Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.154
Arne Defour, Daan De Vlieger, Robbe De Baets, Kristine Oostra, Dirk Cambier, Hanne Maebe, Koen Matthys, Pieter Meyns, Anke Van Bladel
Visual gait assessment is a cost-effective and more feasible way to evaluate post-stroke gait deviations in a clinical setting. Most observation scales focus on the lower limb during walking and therefore contain little information concerning the upper limb1,2. However, the upper limbs also contributes to various aspects of functional ambulation3. Therefore, an observation scale was developed to assess the arm swing during walking in persons after stroke. The aim of this study is to examine the inter- and intra-tester reliability and concurrent validity of the upper limb observation scale using two-dimensional (2D) videos of the persons after stroke during walking. Twenty-four persons after stroke (14 female, 10 male; age 54.29 ± 10.9 years, 5.50 ± 29.6 months post-stroke) underwent clinical tests and walked along a 10-meter walkway at self-selected speed. Walking was videotaped (frontal and sagittal view) to score the upper limb observation scale (Fig. 1) afterwards by three different researchers who were blinded from one another. One researcher scored this scale twice with an interval of two weeks. To assess the inter- and intra-tester reliability, intraclass correlation coefficients (ICC), spearman rank correlations (r) and Cronbach’s alpha’s were calculated. Additionally, 3D data, collected from four participants using the Gait Real-time Analysis Interactive Lab (GRAIL, Motek), was compared to the scores on the U.L.O.H.S.W. to validate the 2D observation of the upper limb during walking.Download : Download high-res image (265KB)Download : Download full-size image Inter-tester reliability for the different items varied with ICC’s between 0.254 and 0.885, correlation coefficients (r) between 0.410 and 1.000 (p<0.05, p<0.01) and Cronbach’s alpha between 0.504 and 0.958. For the intra-tester reliability, the ICC’s ranged from 0.594 to 0.957, the correlation coefficients (r) from 0.585 to 0.945 (p<0.01) and the Cronbach’s alpha from 0.738 to 0.978. Scoring the items concerning the more distal parts of the upper limb and the arm swing itself tended to be more reliable compared to the more proximal parts. Percentages of agreement, calculated between the scores on the observation scale and the 3D data to investigate concurrent validity, ranged from 29% (elbow flexion item) to 83% (shoulder abduction item). This is the first study to investigate the inter- and intra-tester reliability and the validity of an observational scale concerning the hemiplegic arm swing during gait. The tool is not yet sufficiently validated as an observation tool of the arm swing during walking in persons after stroke. Scoring the proximal movements of the upper limb appeared to be least reliable. Further research with a larger study population and a renewed version of this scale should provide more information concerning its clinical usability.
{"title":"Reliability and validity of a new observation scale to evaluate the upper limb during gait in persons after stroke","authors":"Arne Defour, Daan De Vlieger, Robbe De Baets, Kristine Oostra, Dirk Cambier, Hanne Maebe, Koen Matthys, Pieter Meyns, Anke Van Bladel","doi":"10.1016/j.gaitpost.2023.07.154","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.154","url":null,"abstract":"Visual gait assessment is a cost-effective and more feasible way to evaluate post-stroke gait deviations in a clinical setting. Most observation scales focus on the lower limb during walking and therefore contain little information concerning the upper limb1,2. However, the upper limbs also contributes to various aspects of functional ambulation3. Therefore, an observation scale was developed to assess the arm swing during walking in persons after stroke. The aim of this study is to examine the inter- and intra-tester reliability and concurrent validity of the upper limb observation scale using two-dimensional (2D) videos of the persons after stroke during walking. Twenty-four persons after stroke (14 female, 10 male; age 54.29 ± 10.9 years, 5.50 ± 29.6 months post-stroke) underwent clinical tests and walked along a 10-meter walkway at self-selected speed. Walking was videotaped (frontal and sagittal view) to score the upper limb observation scale (Fig. 1) afterwards by three different researchers who were blinded from one another. One researcher scored this scale twice with an interval of two weeks. To assess the inter- and intra-tester reliability, intraclass correlation coefficients (ICC), spearman rank correlations (r) and Cronbach’s alpha’s were calculated. Additionally, 3D data, collected from four participants using the Gait Real-time Analysis Interactive Lab (GRAIL, Motek), was compared to the scores on the U.L.O.H.S.W. to validate the 2D observation of the upper limb during walking.Download : Download high-res image (265KB)Download : Download full-size image Inter-tester reliability for the different items varied with ICC’s between 0.254 and 0.885, correlation coefficients (r) between 0.410 and 1.000 (p<0.05, p<0.01) and Cronbach’s alpha between 0.504 and 0.958. For the intra-tester reliability, the ICC’s ranged from 0.594 to 0.957, the correlation coefficients (r) from 0.585 to 0.945 (p<0.01) and the Cronbach’s alpha from 0.738 to 0.978. Scoring the items concerning the more distal parts of the upper limb and the arm swing itself tended to be more reliable compared to the more proximal parts. Percentages of agreement, calculated between the scores on the observation scale and the 3D data to investigate concurrent validity, ranged from 29% (elbow flexion item) to 83% (shoulder abduction item). This is the first study to investigate the inter- and intra-tester reliability and the validity of an observational scale concerning the hemiplegic arm swing during gait. The tool is not yet sufficiently validated as an observation tool of the arm swing during walking in persons after stroke. Scoring the proximal movements of the upper limb appeared to be least reliable. Further research with a larger study population and a renewed version of this scale should provide more information concerning its clinical usability.","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":"135298555","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}
Hallux valgus deformity (HV), which is among the most common foot deformities in adulthood, has been associated with impaired quality of life and function [1–4]. On the other hand, not only the presence of HV but also unilateral or bilateral involvement and whether it is painful or not may affect self-reported and performance-based measures [1,4]. Do foot function, physical performance, and quality of life differ between women with and without symptomatic bilateral HV? Forty-four women with bilateral HV (average HV angle for dominant foot=27.98±9.51° and for non-dominant foot=29.48±9.12°, average age=37.68±12.1 years, average BMI=25.30±5.17 kg/m2) and forty-three controls (average age=37.47±10.35 years, average BMI=24.87±4.52 kg/m2) were included. The HV angles of women presenting to orthopedic outpatient clinics with HV complaints were calculated from weight-bearing dorsoplantar radiographs. Women having HV angles equal to or greater than 15° in both feet were included in the HV group, also severity of HV was classified according to the HV angle of the dominant foot as mild (15-20°), moderate (21-39°), and severe (equal or greater than 40°). Volunteer women classified using the Manchester scale as normal were included in the control group. Foot pain and foot function were assessed using the Foot Function Index (FFI) and American Orthopaedic Foot and Ankle Society Hallux Metatarsophalangeal Interphalangeal Joints Scale (AOFAS Hallux MTF-IP). To assess physical performance, the time required to complete the following tasks was measured: (1) Walking 10 meter-walkway, (2) ascending ten stairs as fast as possible, and (3) descending ten stairs as fast as possible. Also, single-limb stance time with eyes-open was measured for both limbs. The Manchester-Oxford Foot Questionnaire was used to assess health-related quality of life. The Mann-Whitney U test was used to compare women with and without HV, also the Kruskal-Wallis test with Dunn’s post-hoc test was used to compare women with mild HV (n=16), moderate HV (n=19), and severe HV (n=9). Women with HV had poorer foot function, physical performance, and quality of life than those without HV according to the subscores and total scores of all assessment tools (p<0.05). Women with mild HV had less foot pain according to AOFAS Hallux MTF-IP and better foot function according to both AOFAS Hallux MTF-IP and FFI than those with severe HV (p<0.05). Furthermore, women with mild HV also had better foot function according to AOFAS Hallux MTF-IP than those with moderate HV (p<0.05). No difference was found between women with moderate and severe HV (p>0.05). Women with symptomatic bilateral HV had poorer self-reported foot function, self-reported quality of life, and physical performance. Furthermore, self-reported foot function differed between women with mild HV and moderate to severe HV, and the mild HV group had better foot function than the moderate HV and severe HV groups.
{"title":"Comparison of foot function, physical performance, and quality of life between women with and without symptomatic bilateral hallux valgus deformity","authors":"Busra Sacli, Sevtap Gunay Ucurum, Müge Kırmızı, Gokhan Cansabuncu","doi":"10.1016/j.gaitpost.2023.07.125","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.125","url":null,"abstract":"Hallux valgus deformity (HV), which is among the most common foot deformities in adulthood, has been associated with impaired quality of life and function [1–4]. On the other hand, not only the presence of HV but also unilateral or bilateral involvement and whether it is painful or not may affect self-reported and performance-based measures [1,4]. Do foot function, physical performance, and quality of life differ between women with and without symptomatic bilateral HV? Forty-four women with bilateral HV (average HV angle for dominant foot=27.98±9.51° and for non-dominant foot=29.48±9.12°, average age=37.68±12.1 years, average BMI=25.30±5.17 kg/m2) and forty-three controls (average age=37.47±10.35 years, average BMI=24.87±4.52 kg/m2) were included. The HV angles of women presenting to orthopedic outpatient clinics with HV complaints were calculated from weight-bearing dorsoplantar radiographs. Women having HV angles equal to or greater than 15° in both feet were included in the HV group, also severity of HV was classified according to the HV angle of the dominant foot as mild (15-20°), moderate (21-39°), and severe (equal or greater than 40°). Volunteer women classified using the Manchester scale as normal were included in the control group. Foot pain and foot function were assessed using the Foot Function Index (FFI) and American Orthopaedic Foot and Ankle Society Hallux Metatarsophalangeal Interphalangeal Joints Scale (AOFAS Hallux MTF-IP). To assess physical performance, the time required to complete the following tasks was measured: (1) Walking 10 meter-walkway, (2) ascending ten stairs as fast as possible, and (3) descending ten stairs as fast as possible. Also, single-limb stance time with eyes-open was measured for both limbs. The Manchester-Oxford Foot Questionnaire was used to assess health-related quality of life. The Mann-Whitney U test was used to compare women with and without HV, also the Kruskal-Wallis test with Dunn’s post-hoc test was used to compare women with mild HV (n=16), moderate HV (n=19), and severe HV (n=9). Women with HV had poorer foot function, physical performance, and quality of life than those without HV according to the subscores and total scores of all assessment tools (p<0.05). Women with mild HV had less foot pain according to AOFAS Hallux MTF-IP and better foot function according to both AOFAS Hallux MTF-IP and FFI than those with severe HV (p<0.05). Furthermore, women with mild HV also had better foot function according to AOFAS Hallux MTF-IP than those with moderate HV (p<0.05). No difference was found between women with moderate and severe HV (p>0.05). Women with symptomatic bilateral HV had poorer self-reported foot function, self-reported quality of life, and physical performance. Furthermore, self-reported foot function differed between women with mild HV and moderate to severe HV, and the mild HV group had better foot function than the moderate HV and severe HV groups.","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":"135298686","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.2023.07.220
Wouter Schallig, Astrid Bieger, Melinda Witbreuk, Annemieke Buizer, Marjolein van der Krogt
Foot deformities are common in children with cerebral palsy (CP)1, but it is hard to predict how they develop. They are likely caused by a disturbed interplay of forces within the foot during gait, which can be quantified with multi-segment foot kinetics. Differences in foot joint kinetics have been shown between several foot deformity types and typically-developed feet2. These differences seem to indicate that mainly the misalignment of the foot causes further deterioration of the deformity rather than muscle actions2. Altered joint moments due to this malalignment are expected to lead to more deformation, which again results in more affected joint moments, entering a negative vicious circle. Assessing the relation between foot deformity severity and joint moments might provide support for this theory and it will allow to identify whether specific kinetic variables could serve as predictors. Is there an association between foot deformity severity and multi-segment foot kinetics in children with CP? 31 children (48 feet) with a spastic paresis (27 CP, 4 hereditary spastic paresis) were included, with a total of 6 equinovarus, 8 cavovarus, 16 planovalgus and 18 neutral feet. Additionally, 13 typically-developed (TD) feet with a normal foot posture were included. All children performed a gait analysis with the Amsterdam Foot Model3 marker set attached, while walking over a pressure plate on top of a force plate to be able to calculate the multi-segment foot kinetics4. The CP and TD children walked at 100% and 75% of comfortable speed respectively, to match their speed for further analyses. Peak foot joint moments were associated to a static measure (the foot posture index5) and a dynamic measure (the foot profile score6) of foot deformity severity, using Pearson correlations. Moderate significant correlations (r=0.60-0.65) were found between the static foot deformity score and the internal plantar flexion peak moment in the Lisfranc joint and the frontal plane peak moment in the ankle and Chopart joints (Fig. 1). For the dynamic foot deformity score, strong significant correlations (r>0.8) were present with peak plantar flexion moment for the equinovarus deformity in all joints. Low to moderate correlations (r=0.4-0.6) were found in the Chopart and Lisfranc joints for the cavovarus deformity in the sagittal and frontal plane and for the planovalgus deformity in the transverse plane. Fig. 1.Download : Download high-res image (154KB)Download : Download full-size image The significant associations between foot deformity severity and specific peak joint moments suggests that foot joint moments may play a role in the deterioration of foot deformities. Furthermore, specific joint moments per foot deformity group were identified which might have a predictive value for the progression of the deformation. However, longitudinal data is required to actually establish this predictive value. Identifying foot deformity predictors will allow for early interventio
{"title":"The predictive value of multi-segment foot kinetics in the development of foot deformities in cerebral palsy","authors":"Wouter Schallig, Astrid Bieger, Melinda Witbreuk, Annemieke Buizer, Marjolein van der Krogt","doi":"10.1016/j.gaitpost.2023.07.220","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.220","url":null,"abstract":"Foot deformities are common in children with cerebral palsy (CP)1, but it is hard to predict how they develop. They are likely caused by a disturbed interplay of forces within the foot during gait, which can be quantified with multi-segment foot kinetics. Differences in foot joint kinetics have been shown between several foot deformity types and typically-developed feet2. These differences seem to indicate that mainly the misalignment of the foot causes further deterioration of the deformity rather than muscle actions2. Altered joint moments due to this malalignment are expected to lead to more deformation, which again results in more affected joint moments, entering a negative vicious circle. Assessing the relation between foot deformity severity and joint moments might provide support for this theory and it will allow to identify whether specific kinetic variables could serve as predictors. Is there an association between foot deformity severity and multi-segment foot kinetics in children with CP? 31 children (48 feet) with a spastic paresis (27 CP, 4 hereditary spastic paresis) were included, with a total of 6 equinovarus, 8 cavovarus, 16 planovalgus and 18 neutral feet. Additionally, 13 typically-developed (TD) feet with a normal foot posture were included. All children performed a gait analysis with the Amsterdam Foot Model3 marker set attached, while walking over a pressure plate on top of a force plate to be able to calculate the multi-segment foot kinetics4. The CP and TD children walked at 100% and 75% of comfortable speed respectively, to match their speed for further analyses. Peak foot joint moments were associated to a static measure (the foot posture index5) and a dynamic measure (the foot profile score6) of foot deformity severity, using Pearson correlations. Moderate significant correlations (r=0.60-0.65) were found between the static foot deformity score and the internal plantar flexion peak moment in the Lisfranc joint and the frontal plane peak moment in the ankle and Chopart joints (Fig. 1). For the dynamic foot deformity score, strong significant correlations (r>0.8) were present with peak plantar flexion moment for the equinovarus deformity in all joints. Low to moderate correlations (r=0.4-0.6) were found in the Chopart and Lisfranc joints for the cavovarus deformity in the sagittal and frontal plane and for the planovalgus deformity in the transverse plane. Fig. 1.Download : Download high-res image (154KB)Download : Download full-size image The significant associations between foot deformity severity and specific peak joint moments suggests that foot joint moments may play a role in the deterioration of foot deformities. Furthermore, specific joint moments per foot deformity group were identified which might have a predictive value for the progression of the deformation. However, longitudinal data is required to actually establish this predictive value. Identifying foot deformity predictors will allow for early interventio","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"16 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":"135298696","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.2023.07.232
Djordje Slijepcevic, Fabian Horst, Marvin Simak, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer, Brian Horsak
Personalizing gait rehabilitation requires a comprehensive understanding of the unique gait characteristics of an individual patient, i.e., personal gait signature. Utilizing machine learning to classify individuals based on their gait can help to identify gait signatures [1]. This work exemplifies how an explainable artificial intelligence method can identify the most important input features that characterize the personal gait signature. How robust can gait signatures be identified with machine learning and how sensitive are these signatures with respect to the amount of training data per person? We utilized subsets of the AIST Gait Database 2019 [2], the GaitRec dataset [3], and the Gutenberg Gait Database [4] containing bilateral ground reaction forces (GRFs) during level walking at a self-selected speed. Eight GRF samples from each of 2,092 individuals (1,410/680 male/female, 809/1,283 health control/gait disorder, 1,355/737 shod/barefoot) were used for a gait-based person classification with a (linear) support vector machine (SVM). Two randomly selected samples from each individual served as test data. Gait signatures were identified using relevance scores obtained with layer-wise relevance propagation [5]. To assess the robustness of the identified gait signatures, we compared the relevance scores using Pearson’s correlation coefficient between step-wise reduced training data, from k=6 to k=1 training samples per individual. For the baseline setup (k=6), the SVM achieved a test classification accuracy of 99.1% with 36 out of 4184 test samples being misclassified. The results for the setups with reduced training samples are visualized in Fig. 1. Fig. 1: Overview of the experimental results.Download : Download high-res image (210KB)Download : Download full-size image A reduction of training samples per individual causes a decrease in classification accuracy (e.g., by 17.7% in the case of one training sample per individual). The results show that at least five training samples per individual are necessary to achieve a classification accuracy of approximately 99% for over 2,000 individuals. A similar effect is observed for gait signatures, which also show a slight degradation in robustness as the number of training samples decreases. In some cases, a model trained with less data per individual learns a different gait signature than a model trained with more data. In the test sample with the lowest correlation (see Fig. 1E), we observe a significant deviation in relevance for some input features. However, only 114 test samples (2.7%) are below a moderate correlation of r=0.4 [6], indicating that gait signatures are quite robust, even when using one training sample per individual. This is supported by a strong median correlation of r=0.71 [6] (and the highest correlation of r=0.96) between the gait signatures. As automatically identified gait signatures seem to be robust, this approach has the potential to serve as a basis for tailoring interven
{"title":"Towards personalized gait rehabilitation: How robustly can we identify personal gait signatures with machine learning?","authors":"Djordje Slijepcevic, Fabian Horst, Marvin Simak, Wolfgang Immanuel Schöllhorn, Matthias Zeppelzauer, Brian Horsak","doi":"10.1016/j.gaitpost.2023.07.232","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.232","url":null,"abstract":"Personalizing gait rehabilitation requires a comprehensive understanding of the unique gait characteristics of an individual patient, i.e., personal gait signature. Utilizing machine learning to classify individuals based on their gait can help to identify gait signatures [1]. This work exemplifies how an explainable artificial intelligence method can identify the most important input features that characterize the personal gait signature. How robust can gait signatures be identified with machine learning and how sensitive are these signatures with respect to the amount of training data per person? We utilized subsets of the AIST Gait Database 2019 [2], the GaitRec dataset [3], and the Gutenberg Gait Database [4] containing bilateral ground reaction forces (GRFs) during level walking at a self-selected speed. Eight GRF samples from each of 2,092 individuals (1,410/680 male/female, 809/1,283 health control/gait disorder, 1,355/737 shod/barefoot) were used for a gait-based person classification with a (linear) support vector machine (SVM). Two randomly selected samples from each individual served as test data. Gait signatures were identified using relevance scores obtained with layer-wise relevance propagation [5]. To assess the robustness of the identified gait signatures, we compared the relevance scores using Pearson’s correlation coefficient between step-wise reduced training data, from k=6 to k=1 training samples per individual. For the baseline setup (k=6), the SVM achieved a test classification accuracy of 99.1% with 36 out of 4184 test samples being misclassified. The results for the setups with reduced training samples are visualized in Fig. 1. Fig. 1: Overview of the experimental results.Download : Download high-res image (210KB)Download : Download full-size image A reduction of training samples per individual causes a decrease in classification accuracy (e.g., by 17.7% in the case of one training sample per individual). The results show that at least five training samples per individual are necessary to achieve a classification accuracy of approximately 99% for over 2,000 individuals. A similar effect is observed for gait signatures, which also show a slight degradation in robustness as the number of training samples decreases. In some cases, a model trained with less data per individual learns a different gait signature than a model trained with more data. In the test sample with the lowest correlation (see Fig. 1E), we observe a significant deviation in relevance for some input features. However, only 114 test samples (2.7%) are below a moderate correlation of r=0.4 [6], indicating that gait signatures are quite robust, even when using one training sample per individual. This is supported by a strong median correlation of r=0.71 [6] (and the highest correlation of r=0.96) between the gait signatures. As automatically identified gait signatures seem to be robust, this approach has the potential to serve as a basis for tailoring interven","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":"135298697","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.2023.07.107
Stephanie Huysmans, Rachel Senden, Eva Jacobs, Paul Willems, Rik Marcellis, Mark van den Boogaart, Kenneth Meijer, Paul Willems
Patients with Adult Spinal Deformity(ASD) have distorted spinal alignment altering their gait pattern [1–3]. However, the deformity may differ between patients previously known with adolescent idiopathic scoliosis(AIS) and ‘de novo’ or degenerative lumbar scoliosis. AIS patients often have normal sagittal alignment on static radiographs, but display postural malalignment in frontal plane [4], while DSc patients experience sagittal malalignment [2,3,5]. The purpose of this project is to compare spatiotemporal parameters(SPT) and 3D trunk kinematic waveforms of both adult patients with symptomatic idiopathic scoliosis(ISc) and adult ‘de novo’ scoliosis(DSc) patients with controls during walking. Are SPT and 3D trunk kinematic waveforms of ISc and DSc patients different from matched controls during walking? ASD patients(n=50) scheduled for long-segment spinal fusion surgery were included and divided into an ISc(n=24, median(Q1-Q3) age 20(19-27) years, leg length 0.9(0.85-0.93) m, BMI 23.1(20.7-26.7) kg/m2), and a DSc(n=26, median(Q1-Q3) age 60.5(55-66) years, leg length 0.89(0.83-0.93) m, BMI 28.1(25.1-30.1) kg/m2) group. Each patient was matched to an age-, gender-, weight- and height asymptomatic healthy control. Gait was measured while walking at comfortable speed on an instrumented treadmill with 3D motion capture system surrounded by a 180° projection screen displaying a virtual environment. The human body lower limb model with trunk markers was used[6]. 250 steps were recorded and averages over all measured steps per individual were used for analyses. SPT were presented as median(interquartile range). Independent t-test or Mann-Whitney U test was used to compare the patients with their control group. Statistical Parametric Mapping(independent t-test) was used to compare 3D trunk kinematics between the groups. Patients with ISc walked with comparable SPT to controls, whereas patients with DSc walked significantly slower(0.99(0.73-1.14) vs 1.30(1.13-1.39) m/s) with lower cadence (108.4(101.8-113.3) vs 118.3 (111.3-122.8) steps/min), smaller (1.08(0.84-1.28) vs 1.29(1.21-1.37) m) but wider steps (20(18-24) vs 16(14-20) cm), and increased stride- (1.11(1.07-1.18) vs 1.02(0.98-1.08) s), stance- (0.70(0.66-0.76) vs 0.61(0.58-0.66) s), and double support time (0.14(0.12-0.17) vs 0.11(0.09-0.13) s). Compared to their matched controls, DSc patients showed significantly increased anterior trunk tilt during the whole gait cycle, while ISc patients walked with significantly increased trunk lateroflexion during stance(0-52% gait cycle; Fig. 1). Both DSc and ISc patients had comparable trunk rotation compared to controls(Fig. 1). Fig. 1. 3D Trunk kinematic waveforms. Patients in green andcontrols in grey. Statistical Parametric Mapping statistics are presented.Download : Download high-res image (137KB)Download : Download full-size image ISc and DSc patients show different gait alterations compared to controls. ISc patients show decreased trunk lateroflexion
{"title":"Alteration of gait characteristics in patients with adult spinal deformity","authors":"Stephanie Huysmans, Rachel Senden, Eva Jacobs, Paul Willems, Rik Marcellis, Mark van den Boogaart, Kenneth Meijer, Paul Willems","doi":"10.1016/j.gaitpost.2023.07.107","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.107","url":null,"abstract":"Patients with Adult Spinal Deformity(ASD) have distorted spinal alignment altering their gait pattern [1–3]. However, the deformity may differ between patients previously known with adolescent idiopathic scoliosis(AIS) and ‘de novo’ or degenerative lumbar scoliosis. AIS patients often have normal sagittal alignment on static radiographs, but display postural malalignment in frontal plane [4], while DSc patients experience sagittal malalignment [2,3,5]. The purpose of this project is to compare spatiotemporal parameters(SPT) and 3D trunk kinematic waveforms of both adult patients with symptomatic idiopathic scoliosis(ISc) and adult ‘de novo’ scoliosis(DSc) patients with controls during walking. Are SPT and 3D trunk kinematic waveforms of ISc and DSc patients different from matched controls during walking? ASD patients(n=50) scheduled for long-segment spinal fusion surgery were included and divided into an ISc(n=24, median(Q1-Q3) age 20(19-27) years, leg length 0.9(0.85-0.93) m, BMI 23.1(20.7-26.7) kg/m2), and a DSc(n=26, median(Q1-Q3) age 60.5(55-66) years, leg length 0.89(0.83-0.93) m, BMI 28.1(25.1-30.1) kg/m2) group. Each patient was matched to an age-, gender-, weight- and height asymptomatic healthy control. Gait was measured while walking at comfortable speed on an instrumented treadmill with 3D motion capture system surrounded by a 180° projection screen displaying a virtual environment. The human body lower limb model with trunk markers was used[6]. 250 steps were recorded and averages over all measured steps per individual were used for analyses. SPT were presented as median(interquartile range). Independent t-test or Mann-Whitney U test was used to compare the patients with their control group. Statistical Parametric Mapping(independent t-test) was used to compare 3D trunk kinematics between the groups. Patients with ISc walked with comparable SPT to controls, whereas patients with DSc walked significantly slower(0.99(0.73-1.14) vs 1.30(1.13-1.39) m/s) with lower cadence (108.4(101.8-113.3) vs 118.3 (111.3-122.8) steps/min), smaller (1.08(0.84-1.28) vs 1.29(1.21-1.37) m) but wider steps (20(18-24) vs 16(14-20) cm), and increased stride- (1.11(1.07-1.18) vs 1.02(0.98-1.08) s), stance- (0.70(0.66-0.76) vs 0.61(0.58-0.66) s), and double support time (0.14(0.12-0.17) vs 0.11(0.09-0.13) s). Compared to their matched controls, DSc patients showed significantly increased anterior trunk tilt during the whole gait cycle, while ISc patients walked with significantly increased trunk lateroflexion during stance(0-52% gait cycle; Fig. 1). Both DSc and ISc patients had comparable trunk rotation compared to controls(Fig. 1). Fig. 1. 3D Trunk kinematic waveforms. Patients in green andcontrols in grey. Statistical Parametric Mapping statistics are presented.Download : Download high-res image (137KB)Download : Download full-size image ISc and DSc patients show different gait alterations compared to controls. ISc patients show decreased trunk lateroflexion","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"17 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":"135298699","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.2023.07.264
Zhongzheng Wang, Francesco Cenni, Iida Laatikainen-Raussi, Taija Finni, Ruoli Wang
Skeletal muscle architecture provides valuable insights for individuals with neuromuscular diseases, such as cerebral palsy (CP) [1]. Yet, to have a comprehensive view of muscle remodelling and better-informed clinical treatments, muscle quality (i.e. intramuscular fat, collagen fibres, and mechanical properties) should also be explored [2]. This comprehensive view can be achieved in a non-invasive image-based manner by combing magnetic resonance imaging (MRI) and shear wave elastography (SWE). What is the relationship between intramuscular fat fraction or T2 relaxation time and muscle mechanical properties? One individual with CP (13 years, male, GMFCS I) and four typically developing (TD, 17.3±7.9 years, 4 females) peers were enrolled in the study. Medial gastrocnemius (MG), lateral gastrocnemius (LG), and soleus (SOL) were assessed in neutral position (middle position between maximal dorsiflexed and plantarflexed position; CP -15.0°, TD -16.3±6.3°), while participants were laying prone with knee extended. SWE (Aixplorer, Supersonic Imagine) was recorded for MG and LG at mid-muscle belly, for SOL distally below the LG muscle-tendon junction. Shear modulus was estimated by means of an open-source software (ELASTOGUI, University of Nantes). Fat fraction and T2 relaxation times were estimated from modified Dixon and T2 mapping sequence using a 3.0-Tesla MR scanner (Ingenia CX, Philips Healthcare) at the same ankle position as SWE measurements. The intramuscular fat fraction was calculated based on 2-point fat-water separation [3]. T2 relaxation time is a quantitative parameter indicating collagen fibres content [4]. The correlation between shear modulus and fat fraction / T2 relaxation time was evaluated using linear correlation coefficient. Overall, the individual with CP showed higher muscle shear moduli than TD peers (Figure A) in all three muscles. The individual with CP had a similar fat content in MG and LG but higher fat content in SOL than TD peers (Figure B&F). Regarding the collagen fibres, the average T2 relaxation time for all three muscles were similar in both groups (Figure C). Overall, the correlation between muscle shear modulus and fat fraction / T2 relaxation time was weak (R=0.24 for fat fraction, R=-0.10 for T2 relaxation time, Figure D&E). Figure. (A-C) Average shear modulus, fat fraction, and T2 relaxation time. (D-E) Correlation between shear modulus and fat fraction / T2 relaxation time. The scatter points mean the imaging parameter and related shear modulus for all subjects. (F-G) Sample fat fraction and T2 maps. Download : Download high-res image (178KB)Download : Download full-size image This study is a first attempt to comprehensively analyze muscle quality in CP by combining MRI and SWE. It confirms the increased muscle fat fraction in CP [5], whilst no difference for T2 relaxation time was observed. The correlation results suggested higher passive muscle stiffness with higher fat content. These preliminary results nee
骨骼肌结构为脑瘫(CP)等神经肌肉疾病患者提供了宝贵的见解[1]。然而，为了全面了解肌肉重塑和更好的临床治疗，还应该探索肌肉质量(即肌内脂肪、胶原纤维和力学性能)[2]。通过结合磁共振成像(MRI)和横波弹性成像(SWE)，可以以一种无创的基于图像的方式获得这种全面的视图。肌内脂肪含量或T2松弛时间与肌肉力学性能有何关系?1例CP患者(13岁，男性，GMFCS I)和4例发育典型的TD患者(17.3±7.9岁，4例女性)被纳入研究。腓肠肌内侧(MG)、腓肠肌外侧(LG)和比目鱼肌(SOL)在中立位(最大背屈位和跖屈位之间的中间位置;CP -15.0°，TD -16.3±6.3°)，受试者俯卧，膝关节伸直。在中肌腹部的MG和LG, LG肌-肌腱连接处远端以下的SOL记录了SWE (aiexplorer, Supersonic Imagine)。剪切模量通过开源软件(ELASTOGUI, University of Nantes)估算。使用3.0-Tesla MR扫描仪(Ingenia CX, Philips Healthcare)在与SWE测量相同的脚踝位置，根据改进的Dixon和T2制图序列估计脂肪分数和T2松弛时间。肌内脂肪分数采用2点脂水分离法计算[3]。T2松弛时间是反映胶原纤维含量的定量参数[4]。用线性相关系数评价剪切模量与脂肪分数/ T2松弛时间的相关性。总体而言，CP患者的三块肌肉剪切模量均高于TD患者(图A)。CP患者的MG和LG脂肪含量相似，但SOL脂肪含量高于TD患者(图B&F)。在胶原纤维方面，两组三种肌肉的平均T2松弛时间相似(图C)。总体而言，肌肉剪切模量与脂肪分数/ T2松弛时间之间的相关性较弱(脂肪分数R=0.24, T2松弛时间R=-0.10，图D&E)。数字(A-C)平均剪切模量、脂肪分数和T2松弛时间。(D-E)剪切模量与脂肪分数/ T2松弛时间的相关性。散点表示所有受试者的成像参数和相关剪切模量。(F-G)样品脂肪分数和T2图。下载:下载高分辨率图像(178KB)下载:下载全尺寸图像本研究首次尝试结合MRI和SWE对CP的肌肉质量进行综合分析。它证实了CP中肌肉脂肪含量的增加[5]，而T2松弛时间没有观察到差异。相关结果表明，脂肪含量越高，被动肌肉僵硬度越高。一旦收集到更大的样本，这些初步结果需要得到证实。
{"title":"Muscle quality: Intramuscular fat, collagen fibres, and mechanical properties in the triceps surae","authors":"Zhongzheng Wang, Francesco Cenni, Iida Laatikainen-Raussi, Taija Finni, Ruoli Wang","doi":"10.1016/j.gaitpost.2023.07.264","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.264","url":null,"abstract":"Skeletal muscle architecture provides valuable insights for individuals with neuromuscular diseases, such as cerebral palsy (CP) [1]. Yet, to have a comprehensive view of muscle remodelling and better-informed clinical treatments, muscle quality (i.e. intramuscular fat, collagen fibres, and mechanical properties) should also be explored [2]. This comprehensive view can be achieved in a non-invasive image-based manner by combing magnetic resonance imaging (MRI) and shear wave elastography (SWE). What is the relationship between intramuscular fat fraction or T2 relaxation time and muscle mechanical properties? One individual with CP (13 years, male, GMFCS I) and four typically developing (TD, 17.3±7.9 years, 4 females) peers were enrolled in the study. Medial gastrocnemius (MG), lateral gastrocnemius (LG), and soleus (SOL) were assessed in neutral position (middle position between maximal dorsiflexed and plantarflexed position; CP -15.0°, TD -16.3±6.3°), while participants were laying prone with knee extended. SWE (Aixplorer, Supersonic Imagine) was recorded for MG and LG at mid-muscle belly, for SOL distally below the LG muscle-tendon junction. Shear modulus was estimated by means of an open-source software (ELASTOGUI, University of Nantes). Fat fraction and T2 relaxation times were estimated from modified Dixon and T2 mapping sequence using a 3.0-Tesla MR scanner (Ingenia CX, Philips Healthcare) at the same ankle position as SWE measurements. The intramuscular fat fraction was calculated based on 2-point fat-water separation [3]. T2 relaxation time is a quantitative parameter indicating collagen fibres content [4]. The correlation between shear modulus and fat fraction / T2 relaxation time was evaluated using linear correlation coefficient. Overall, the individual with CP showed higher muscle shear moduli than TD peers (Figure A) in all three muscles. The individual with CP had a similar fat content in MG and LG but higher fat content in SOL than TD peers (Figure B&F). Regarding the collagen fibres, the average T2 relaxation time for all three muscles were similar in both groups (Figure C). Overall, the correlation between muscle shear modulus and fat fraction / T2 relaxation time was weak (R=0.24 for fat fraction, R=-0.10 for T2 relaxation time, Figure D&E). Figure. (A-C) Average shear modulus, fat fraction, and T2 relaxation time. (D-E) Correlation between shear modulus and fat fraction / T2 relaxation time. The scatter points mean the imaging parameter and related shear modulus for all subjects. (F-G) Sample fat fraction and T2 maps. Download : Download high-res image (178KB)Download : Download full-size image This study is a first attempt to comprehensively analyze muscle quality in CP by combining MRI and SWE. It confirms the increased muscle fat fraction in CP [5], whilst no difference for T2 relaxation time was observed. The correlation results suggested higher passive muscle stiffness with higher fat content. These preliminary results nee","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"371 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":"135298837","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.2023.07.188
Yunus Ozdemir, Nazif Ekin Akalan, Yener Temelli
The Selective Motor Control Assessment of the Lower Extremity (SCALE) is a tool used to assess the quality of motor control of the lower extremity in cerebral palsy (CP). Selective motor control (SMC) is known to be associated with balance and some walking alterations, as well as a significant sign for gross motor function (1-3). It is well known that the single limb stance has a strong relationship with the stability in stance which is the main aim of physiotherapy for improving the quality of walking for CP (4). Therefore the aim of this study is to determine the relationship between SMC, single-limb standing (SLS) time and single support time (SST) of gait in CP. Is there any relationship between SMC with SLS time and SST of gait in individuals with CP? In this study, 10 individuals with spastic type diplegics CP (mean age: 12,7±5,86) were included and bilateral limbs (n:20) were evaluated. Inclusion criteria were GMFCS level I or II, walk 10 meters without assistive device. Patients who had undergone surgery or had botulinum toxin injections in the last 6 months were excluded. The Selective Control Assessment of the Lower Extremity (SCALE) was performed on the hip (S1), knee (S2), subtalar (S3), ankle (S4) and toes (S5) joint for SMC. In addition, the total foot score (TFS) was calculated by summing the subtalar, ankle and toe joint scores; and the total score (TS) is calculated by summing all joints. Independent SLS score of the Gross Motor Function Measure was applied (three point scale). The interested gait parameters of each individual were analyzed with a pedobarography (Win-track, Balma, France). The SST was normalized by dividing stance time. For each parameter, 3 averaged trials were included. Pearson and Spearman’s correlation with Cohen's classification were used for statistical analysis (5). S3, TFS and TS had a strongly positive correlation with SLS score. There was a moderate positive correlation between S5 and SST (Table 1). Download : Download high-res image (207KB)Download : Download full-size image Strong positive correlation of total foot and total scores on SCALE test with single limb stance may show that improving total SMC, especially on subtalar joints, may increase the time of independent standing on one leg. Although only SMC at toes has the moderate level correlation with SST which is also the parameter related with stability in stance phase (4). Therefore improving motor control on toe flex-extension may have a great potential on increasing stance phase stability for CP. It is worthwhile to design randomized control studies with a large number of participants to analyze the relationship of improving SMC and stability in the stance phase by 3D gait analysis in the future.
选择性下肢运动控制评估(Selective Motor Control Assessment of The Lower Extremity, SCALE)是一种用于评估脑瘫患者下肢运动控制质量的工具。选择性运动控制(SMC)已知与平衡和一些行走改变有关，也是大运动功能的重要标志(1-3)。众所周知，单肢站立与站立稳定性有很强的关系，而站立稳定性是CP物理治疗提高行走质量的主要目的(4)。因此，本研究的目的是确定SMC与CP中单肢站立(SLS)时间和步态单支撑时间(SST)之间的关系。在CP个体中，SMC与SLS时间和步态SST之间是否存在关系?本研究纳入10例痉挛性双瘫CP患者(平均年龄:12、7±5、86)，对20例双侧肢体进行评估。纳入标准为GMFCS I级或II级，无辅助器具行走10米。排除在过去6个月内接受过手术或注射过肉毒杆菌毒素的患者。对髋关节(S1)、膝关节(S2)、距下关节(S3)、踝关节(S4)和脚趾关节(S5)进行下肢选择性控制评估(SCALE)。此外，将距下、踝关节和脚趾关节评分相加计算足部总评分(TFS);总得分(TS)由所有关节之和计算。采用大肌肉运动功能量表独立SLS评分(三分制)。对每个个体感兴趣的步态参数进行足部摄影分析(Win-track, Balma, France)。通过除以姿态时间对海表温度进行归一化。对于每个参数，包括3次平均试验。采用Pearson和Spearman与Cohen分类的相关性进行统计分析(5)。S3、TFS、TS与SLS评分呈强正相关。S5与SST之间存在中等正相关(表1)。下载:下载高分辨率图像(207KB)下载:下载全尺寸图像单肢站立时，全足与SCALE测试总分呈强正相关，可能表明改善全足SMC，特别是距下关节，可以增加单腿独立站立的时间。虽然只有趾部SMC与SST有中等程度的相关性，而SST也是与站立阶段稳定性相关的参数(4)。因此，改善趾部屈伸运动控制可能对提高CP的站立阶段稳定性有很大的潜力。未来值得设计大量参与者的随机对照研究，通过三维步态分析来分析改善SMC与站立阶段稳定性的关系。
{"title":"Selective motor control may be associated with the single support time of gait and single limb standing time in cerebral palsy","authors":"Yunus Ozdemir, Nazif Ekin Akalan, Yener Temelli","doi":"10.1016/j.gaitpost.2023.07.188","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.188","url":null,"abstract":"The Selective Motor Control Assessment of the Lower Extremity (SCALE) is a tool used to assess the quality of motor control of the lower extremity in cerebral palsy (CP). Selective motor control (SMC) is known to be associated with balance and some walking alterations, as well as a significant sign for gross motor function (1-3). It is well known that the single limb stance has a strong relationship with the stability in stance which is the main aim of physiotherapy for improving the quality of walking for CP (4). Therefore the aim of this study is to determine the relationship between SMC, single-limb standing (SLS) time and single support time (SST) of gait in CP. Is there any relationship between SMC with SLS time and SST of gait in individuals with CP? In this study, 10 individuals with spastic type diplegics CP (mean age: 12,7±5,86) were included and bilateral limbs (n:20) were evaluated. Inclusion criteria were GMFCS level I or II, walk 10 meters without assistive device. Patients who had undergone surgery or had botulinum toxin injections in the last 6 months were excluded. The Selective Control Assessment of the Lower Extremity (SCALE) was performed on the hip (S1), knee (S2), subtalar (S3), ankle (S4) and toes (S5) joint for SMC. In addition, the total foot score (TFS) was calculated by summing the subtalar, ankle and toe joint scores; and the total score (TS) is calculated by summing all joints. Independent SLS score of the Gross Motor Function Measure was applied (three point scale). The interested gait parameters of each individual were analyzed with a pedobarography (Win-track, Balma, France). The SST was normalized by dividing stance time. For each parameter, 3 averaged trials were included. Pearson and Spearman’s correlation with Cohen's classification were used for statistical analysis (5). S3, TFS and TS had a strongly positive correlation with SLS score. There was a moderate positive correlation between S5 and SST (Table 1). Download : Download high-res image (207KB)Download : Download full-size image Strong positive correlation of total foot and total scores on SCALE test with single limb stance may show that improving total SMC, especially on subtalar joints, may increase the time of independent standing on one leg. Although only SMC at toes has the moderate level correlation with SST which is also the parameter related with stability in stance phase (4). Therefore improving motor control on toe flex-extension may have a great potential on increasing stance phase stability for CP. It is worthwhile to design randomized control studies with a large number of participants to analyze the relationship of improving SMC and stability in the stance phase by 3D gait analysis in the future.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"2010 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":"135298851","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}
One of the main parts of body that play key role in tennis matches is shoulder complex [1,2]. There are many joints and muscles caused shoulder to be complex [2–5]. Evaluation of the muscle activities is necessary to improve safety and performance [5]. The fundamental challenge for evaluation of muscle activity is measuring by EMG due to limitation of equipment, expensiveness, and inaccessibility to deep muscles [6–8]. Therefore, it is important to use musculoskeletal modeling to evaluate muscle activation [9–12]. On the other hand, there have been different musculoskeletal models with different joint definitions and the DOF [13,14]. Thus, the goal of this study was to validate the muscle activation output from different model by EMG data for the TFOS. How does muscle activity from experimental and modeling valuations change during the tennis forehand overhead smash (TFOS)? Twenty-five professional tennis athletes (Mass: 69.3±7.5 kg, Heights: 178±9.3 cm, Age: 29.5±7.5 years). The kinematics of markers were recorded by a 12 high-speed motion captures (Vicon, Oxford, UK, 100 Hz). The shoulder model of Holzbaur et al. [15–17] selected as base model and three version of models extracted based on the DOF: (5 DOF) a model with only three rotational DOF between humerus and trunk Glenohumeral joint, (11 DOF) a model with three rotational DOF for Scapulothoracic joint, Acromioclavicular joint, and Glenohumeral joint, (Stanford) a model with coupled motions for scapula, clavicle, and humerus. All models include two DOF for radio-ulna and elbow joints. After scaling the models, the inverse kinematics, inverse dynamics, and static optimization tools were applied to compute kinematics, kinetics, and muscle activity variables. The EMG activity in selective muscles was measured by the Myon wireless EMG system with a sampling frequency of 1000 Hz [18]. The average RMS of differences between each model and EMG (RMSE) over the muscles were 0.27±0.10, 0.29±0.12, and 0.22±0.10 for 5DOF, Stanford, and 11DOF models, respectively. Furthermore, the average Pearson's correlation coefficient over the muscles were 0.89±0.08, 0.88±0.09, and 0.93±0.60 for 5DOF, Stanford, and 11DOF models, respectively. The minimum RMS error (0.22±0.10) and maximum Pearson's correlation coefficient (0.93±0.60) were observed for 11 DOF model. Table 1: Muscle activity comparison between musculoskeletal simulation outputs (from three different models) and experimental data (EMG) including the RMSE, and Pearson's correlation coefficient for the TFOS movement.Download : Download high-res image (181KB)Download : Download full-size image According to the results, the 11 DOF model are more similar to the experimental (EMG) based on both RMSE and Pearson's correlation coefficient. Although the simulation results of some muscles were significantly different from the experimental results. Therefore, the alternative method to quantify muscle activation is musculoskeletal modeling. Moreover, the best mode
{"title":"Muscle activity of upper extremity during the is tennis forehand overhead smash: Experimental VS musculoskeletal modeling","authors":"Sheida Shourabadi Takabi, Meroeh Mohammadi, Reza Najarpour","doi":"10.1016/j.gaitpost.2023.07.162","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.162","url":null,"abstract":"One of the main parts of body that play key role in tennis matches is shoulder complex [1,2]. There are many joints and muscles caused shoulder to be complex [2–5]. Evaluation of the muscle activities is necessary to improve safety and performance [5]. The fundamental challenge for evaluation of muscle activity is measuring by EMG due to limitation of equipment, expensiveness, and inaccessibility to deep muscles [6–8]. Therefore, it is important to use musculoskeletal modeling to evaluate muscle activation [9–12]. On the other hand, there have been different musculoskeletal models with different joint definitions and the DOF [13,14]. Thus, the goal of this study was to validate the muscle activation output from different model by EMG data for the TFOS. How does muscle activity from experimental and modeling valuations change during the tennis forehand overhead smash (TFOS)? Twenty-five professional tennis athletes (Mass: 69.3±7.5 kg, Heights: 178±9.3 cm, Age: 29.5±7.5 years). The kinematics of markers were recorded by a 12 high-speed motion captures (Vicon, Oxford, UK, 100 Hz). The shoulder model of Holzbaur et al. [15–17] selected as base model and three version of models extracted based on the DOF: (5 DOF) a model with only three rotational DOF between humerus and trunk Glenohumeral joint, (11 DOF) a model with three rotational DOF for Scapulothoracic joint, Acromioclavicular joint, and Glenohumeral joint, (Stanford) a model with coupled motions for scapula, clavicle, and humerus. All models include two DOF for radio-ulna and elbow joints. After scaling the models, the inverse kinematics, inverse dynamics, and static optimization tools were applied to compute kinematics, kinetics, and muscle activity variables. The EMG activity in selective muscles was measured by the Myon wireless EMG system with a sampling frequency of 1000 Hz [18]. The average RMS of differences between each model and EMG (RMSE) over the muscles were 0.27±0.10, 0.29±0.12, and 0.22±0.10 for 5DOF, Stanford, and 11DOF models, respectively. Furthermore, the average Pearson's correlation coefficient over the muscles were 0.89±0.08, 0.88±0.09, and 0.93±0.60 for 5DOF, Stanford, and 11DOF models, respectively. The minimum RMS error (0.22±0.10) and maximum Pearson's correlation coefficient (0.93±0.60) were observed for 11 DOF model. Table 1: Muscle activity comparison between musculoskeletal simulation outputs (from three different models) and experimental data (EMG) including the RMSE, and Pearson's correlation coefficient for the TFOS movement.Download : Download high-res image (181KB)Download : Download full-size image According to the results, the 11 DOF model are more similar to the experimental (EMG) based on both RMSE and Pearson's correlation coefficient. Although the simulation results of some muscles were significantly different from the experimental results. Therefore, the alternative method to quantify muscle activation is musculoskeletal modeling. Moreover, the best mode","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":"135299042","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.2023.07.216
Maria B. Sánchez, Andy Sanderson, Emma Hodson-Tole
The trunk represents almost 50% of the total mass of a person [1] and, because it comprises multiple segments, has a large range of motion [2]. Trunk posture and movement are important in the execution of activities of daily living (ADL), especially for those related with arm function [3]. However, in movement analysis, the trunk is usually defined as a single rigid, cylindrical segment between the shoulders and pelvis. This oversimplification ignores the large movement potential the trunk has [2], and therefore does not enable a complete evaluation of trunk movement. Does a single segment trunk model adequately reveal trunk movements for a simple reaching and grasping movement? The University Ethics Committee (ref:47565) approved the project. Eleven people (7 male; (mean ±SD) age: 27.82 ±3.18years, height: 1.74 ±0.11 m; weight: 75.0 ±12.7 kg) participated after signing the consent form. An upper-body marker-set was used: left/right acromion, iliac-crest, ASIS; manubrium, S1; five inverted “L” clusters of 3 markers: two 2.5 cm lateral of C7, T3, T7, T11 and L3, with the third marker on the long end of the “L” with the length adjusted based on the participant’ s size. These defined a single-segment-trunk (acromia to iliac-crests), and upper-, mid- and lower-thoracic, and upper- and lower-lumbar segments (multi-segment-trunk). Participants were asked to stand from a hight-adjustable bench, walk to a low table and lean to collect a mug before returning to the bench. Motion capture data were recorded (100 Hz), tracked, and processed. Segmental angles (in relation to the absolute coordinate system) were estimated for the “leaning to collect” section of each trial. The total displacement in each plane and a combined 3D movement (sum of the three planes) of the single-segment-trunk and of the multi-segment-trunk compared with a paired sample t-test. Table 1 shows the difference in the combined 3D movement for the single-segment-trunk when compared to the multi-segment-trunk (t = 27.95, p<.01) and for each of the planes of movement (t = 18.21, 11.19, 14.15, p<.01, for sagittal, frontal and horizontal). The standardised mean difference was considered very large (8.07 ±8.06).Download : Download high-res image (82KB)Download : Download full-size image This simplified approach identified the scale of additional information that could be gained from a multi-segment-trunk. Further exploration should focus on understanding if the amount of movement in a multi-segment-trunk vs single-segment-trunk is of a very different magnitude; it should also look specifically at where are the more important differences. Additional development might focus on understanding the best representation of the trunk movement when assessing ADL in clinical populations. I would say this phrasing is better, calling your approach very simple is an insult to your work, calling it simplified indicates that you’re just presenting in a simple way for them.
{"title":"Does a single segment trunk model adequately reveal trunk movements for a simple reaching and grasping movement?","authors":"Maria B. Sánchez, Andy Sanderson, Emma Hodson-Tole","doi":"10.1016/j.gaitpost.2023.07.216","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.216","url":null,"abstract":"The trunk represents almost 50% of the total mass of a person [1] and, because it comprises multiple segments, has a large range of motion [2]. Trunk posture and movement are important in the execution of activities of daily living (ADL), especially for those related with arm function [3]. However, in movement analysis, the trunk is usually defined as a single rigid, cylindrical segment between the shoulders and pelvis. This oversimplification ignores the large movement potential the trunk has [2], and therefore does not enable a complete evaluation of trunk movement. Does a single segment trunk model adequately reveal trunk movements for a simple reaching and grasping movement? The University Ethics Committee (ref:47565) approved the project. Eleven people (7 male; (mean ±SD) age: 27.82 ±3.18years, height: 1.74 ±0.11 m; weight: 75.0 ±12.7 kg) participated after signing the consent form. An upper-body marker-set was used: left/right acromion, iliac-crest, ASIS; manubrium, S1; five inverted “L” clusters of 3 markers: two 2.5 cm lateral of C7, T3, T7, T11 and L3, with the third marker on the long end of the “L” with the length adjusted based on the participant’ s size. These defined a single-segment-trunk (acromia to iliac-crests), and upper-, mid- and lower-thoracic, and upper- and lower-lumbar segments (multi-segment-trunk). Participants were asked to stand from a hight-adjustable bench, walk to a low table and lean to collect a mug before returning to the bench. Motion capture data were recorded (100 Hz), tracked, and processed. Segmental angles (in relation to the absolute coordinate system) were estimated for the “leaning to collect” section of each trial. The total displacement in each plane and a combined 3D movement (sum of the three planes) of the single-segment-trunk and of the multi-segment-trunk compared with a paired sample t-test. Table 1 shows the difference in the combined 3D movement for the single-segment-trunk when compared to the multi-segment-trunk (t = 27.95, p<.01) and for each of the planes of movement (t = 18.21, 11.19, 14.15, p<.01, for sagittal, frontal and horizontal). The standardised mean difference was considered very large (8.07 ±8.06).Download : Download high-res image (82KB)Download : Download full-size image This simplified approach identified the scale of additional information that could be gained from a multi-segment-trunk. Further exploration should focus on understanding if the amount of movement in a multi-segment-trunk vs single-segment-trunk is of a very different magnitude; it should also look specifically at where are the more important differences. Additional development might focus on understanding the best representation of the trunk movement when assessing ADL in clinical populations. I would say this phrasing is better, calling your approach very simple is an insult to your work, calling it simplified indicates that you’re just presenting in a simple way for them.","PeriodicalId":94018,"journal":{"name":"Gait & posture","volume":"37 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":"135299043","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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