAME optimisation (Fig1). For each underlying movement pattern, the analysis approach (method of axis definition) affected the characteristics of the kinematic signals. By implementing REFRAME, tibi","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135298854","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.2023.07.268
Saskia Wijnands, Lianne Grin, Lianne van Dijk, Arnold Besselaar, Marieke van der Steen, Benedicte Vanwanseele
Idiopathic clubfoot patients show deviations in their gait patterns and other motor activities [1–4]. One of the most challenging motor activities for clubfoot patients is hopping on one leg [4–6]. Difficulty with one-leg-hopping might result from limitations in ankle mobility and plantarflexor force production in clubfoot patients [7]. This hypothesis has however not yet been investigated with detailed three-dimensional motion analysis. What are the differences in ankle power and mobility during walking and one-leg-hopping in clubfoot patients and typically developing children of 5-to-9 years old? Motion analysis was performed in 14 typically developing children (TDC) and 15 Ponseti- treated clubfoot patients of 5-to-9-year-old. Motion analysis during walking and one-leg-hopping was performed using an extended Helen-Hayes model. Spatiotemporal, kinematic, and kinetic data was collected using two integrated force plates (AMTI OR6-7) and four cameras (Codamotion CX1). For clubfoot patients, data from the most affected leg and for TDC, data from the preferred leg was used for further processing. Stride and hop length were calculated based on heel marker displacement, which was divided by stride and hop time to provide velocity. Average group data was computed for TDC and clubfoot patients, and compared using Mann-Withney U tests (p<0.05). Data from one clubfoot patient was excluded from the data analysis of one-leg-hopping, as the patient was unable to perform consecutive hops. No differences were found in spatiotemporal, kinematic, and kinetic parameters during walking between TDC and clubfoot patients (Table 1). During one-leg-hopping, however, differences were found between clubfoot patients and TDC (Table 1). Clubfoot patients showed lower peak ankle power generation (4.25 ± 1.46 W/kg) and absorption (4.65 ± 2.47 W/kg). Furthermore, clubfoot patients showed a lower peak ankle moment (1.60 ± 0.49 N/kg) and a lower velocity during one-leg-hopping. Also, a trend where clubfoot patients showed a smaller hop length was observed (p = 0.085). No differences were found in ankle range of motion during hopping.Download : Download high-res image (164KB)Download : Download full-size image During one-leg-hopping, clubfoot patients absorbed and generated less power at the ankle joint when compared to TDC. These results might indicate that clubfoot patients have a less effective stretch-shortening mechanism of the plantarflexor muscles. This could be due to different elastic properties of the muscle complex, inherent to their pathology [8]. Subsequently, there might be less stored energy that contributes to the ankle power generation. Additionally, the lower ankle moment might indicate that the force-generating capacity of clubfoot patients might be lower, resulting in a lower ankle power generation. This might have resulted in the lower hopping velocity that was seen in clubfoot patients. These results provide insight in the problems clubfoot patients have d
{"title":"Idiopathic clubfoot patients produce less ankle power during hopping when compared to typically developing children","authors":"Saskia Wijnands, Lianne Grin, Lianne van Dijk, Arnold Besselaar, Marieke van der Steen, Benedicte Vanwanseele","doi":"10.1016/j.gaitpost.2023.07.268","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.268","url":null,"abstract":"Idiopathic clubfoot patients show deviations in their gait patterns and other motor activities [1–4]. One of the most challenging motor activities for clubfoot patients is hopping on one leg [4–6]. Difficulty with one-leg-hopping might result from limitations in ankle mobility and plantarflexor force production in clubfoot patients [7]. This hypothesis has however not yet been investigated with detailed three-dimensional motion analysis. What are the differences in ankle power and mobility during walking and one-leg-hopping in clubfoot patients and typically developing children of 5-to-9 years old? Motion analysis was performed in 14 typically developing children (TDC) and 15 Ponseti- treated clubfoot patients of 5-to-9-year-old. Motion analysis during walking and one-leg-hopping was performed using an extended Helen-Hayes model. Spatiotemporal, kinematic, and kinetic data was collected using two integrated force plates (AMTI OR6-7) and four cameras (Codamotion CX1). For clubfoot patients, data from the most affected leg and for TDC, data from the preferred leg was used for further processing. Stride and hop length were calculated based on heel marker displacement, which was divided by stride and hop time to provide velocity. Average group data was computed for TDC and clubfoot patients, and compared using Mann-Withney U tests (p<0.05). Data from one clubfoot patient was excluded from the data analysis of one-leg-hopping, as the patient was unable to perform consecutive hops. No differences were found in spatiotemporal, kinematic, and kinetic parameters during walking between TDC and clubfoot patients (Table 1). During one-leg-hopping, however, differences were found between clubfoot patients and TDC (Table 1). Clubfoot patients showed lower peak ankle power generation (4.25 ± 1.46 W/kg) and absorption (4.65 ± 2.47 W/kg). Furthermore, clubfoot patients showed a lower peak ankle moment (1.60 ± 0.49 N/kg) and a lower velocity during one-leg-hopping. Also, a trend where clubfoot patients showed a smaller hop length was observed (p = 0.085). No differences were found in ankle range of motion during hopping.Download : Download high-res image (164KB)Download : Download full-size image During one-leg-hopping, clubfoot patients absorbed and generated less power at the ankle joint when compared to TDC. These results might indicate that clubfoot patients have a less effective stretch-shortening mechanism of the plantarflexor muscles. This could be due to different elastic properties of the muscle complex, inherent to their pathology [8]. Subsequently, there might be less stored energy that contributes to the ankle power generation. Additionally, the lower ankle moment might indicate that the force-generating capacity of clubfoot patients might be lower, resulting in a lower ankle power generation. This might have resulted in the lower hopping velocity that was seen in clubfoot patients. These results provide insight in the problems clubfoot patients have d","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135299041","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.2023.07.199
Harri Piitulainen, Maria Sukanen, Taija Finni, Francesco Cenni
Children with cerebral palsy (CP) have various motor impairments, but less is known about their possible proprioceptive deficits, and role of proprioception in the motor impairments1. There are no prior studies quantifying the proprioceptive-perception threshold in CP, but detection of passive movement event has either been intact2 or impaired3, predominantly in their more affected arm4. Joint-position replication performance has also been impaired in the more affected arm5 and bilaterally in the lower limbs6 in CP. To quantify proprioceptive-perception threshold for the ankle joint in adolescents with CP and their healthy peers and examine the association to standing balance performance. We recruited 12 participants with CP (age 16 ± 3.2 y, 11–26 y, 4 females, GMFCS I/III: 8/4) and 12 healthy peers (16.8 ± 4.8 y, 12–26 y, 3 females) after giving informed consent. The proprioceptive-perception threshold was quantified as ability to perceive light rotations of their more affected ankle joint in dorsiflexed position using a silent ankle-movement actuator (Fig. 1). In healthy peers, the tested left/right ankle was randomized. The participant pressed a response button when perceiving brief 2-s plantar flexions delivered every 4–12 s. Adaptive-test algorithm modified the angular velocity (<5 °/s) based on individual performance. The threshold was defined as the slowest angular velocity with >50% correctly perceived stimuli among ~30 rotations. The test was repeated twice to assess test-retest reproducibility. Postural sway using pressure plate recording was quantified in standing posture with hands held on hips in eyes open and closed conditions (60 s each). Fig. 1.Download : Download high-res image (215KB)Download : Download full-size image Proprioceptive-perception threshold was ~0–2.5 fold higher in participants with CP (mean ± SD 1.00 ± 0.39 °/s) compared to healthy peers (0.67 ± 0.13 °/s, p = 0.007, Mann-Whitney test). Test-retest reproducibility was excellent (ICC 0.90). No significant differences were detected between the groups in postural sway. However, the proprioceptive-perception threshold was correlated to the postural sway during eyes open (r = 0.645, p < 0.001, Spearman’s rho) and closed (r = 0.690, p < 0.001) tasks. Our results showed that the proprioceptive perception is impaired in CP. Incidence of marked proprioceptive impairment was 33% in our limited CP sample. These particular patients had ~2-fold higher threshold compared to healthy peers, their GMFCS was predominantly 3 (one with score 1) and showed the weakest postural balance. These results indicate that impaired proprioception may partially explain their motor impairments. Thus, our novel test may have high diagnostic value when planning and monitoring individualized rehabilitation in CP. The test directly quantifies perception, that is essentially a cortical process, and thus may provide highly relevant information when investigating patients with cortical lesions or defici
脑瘫患儿存在多种运动障碍，但对其本体感觉障碍的可能性及本体感觉在运动障碍中的作用尚不清楚。目前还没有研究量化CP的本体感觉-知觉阈值，但被动运动事件的检测要么完好无损，要么受损，主要是在他们更受影响的手臂上。在CP患者中，受影响更严重的手臂(5)和双侧下肢(6)的关节位置复制能力也受到损害。量化患有CP的青少年及其健康同龄人的踝关节本体感觉知觉阈值，并检查其与站立平衡能力的关系。在知情同意后，我们招募了12名CP患者(16±3.2岁，11-26岁，4名女性，GMFCS I/III: 8/4)和12名健康同伴(16.8±4.8岁，12 - 26岁，3名女性)。本体感觉感知阈值被量化为使用无声踝关节运动致动器感知背屈姿势下受影响更大的踝关节的轻微旋转的能力(图1)。在健康的同龄人中，被测试的左/右踝关节是随机的。参与者在感受到每4-12秒短暂的2秒足底屈曲时按下响应按钮。自适应测试算法修正了角速度(在~30个旋转中正确感知刺激的50%)。试验重复两次以评估试验-再试验的再现性。采用压力板记录在睁眼和闭眼条件下双手叉腰站立时的体位摇摆(各60秒)。图1所示。CP患者本体感觉阈值(平均±SD 1.00±0.39°/s)比健康同龄人(0.67±0.13°/s, p = 0.007, Mann-Whitney检验)高~ 0-2.5倍。复测重现性极好(ICC 0.90)。两组之间的体位摇摆无显著差异。然而，本体感觉知觉阈值与睁眼(r = 0.645, p < 0.001, Spearman’s rho)和闭眼(r = 0.690, p < 0.001)任务时的体位摇摆相关。我们的研究结果表明，CP患者的本体感觉受损。在我们有限的CP样本中，明显的本体感觉受损的发生率为33%。这些特殊患者的阈值比健康同龄人高约2倍，他们的GMFCS以3分为主(1分)，并表现出最弱的姿势平衡。这些结果表明，本体感觉受损可能部分解释了他们的运动障碍。因此，我们的新测试在计划和监测CP个体化康复时可能具有很高的诊断价值。该测试直接量化感知，这本质上是一个皮质过程，因此在调查皮质病变或缺陷患者时可能提供高度相关的信息。对于长期监测而言，出色的测试-再测试可重复性也令人鼓舞。
{"title":"Proprioceptive-perception threshold is impaired in cerebral palsy and is associated with worse balance performance","authors":"Harri Piitulainen, Maria Sukanen, Taija Finni, Francesco Cenni","doi":"10.1016/j.gaitpost.2023.07.199","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.199","url":null,"abstract":"Children with cerebral palsy (CP) have various motor impairments, but less is known about their possible proprioceptive deficits, and role of proprioception in the motor impairments1. There are no prior studies quantifying the proprioceptive-perception threshold in CP, but detection of passive movement event has either been intact2 or impaired3, predominantly in their more affected arm4. Joint-position replication performance has also been impaired in the more affected arm5 and bilaterally in the lower limbs6 in CP. To quantify proprioceptive-perception threshold for the ankle joint in adolescents with CP and their healthy peers and examine the association to standing balance performance. We recruited 12 participants with CP (age 16 ± 3.2 y, 11–26 y, 4 females, GMFCS I/III: 8/4) and 12 healthy peers (16.8 ± 4.8 y, 12–26 y, 3 females) after giving informed consent. The proprioceptive-perception threshold was quantified as ability to perceive light rotations of their more affected ankle joint in dorsiflexed position using a silent ankle-movement actuator (Fig. 1). In healthy peers, the tested left/right ankle was randomized. The participant pressed a response button when perceiving brief 2-s plantar flexions delivered every 4–12 s. Adaptive-test algorithm modified the angular velocity (<5 °/s) based on individual performance. The threshold was defined as the slowest angular velocity with >50% correctly perceived stimuli among ~30 rotations. The test was repeated twice to assess test-retest reproducibility. Postural sway using pressure plate recording was quantified in standing posture with hands held on hips in eyes open and closed conditions (60 s each). Fig. 1.Download : Download high-res image (215KB)Download : Download full-size image Proprioceptive-perception threshold was ~0–2.5 fold higher in participants with CP (mean ± SD 1.00 ± 0.39 °/s) compared to healthy peers (0.67 ± 0.13 °/s, p = 0.007, Mann-Whitney test). Test-retest reproducibility was excellent (ICC 0.90). No significant differences were detected between the groups in postural sway. However, the proprioceptive-perception threshold was correlated to the postural sway during eyes open (r = 0.645, p < 0.001, Spearman’s rho) and closed (r = 0.690, p < 0.001) tasks. Our results showed that the proprioceptive perception is impaired in CP. Incidence of marked proprioceptive impairment was 33% in our limited CP sample. These particular patients had ~2-fold higher threshold compared to healthy peers, their GMFCS was predominantly 3 (one with score 1) and showed the weakest postural balance. These results indicate that impaired proprioception may partially explain their motor impairments. Thus, our novel test may have high diagnostic value when planning and monitoring individualized rehabilitation in CP. The test directly quantifies perception, that is essentially a cortical process, and thus may provide highly relevant information when investigating patients with cortical lesions or defici","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297861","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.2023.07.241
Thijs Tankink, Han Houdijk, Raffaella Carloni, Juha- M. Hijmans
Changing the apex position and angle of a rocker shoe can modify the gear ratio around the ankle [1], base of support and roll-over direction [2], and therefore affect different gait related objectives (e.g. metabolic cost, mechanical load or stability). Optimal apex parameters for these different objectives are dependent on individual musculoskeletal characteristics and the voluntary, yet unpredictable, gait adaptations of the user in response to changes in apex parameters [3]. A method to overcome these challenges is human-in-the-loop optimization [4], in which the human is included ‘in vivo’ in the control loop and apex parameters are systematically varied using an optimization algorithm in response to measured performances to optimize human performance. However, the outcome of this process might depend on the selected optimization objective, but knowledge about how different cost functions affect this outcome is lacking. The aim of the study is to investigate whether human-in-the-loop optimization via different cost functions, i.e. metabolic cost, external mechanical work, and gait stability, affects the optimal apex position and angle for individuals during walking. Seven healthy participants underwent three different optimization protocols while walking on a treadmill. With the different optimization protocols, we aimed to minimize (1) metabolic cost of walking, (2) negative collision work on the centre of mass, and (3) step distance (vector step length and step width) variability (as measure of gait stability) by optimizing the rocker profile of experimental shoes, with tuneable apex position and angle, using an evolutionary optimization algorithm [5]. Optimal shoe settings for the different cost functions and standard settings were compared. Optimized apex lines for the different cost functions are presented in Fig. 1. The optimized apex positions (percentage total shoe length) were located more distal compared to the standard position (64.0%) and significant difference between cost functions was approached (metabolic cost: 70.3±4.3%, collision work: 76.5±12.4%, step distance variability: 73.4±4.4%, p=0.05). The optimized apex angles tended to be larger compared to the standard angle (88.0˚), but were quite variable among participants (metabolic cost: 118.0±16.0˚, collision work: 93.2±33.5˚, and step distance variability: 103.0±27.7˚). Consequently, significant differences in apex angle between cost functions were not found.Download : Download high-res image (108KB)Download : Download full-size image Cost function tended to have an effect on optimal apex parameters. Optimizing for metabolic cost tended to result in a more proximal apex position compared to the other cost functions, while high variability in optimal angles between participants were found for most cost functions. The variety in optimal apex parameters between participants emphasizes the importance of an individualized approach. Our next step is to investigate how these opti
{"title":"Human-in-the-loop optimization of rocker shoes via different cost functions during walking","authors":"Thijs Tankink, Han Houdijk, Raffaella Carloni, Juha- M. Hijmans","doi":"10.1016/j.gaitpost.2023.07.241","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.241","url":null,"abstract":"Changing the apex position and angle of a rocker shoe can modify the gear ratio around the ankle [1], base of support and roll-over direction [2], and therefore affect different gait related objectives (e.g. metabolic cost, mechanical load or stability). Optimal apex parameters for these different objectives are dependent on individual musculoskeletal characteristics and the voluntary, yet unpredictable, gait adaptations of the user in response to changes in apex parameters [3]. A method to overcome these challenges is human-in-the-loop optimization [4], in which the human is included ‘in vivo’ in the control loop and apex parameters are systematically varied using an optimization algorithm in response to measured performances to optimize human performance. However, the outcome of this process might depend on the selected optimization objective, but knowledge about how different cost functions affect this outcome is lacking. The aim of the study is to investigate whether human-in-the-loop optimization via different cost functions, i.e. metabolic cost, external mechanical work, and gait stability, affects the optimal apex position and angle for individuals during walking. Seven healthy participants underwent three different optimization protocols while walking on a treadmill. With the different optimization protocols, we aimed to minimize (1) metabolic cost of walking, (2) negative collision work on the centre of mass, and (3) step distance (vector step length and step width) variability (as measure of gait stability) by optimizing the rocker profile of experimental shoes, with tuneable apex position and angle, using an evolutionary optimization algorithm [5]. Optimal shoe settings for the different cost functions and standard settings were compared. Optimized apex lines for the different cost functions are presented in Fig. 1. The optimized apex positions (percentage total shoe length) were located more distal compared to the standard position (64.0%) and significant difference between cost functions was approached (metabolic cost: 70.3±4.3%, collision work: 76.5±12.4%, step distance variability: 73.4±4.4%, p=0.05). The optimized apex angles tended to be larger compared to the standard angle (88.0˚), but were quite variable among participants (metabolic cost: 118.0±16.0˚, collision work: 93.2±33.5˚, and step distance variability: 103.0±27.7˚). Consequently, significant differences in apex angle between cost functions were not found.Download : Download high-res image (108KB)Download : Download full-size image Cost function tended to have an effect on optimal apex parameters. Optimizing for metabolic cost tended to result in a more proximal apex position compared to the other cost functions, while high variability in optimal angles between participants were found for most cost functions. The variety in optimal apex parameters between participants emphasizes the importance of an individualized approach. Our next step is to investigate how these opti","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297874","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.2023.07.257
Ines Vandekerckhove, Dhruv Gupta, Lars D'Hondt, Marleen Van den Hauwe, Anja Van Campenhout, Liesbeth De Waele, Nathalie Goemans, Kaat Desloovere, Friedl De Groote
Predictive simulations of gait can improve our understanding of how underlying impairments contribute to gait pathology in children with Duchenne muscular dystrophy (DMD). This is essential to make progress in gait rehabilitation and orthotic treatments aiming at prolonging ambulation in DMD. Yet, there is still a need to evaluate if predictive simulations can capture the key features of DMD gait. Can we simulate DMD gait pathology? 3D gait analysis was collected in three boys with DMD, who were situated at different stages of the disease progression. Muscle weakness was measured using a fixed dynamometer [1]. Muscle stiffness and contractures were assessed using goniometry and clinical scales. For each subject, a generic musculoskeletal model [2] was scaled to the subject’s anthropometry based on marker data. The maximal isometric muscle forces (MIMF), joint stiffness, properties of the foot-ground contact model, weights of the cost function and imposed walking speed were scaled to reflect the child’s dimensions. Subject-specific muscle weakness was modeled by decreasing active MIMF based on the individual’s weakness scores. Muscle stiffness and contractures were modeled by shifting and increasing the steepness of the passive force-length relationship of the assessed muscles. Gait was predicted by minimizing a cost function while imposing the gait speed and periodicity of the gait pattern (without relying on motion capture data) [3]. For each subject, simulations were performed based on four models: (1) reference (child’s dimensions), (2) weakness, (3) stiffness, and (4) combination of weakness and stiffness. Root mean squared error (RMSE) between the simulated kinematics and the mean experimental kinematics was calculated. Fig. 1 shows the experimental data and simulation results of DMD1 (10.6years), DMD2 (15.6years) and DMD3 (11.1years). The predicted gait patterns are closer to the experimental data when modeling weakness and stiffness. The sum of RMSEs between predicted and experimental kinematics decreased from 40.9 to 36.2 between model1 to model4 for DMD1, from 47.5 to 30.3 for DMD2 and from 48.2 to 39.2 for DMD3. The increasing gait pathology over the three cases with increasing severity of muscle impairments, was also reflected in the predictive simulations.Download : Download high-res image (273KB)Download : Download full-size image Several key features of the DMD gait, such as tiptoeing gait, increased anterior pelvic tilt, reduced knee flexion during stance and drop foot in swing, were reasonably captured in the predictive simulations. However, the exaggerated lumbar extension was not fully captured. Differences between simulations and experiments might be due to the use of a simple trunk model. In addition, foot deformities were not yet modeled. In the future, we will further refine the model and personalization workflow by using data from instrumented stiffness assessment. Nevertheless, the current results show the potential of pre
{"title":"Predictive simulations of common gait features in children with Duchenne muscular dystrophy","authors":"Ines Vandekerckhove, Dhruv Gupta, Lars D'Hondt, Marleen Van den Hauwe, Anja Van Campenhout, Liesbeth De Waele, Nathalie Goemans, Kaat Desloovere, Friedl De Groote","doi":"10.1016/j.gaitpost.2023.07.257","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.257","url":null,"abstract":"Predictive simulations of gait can improve our understanding of how underlying impairments contribute to gait pathology in children with Duchenne muscular dystrophy (DMD). This is essential to make progress in gait rehabilitation and orthotic treatments aiming at prolonging ambulation in DMD. Yet, there is still a need to evaluate if predictive simulations can capture the key features of DMD gait. Can we simulate DMD gait pathology? 3D gait analysis was collected in three boys with DMD, who were situated at different stages of the disease progression. Muscle weakness was measured using a fixed dynamometer [1]. Muscle stiffness and contractures were assessed using goniometry and clinical scales. For each subject, a generic musculoskeletal model [2] was scaled to the subject’s anthropometry based on marker data. The maximal isometric muscle forces (MIMF), joint stiffness, properties of the foot-ground contact model, weights of the cost function and imposed walking speed were scaled to reflect the child’s dimensions. Subject-specific muscle weakness was modeled by decreasing active MIMF based on the individual’s weakness scores. Muscle stiffness and contractures were modeled by shifting and increasing the steepness of the passive force-length relationship of the assessed muscles. Gait was predicted by minimizing a cost function while imposing the gait speed and periodicity of the gait pattern (without relying on motion capture data) [3]. For each subject, simulations were performed based on four models: (1) reference (child’s dimensions), (2) weakness, (3) stiffness, and (4) combination of weakness and stiffness. Root mean squared error (RMSE) between the simulated kinematics and the mean experimental kinematics was calculated. Fig. 1 shows the experimental data and simulation results of DMD1 (10.6years), DMD2 (15.6years) and DMD3 (11.1years). The predicted gait patterns are closer to the experimental data when modeling weakness and stiffness. The sum of RMSEs between predicted and experimental kinematics decreased from 40.9 to 36.2 between model1 to model4 for DMD1, from 47.5 to 30.3 for DMD2 and from 48.2 to 39.2 for DMD3. The increasing gait pathology over the three cases with increasing severity of muscle impairments, was also reflected in the predictive simulations.Download : Download high-res image (273KB)Download : Download full-size image Several key features of the DMD gait, such as tiptoeing gait, increased anterior pelvic tilt, reduced knee flexion during stance and drop foot in swing, were reasonably captured in the predictive simulations. However, the exaggerated lumbar extension was not fully captured. Differences between simulations and experiments might be due to the use of a simple trunk model. In addition, foot deformities were not yet modeled. In the future, we will further refine the model and personalization workflow by using data from instrumented stiffness assessment. Nevertheless, the current results show the potential of pre","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135297877","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}
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