Pub Date : 2023-09-01DOI: 10.1016/j.gaitpost.2023.07.149
Mario Martínez Zarzuela, David González-Ortega, Míriam Antón-Rodríguez, Francisco Javier Díaz-Pernas, Henning Müller, Cristina Simón-Martínez
The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings [1]. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems [2]. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial [3]. For upper-limb activity recognition out-of-the-lab, do wearables or single camera offer better performance? Recordings from 16 healthy subjects performing 8 upper-limb activities from the VIDIMU dataset [4] were used. For wearable recordings, the subjects wore 5 IMU-based wearables and adopted a neutral pose (N-pose) for calibration. Joint angles were estimated with inverse kinematics algorithms in OpenSense [5]. Single-camera video recordings occurred simultaneously. Joint angles were estimated with inverse kinematics algorithms in OpenSense. Single-camera video recordings occurred simultaneously, and the subject’s pose was estimated with DeepStream [6]. We compared various Deep Learning architectures (DNN, CNN, CNN-LSTM, LSTM-CNN, LSTM, LSTM-AE) for recognizing daily living activities. The input to the different neural architectures consisted in a 2-second time series containing the estimated joint angles and their 2D FFT. Every network was trained using 2 subjects for validation, a batch size of 20, Adam as the optimizer, and combining early stopping and other regularization techniques. Performance metrics were extracted from 4-fold cross-validation experiments. In all neural networks, performance was higher with IMU-based wearables data compared to video. The best network was an LSTM AutoEncoder (6 layers, 700 K parameters; wearable data accuracy:0.985, F1-score:0.936 (Fig. 1); video data accuracy:0.962, F1-score:0.842). Remarkably, when using video as input there were no significant differences in the performance metrics obtained among different architectures. On the contrary, the F1 scores using IMU data varied significantly (DNN: 0.849, CNN: 0.889, CNN-LSTM: 0.879, LSTM-CNN: 0.904, LSTM: 0.920, LSTM-AE: 0.936).Download : Download high-res image (108KB)Download : Download full-size image Wearables and video present advantages and disadvantages. While IMUs can provide accurate information about the orientation and acceleration of body parts, body-to-segment calibration and drift can affect data reliability. Similarly, a single camera can easily track the position of different body joints, but the recorded data does not yet reliably represent the movement with all degrees of freedom. Our experiments confirm that despite the current limitations of wearables, with a very si
{"title":"A comparative study on wearables and single-camera video for upper-limb out-of-the-lab activity recognition with different deep learning architectures","authors":"Mario Martínez Zarzuela, David González-Ortega, Míriam Antón-Rodríguez, Francisco Javier Díaz-Pernas, Henning Müller, Cristina Simón-Martínez","doi":"10.1016/j.gaitpost.2023.07.149","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.149","url":null,"abstract":"The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings [1]. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices for movement acquisition. Promising solutions in this context are IMU-based wearables and single camera systems [2]. Additionally, the development of machine learning systems able to recognize and digest clinically relevant data in-the-wild is needed, and therefore determining the ideal input to those is crucial [3]. For upper-limb activity recognition out-of-the-lab, do wearables or single camera offer better performance? Recordings from 16 healthy subjects performing 8 upper-limb activities from the VIDIMU dataset [4] were used. For wearable recordings, the subjects wore 5 IMU-based wearables and adopted a neutral pose (N-pose) for calibration. Joint angles were estimated with inverse kinematics algorithms in OpenSense [5]. Single-camera video recordings occurred simultaneously. Joint angles were estimated with inverse kinematics algorithms in OpenSense. Single-camera video recordings occurred simultaneously, and the subject’s pose was estimated with DeepStream [6]. We compared various Deep Learning architectures (DNN, CNN, CNN-LSTM, LSTM-CNN, LSTM, LSTM-AE) for recognizing daily living activities. The input to the different neural architectures consisted in a 2-second time series containing the estimated joint angles and their 2D FFT. Every network was trained using 2 subjects for validation, a batch size of 20, Adam as the optimizer, and combining early stopping and other regularization techniques. Performance metrics were extracted from 4-fold cross-validation experiments. In all neural networks, performance was higher with IMU-based wearables data compared to video. The best network was an LSTM AutoEncoder (6 layers, 700 K parameters; wearable data accuracy:0.985, F1-score:0.936 (Fig. 1); video data accuracy:0.962, F1-score:0.842). Remarkably, when using video as input there were no significant differences in the performance metrics obtained among different architectures. On the contrary, the F1 scores using IMU data varied significantly (DNN: 0.849, CNN: 0.889, CNN-LSTM: 0.879, LSTM-CNN: 0.904, LSTM: 0.920, LSTM-AE: 0.936).Download : Download high-res image (108KB)Download : Download full-size image Wearables and video present advantages and disadvantages. While IMUs can provide accurate information about the orientation and acceleration of body parts, body-to-segment calibration and drift can affect data reliability. Similarly, a single camera can easily track the position of different body joints, but the recorded data does not yet reliably represent the movement with all degrees of freedom. Our experiments confirm that despite the current limitations of wearables, with a very si","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":"135297896","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.2023.07.193
Sofia Pastrouma, Filippos Kasiotis, Aikaterini - Evanthia Gkanatsiou, Natalia Kitsouli, Konstantinos Vassis, Zacharias Dimitriadis, Savvas Spanos, Ioannis Poulis
Decreased hip abductor strength has been associated with a range of knee pathologies. Hip abduction muscles play a critical role in providing pelvic stability and leg alignment during weight-bearing movements by eccentrically controlling hip adduction. Poor hip control can result in abnormal lower extremity motions, and studies have reported that lower limb problems such as patellofemoral pain (PFP),1 knee osteoarthritis (OA),2 and ACL injuries,3,4 are linked with altered lower limb kinematics, with a higher prevalence in females.5 A body of literature suggests that increased dynamic knee valgus is associated with hip abductor weakness.6 Moreover, several studies have shown that interventions such as neuromuscular training (NMT) programs can lower the incidence of lower extremity problems. An NMT program emphasizing optimal alignment of the trunk and lower limb joints relative to each other, along with quality movement performance while dynamically and functionally strengthening the lower limb muscles, may be better at enhancing hip muscle strength. Therefore, we performed a randomized controlled trial evaluating the effects of NMT in comparison to a progressive resistance training program (PRT) on muscle hip abductor strength. To investigate whether a 6-week NMT can improve the hip abductor muscle strength better than a PRT. The present study was a single-blind randomized controlled trial aiming to investigate the effects of two interventions on asymptomatic females, aged 18-35 years old. Sample size calculation revealed that 26 participants per group were required. Following a baseline assessment, 52 participants were randomly assigned to either a 6-week PRT or NMT intervention involving 3 sessions per week. The PRT intervention consisted of hip abductor exercises performed in an open kinetic chain, with three to four sets of ten repetitions at a target intensity of 6-8 RPE.7,8,9 The NMT intervention focused on improving functional stability, balance, proprioception, strength, agility, postural function, and orientation,10,11 consisting of weight-bearing positions. The participants were assessed after the 6-week intervention. Mean peak hip abduction, concentric and eccentric torque, were measured by a blinded assessor on a Biodex System 3 Pro isokinetic dynamometer at 60°/s. Dependent t-tests showed significant improvements in CON60, and ECC60 after both interventions (<0.05) (Table 1). Two-way mixed ANOVAs did not reveal statistically significant Group*Time interactions for the CON60 and ECC60. The results from the comparison of the effectiveness of each intervention are visually presented in Figs. 1 and 2. Download : Download high-res image (114KB)Download : Download full-size image Both PRT and NMT improved abductor strength. However, both groups had similar overall differences in strength before and after the intervention. Since no intervention is superior to the other, neuromuscular training might be clinically preferred as it combines dyn
髋关节外展肌力量下降与一系列膝关节病变有关。髋外展肌通过偏心控制髋内收，在负重运动中提供骨盆稳定性和腿部对齐方面发挥关键作用。髋关节控制不佳可导致下肢运动异常，研究报道，下肢问题，如髌股疼痛(PFP)，1膝骨关节炎(OA)，2和前交叉韧带损伤，3,4与下肢运动学改变有关，女性患病率更高5大量文献表明，动态膝外翻增加与髋外展肌无力有关此外，一些研究表明，神经肌肉训练(NMT)计划等干预措施可以降低下肢问题的发生率。NMT项目强调躯干和下肢关节相对于彼此的最佳对齐，以及在动态和功能性加强下肢肌肉的同时进行高质量的运动表现，可能会更好地增强髋关节肌肉力量。因此，我们进行了一项随机对照试验，评估NMT与进行性阻力训练计划(PRT)对肌肉髋关节外展肌力量的影响。研究6周的NMT是否能比PRT更好地改善髋关节外展肌力量。本研究是一项单盲随机对照试验，旨在探讨两种干预措施对18-35岁无症状女性的影响。样本量计算显示，每组需要26名参与者。在基线评估之后，52名参与者被随机分配到为期6周的PRT或NMT干预组，每周进行3次干预。PRT干预包括在开放的动力链中进行髋关节外展肌锻炼，每组重复3至4组，每组10次，目标强度为6- 8rpe 7,8,9。NMT干预侧重于改善功能稳定性、平衡、本体感觉、力量、敏捷性、姿势功能和定向10,11，包括负重姿势。干预6周后对参与者进行评估。平均峰值髋外展，同心和偏心扭矩，由盲法评估者在60°/s的Biodex System 3 Pro等速测力仪上测量。依赖t检验显示，两种干预措施后CON60和ECC60均有显著改善(<0.05)(表1)。双向混合方差分析未显示CON60和ECC60在组*时间的相互作用具有统计学意义。图1和图2直观地展示了各干预措施有效性比较的结果。下载:下载高分辨率图像(114KB)下载:下载全尺寸图像PRT和NMT均提高了外展肌力量。然而，两组在干预前后的力量总体差异相似。由于没有任何干预措施优于其他干预措施，神经肌肉训练可能是临床首选，因为它结合了个体的动态和功能强化。
{"title":"Comparison of neuromuscular and abductor strengthening exercises in the hip abductor muscle strength: A randomized controlled trial","authors":"Sofia Pastrouma, Filippos Kasiotis, Aikaterini - Evanthia Gkanatsiou, Natalia Kitsouli, Konstantinos Vassis, Zacharias Dimitriadis, Savvas Spanos, Ioannis Poulis","doi":"10.1016/j.gaitpost.2023.07.193","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.193","url":null,"abstract":"Decreased hip abductor strength has been associated with a range of knee pathologies. Hip abduction muscles play a critical role in providing pelvic stability and leg alignment during weight-bearing movements by eccentrically controlling hip adduction. Poor hip control can result in abnormal lower extremity motions, and studies have reported that lower limb problems such as patellofemoral pain (PFP),1 knee osteoarthritis (OA),2 and ACL injuries,3,4 are linked with altered lower limb kinematics, with a higher prevalence in females.5 A body of literature suggests that increased dynamic knee valgus is associated with hip abductor weakness.6 Moreover, several studies have shown that interventions such as neuromuscular training (NMT) programs can lower the incidence of lower extremity problems. An NMT program emphasizing optimal alignment of the trunk and lower limb joints relative to each other, along with quality movement performance while dynamically and functionally strengthening the lower limb muscles, may be better at enhancing hip muscle strength. Therefore, we performed a randomized controlled trial evaluating the effects of NMT in comparison to a progressive resistance training program (PRT) on muscle hip abductor strength. To investigate whether a 6-week NMT can improve the hip abductor muscle strength better than a PRT. The present study was a single-blind randomized controlled trial aiming to investigate the effects of two interventions on asymptomatic females, aged 18-35 years old. Sample size calculation revealed that 26 participants per group were required. Following a baseline assessment, 52 participants were randomly assigned to either a 6-week PRT or NMT intervention involving 3 sessions per week. The PRT intervention consisted of hip abductor exercises performed in an open kinetic chain, with three to four sets of ten repetitions at a target intensity of 6-8 RPE.7,8,9 The NMT intervention focused on improving functional stability, balance, proprioception, strength, agility, postural function, and orientation,10,11 consisting of weight-bearing positions. The participants were assessed after the 6-week intervention. Mean peak hip abduction, concentric and eccentric torque, were measured by a blinded assessor on a Biodex System 3 Pro isokinetic dynamometer at 60°/s. Dependent t-tests showed significant improvements in CON60, and ECC60 after both interventions (<0.05) (Table 1). Two-way mixed ANOVAs did not reveal statistically significant Group*Time interactions for the CON60 and ECC60. The results from the comparison of the effectiveness of each intervention are visually presented in Figs. 1 and 2. Download : Download high-res image (114KB)Download : Download full-size image Both PRT and NMT improved abductor strength. However, both groups had similar overall differences in strength before and after the intervention. Since no intervention is superior to the other, neuromuscular training might be clinically preferred as it combines dyn","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":"135298030","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.2023.07.172
Anne Mcnee, Jonathan Noble, Stuart Evans, Karen Ziegler, Stephen Ng Man Sun, Alison Hulme, Nicola Fry, Adam Shortland
Plantarflexion contractures are often the focus for intervention in children who toe walk (TW). Caserta et.al1 found reduced plantarflexor strength in TW and greater proportions of type 1 fibres were identified in the plantarflexors2. Variable but mild differences in kinematics have been found between children with mild bilateral cerebral palsy (CP) and TW3,4. Children with CP have reduced muscle volumes compared to typically developing children5. Plantarflexor morphology in TW has not yet been described. Is ankle plantarflexor volume reduced in children who toe walk? Eight children (5male) aged 7-15 yr (mean=11.86 yrs) referred to our orthopaedic department for toe walking and plantarflexion contractures, with no underlying diagnosis, had a routine examination in the gait laboratory. They were matched for age and sex to children with CP (GMFCS I-II) who had also been examined. Assessment included gait analysis and 2D ultrasound imaging of the lateral gastrocnemius(LG). Muscle volumes were estimated by the Vanmechelen et.al6 method, normalised to mass. Selective motor control (SCALE) was assessed according to Fowler et.al7. Mobility was assessed using the Gillette Functional Assessment Questionnaire (GFAQ) 8. Data was compared to a large database of controls (unpaired t-test) and between groups (paired t-test). One limb per subject was randomly selected for analysis. All children had plantarflexor contractures: mean passive dorsiflexion range (knee extended) of -9.4° (SD10.9°) for TW and -6.5° (SD7.2°) for CP. TW had close to normal motor control (SCALE:Median=10, Range=8-10) whereas CP had a greater variability (SCALE:Median=9.5, Range=5-10). Walking function was within normal limits for TW (GFAQ Median=10 Range=8-10) but more variable for CP (GFAQ Median=8 Range=5-10). No difference in speed/cadence was found between groups (p=0.5/p=0.86) and these were within normal limits. All children were in ankle plantarflexion at initial contact (no difference between groups, p=0.48). Mean ankle dorsiflexion in stance and swing were not different between groups (p=0.94, p=0.84). For four TW children, normalised mean LG volume was significantly smaller than controls (1.07vs1.53 ml/kg) (p<0.01) but no different to CP (1.01 ml/kg) (p=0.64). The other TW had LG CSA which was too great for the US field of view. In the presence of an ankle plantarflexion contracture, TW children show less variability in selective motor control and functional mobility to a matched CP group. TW and CP show similar kinematics at the ankle, cadence and speed. A subgroup of TW children had reduced normalised LG compared to control data, comparable in size to the CP group. Other subjects’ muscles were larger and could not be measured. This suggests subgroups of TW with different muscle sizes, which has implications for aetiology and management. Further work is required to further elucidate the triceps surae muscle morphology in TW and relationship between morphology and toe walking.
{"title":"The volume of the lateral gastrocnemius appears reduced in some Idiopathic toe walkers","authors":"Anne Mcnee, Jonathan Noble, Stuart Evans, Karen Ziegler, Stephen Ng Man Sun, Alison Hulme, Nicola Fry, Adam Shortland","doi":"10.1016/j.gaitpost.2023.07.172","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.172","url":null,"abstract":"Plantarflexion contractures are often the focus for intervention in children who toe walk (TW). Caserta et.al1 found reduced plantarflexor strength in TW and greater proportions of type 1 fibres were identified in the plantarflexors2. Variable but mild differences in kinematics have been found between children with mild bilateral cerebral palsy (CP) and TW3,4. Children with CP have reduced muscle volumes compared to typically developing children5. Plantarflexor morphology in TW has not yet been described. Is ankle plantarflexor volume reduced in children who toe walk? Eight children (5male) aged 7-15 yr (mean=11.86 yrs) referred to our orthopaedic department for toe walking and plantarflexion contractures, with no underlying diagnosis, had a routine examination in the gait laboratory. They were matched for age and sex to children with CP (GMFCS I-II) who had also been examined. Assessment included gait analysis and 2D ultrasound imaging of the lateral gastrocnemius(LG). Muscle volumes were estimated by the Vanmechelen et.al6 method, normalised to mass. Selective motor control (SCALE) was assessed according to Fowler et.al7. Mobility was assessed using the Gillette Functional Assessment Questionnaire (GFAQ) 8. Data was compared to a large database of controls (unpaired t-test) and between groups (paired t-test). One limb per subject was randomly selected for analysis. All children had plantarflexor contractures: mean passive dorsiflexion range (knee extended) of -9.4° (SD10.9°) for TW and -6.5° (SD7.2°) for CP. TW had close to normal motor control (SCALE:Median=10, Range=8-10) whereas CP had a greater variability (SCALE:Median=9.5, Range=5-10). Walking function was within normal limits for TW (GFAQ Median=10 Range=8-10) but more variable for CP (GFAQ Median=8 Range=5-10). No difference in speed/cadence was found between groups (p=0.5/p=0.86) and these were within normal limits. All children were in ankle plantarflexion at initial contact (no difference between groups, p=0.48). Mean ankle dorsiflexion in stance and swing were not different between groups (p=0.94, p=0.84). For four TW children, normalised mean LG volume was significantly smaller than controls (1.07vs1.53 ml/kg) (p<0.01) but no different to CP (1.01 ml/kg) (p=0.64). The other TW had LG CSA which was too great for the US field of view. In the presence of an ankle plantarflexion contracture, TW children show less variability in selective motor control and functional mobility to a matched CP group. TW and CP show similar kinematics at the ankle, cadence and speed. A subgroup of TW children had reduced normalised LG compared to control data, comparable in size to the CP group. Other subjects’ muscles were larger and could not be measured. This suggests subgroups of TW with different muscle sizes, which has implications for aetiology and management. Further work is required to further elucidate the triceps surae muscle morphology in TW and relationship between morphology and toe walking.","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":"135298032","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.2023.08.027
Patricia Van De Walle, An Jansen, An-Sofie Schoonjans, Anke Van Dijck, Colette Puts, Iris van Hal, Marijn Weren, Kinaci Esra, Ann Hallemans
{"title":"Gait deviations in rare genetic syndromes: is there a common denomitator for patients with Dravet, HVDAS and TSC?","authors":"Patricia Van De Walle, An Jansen, An-Sofie Schoonjans, Anke Van Dijck, Colette Puts, Iris van Hal, Marijn Weren, Kinaci Esra, Ann Hallemans","doi":"10.1016/j.gaitpost.2023.08.027","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.08.027","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":"135298034","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.2023.08.024
Damien Kiernan, Ailish Malone
{"title":"Age related changes in lower-limb joint coordination during gait in children with bilateral cerebral palsy","authors":"Damien Kiernan, Ailish Malone","doi":"10.1016/j.gaitpost.2023.08.024","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.08.024","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":"135298035","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.2023.07.167
Mariapia Musci, Simona Aresta, Francesco Bottiglione, Michele Ruta, Tommaso Di Noia, Rodolfo Sardone, Ilaria Bortone
Weakness, as measured by maximal Hand Grip Strength (HGS), represents one of the five criteria used in Fried's definition of frailty [1] and is associated with a wide range of health conditions, which makes it challenging to delineate what body system processes are responsible for weakness. [2]. Still, poor studies have investigated the associations between HGS and dynamic functional assessments [3]. To identify a pattern of functional characteristics, extracted from the 5-repetitions-sit-to-stand (5STS) test biomechanical signals best predict weakness. An Explanation approach to a Machine Learning model was also used. In a subcohort of the longitudinal study of aging [4], 86 subjects over 65 performed the 5STS test [5,6]. They were equipped with an IMU on the L5 vertebra and four sEMG probes (BTS Bioengineering) on the Gastrocnemius Medialis and Tibialis Anterior both side muscles. Several kinematic and muscular features were extracted from the cycle, standing and sitting phases. A handgrip dynamometer was used to measure HGS. Men and women who were considered weak had HGS<26 kg and <16 kg, respectively. Socio-demographic information (age, sex and BMI) was also included. The final dataset consisted of 119 features for all subjects. We first performed the undersampling of the majority class (without weakness); then the dataset was divided into 70% training and 30% testing and normalised using the z-score method. Because of the curse of dimensionality, a pipeline for feature selection and hyperparameter tuning, using the GridSearchCV method, was defined to obtain the best Kernel-SVM model. The best model was chosen according to the accuracy score. To evaluate our model accuracy, precision and recall were calculated. All the analyses were performed using the Scikit-Learn library [7] with Python 3.6. To explain our model Python's SHAP library was used [8]. From the hyperparameter tuning, we obtained six features: hip power (Whip), power along the vertical axis (Wvert), and cycle jerk along the vertical axis and its coefficient of variation, age, and sex. Fig. 1 shows the boxplots for the biomechanical selected variables.The model showed 90.0% and 85.7% accuracy on the training and testing sets, respectively. The precision of 100%, recall of 71%, and f1-score 83%, while the precision of 78%, recall of 100%, and f1-score of 88% was obtained on the class without weakness and its counterpart, respectively.The explainability analysis showed that age, Wvert and Whip were the three most important variables in predicting weakness in absolute terms. Sex resulted being the least important variable. Picture 1 - "Boxplot of the biomechanical selected features according to the weakness condition"Download : Download high-res image (71KB)Download : Download full-size image Measures of HGS are associated with deficits in several physical functions. In a population-based setting, we identified biomechanical features from 5STS related to stability that could help pre
{"title":"Explainable machine learning approach on biomechanical features to identify weakness in a population-based setting on aging","authors":"Mariapia Musci, Simona Aresta, Francesco Bottiglione, Michele Ruta, Tommaso Di Noia, Rodolfo Sardone, Ilaria Bortone","doi":"10.1016/j.gaitpost.2023.07.167","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.167","url":null,"abstract":"Weakness, as measured by maximal Hand Grip Strength (HGS), represents one of the five criteria used in Fried's definition of frailty [1] and is associated with a wide range of health conditions, which makes it challenging to delineate what body system processes are responsible for weakness. [2]. Still, poor studies have investigated the associations between HGS and dynamic functional assessments [3]. To identify a pattern of functional characteristics, extracted from the 5-repetitions-sit-to-stand (5STS) test biomechanical signals best predict weakness. An Explanation approach to a Machine Learning model was also used. In a subcohort of the longitudinal study of aging [4], 86 subjects over 65 performed the 5STS test [5,6]. They were equipped with an IMU on the L5 vertebra and four sEMG probes (BTS Bioengineering) on the Gastrocnemius Medialis and Tibialis Anterior both side muscles. Several kinematic and muscular features were extracted from the cycle, standing and sitting phases. A handgrip dynamometer was used to measure HGS. Men and women who were considered weak had HGS<26 kg and <16 kg, respectively. Socio-demographic information (age, sex and BMI) was also included. The final dataset consisted of 119 features for all subjects. We first performed the undersampling of the majority class (without weakness); then the dataset was divided into 70% training and 30% testing and normalised using the z-score method. Because of the curse of dimensionality, a pipeline for feature selection and hyperparameter tuning, using the GridSearchCV method, was defined to obtain the best Kernel-SVM model. The best model was chosen according to the accuracy score. To evaluate our model accuracy, precision and recall were calculated. All the analyses were performed using the Scikit-Learn library [7] with Python 3.6. To explain our model Python's SHAP library was used [8]. From the hyperparameter tuning, we obtained six features: hip power (Whip), power along the vertical axis (Wvert), and cycle jerk along the vertical axis and its coefficient of variation, age, and sex. Fig. 1 shows the boxplots for the biomechanical selected variables.The model showed 90.0% and 85.7% accuracy on the training and testing sets, respectively. The precision of 100%, recall of 71%, and f1-score 83%, while the precision of 78%, recall of 100%, and f1-score of 88% was obtained on the class without weakness and its counterpart, respectively.The explainability analysis showed that age, Wvert and Whip were the three most important variables in predicting weakness in absolute terms. Sex resulted being the least important variable. Picture 1 - \"Boxplot of the biomechanical selected features according to the weakness condition\"Download : Download high-res image (71KB)Download : Download full-size image Measures of HGS are associated with deficits in several physical functions. In a population-based setting, we identified biomechanical features from 5STS related to stability that could help 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":"135298039","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.2023.07.110
Narges Jahantigh Akbari, Mohammad Yousefi
MS is an autoimmune disease of the central nervous system (1). Postural balance impairment is frequently depicted as one of the initial symptoms of MS and one of the most impairing MS symptoms (2, 3). the gait impairment is one of the most common indications of MS and influences the quality of life (4). Although investigation of the quiet stance is critical for analyzing the balance impairments, more challenging situations such as gait initiation (GI) may give a deeper knowledge into understanding of the mechanisms of dynamic postural control (5). Therefore, the aim of this study was to evaluate the gait pattern in terms of spatio-temporal, kinematic and kinetic parameters during the gait initiation phase in people with MS. What are the influencing factors on the gait pattern in gait initiation phase in MS patients? Search in PubMed, Cochrane, Science of Direct, Web of science, Scopus, and Google scholar Databases from 2019 to December 2022 was conducted. Studies were included if: individuals with multiple sclerosis disease, examination the gait initiation phase, and assessment of center of pressure(COP), anticipatory postural adjustments (APA), spatio-temporal, kinematic and kinetic parameters and muscular synergies as outcome measure were evaluated. Included studies were independently reviewed by two authors for their quality evaluation using modified Downs and Black checklist. Since meta-analysis was not possible, studies were descriptively presented. Ten articles were selected for final analysis. A total of 231 MS patients and 132 healthy subjects were included with ages ranging from 18 to 76 years old. The findings of studies showed COP position at gait initiation was more anterior and net muscular moments for each joint were significantly different during the APA phase with smoother variations in patients with MS than healthy participants. A lower amount of muscle activity during APA were reported. The first step was shorter in MS patients compared to healthy people. Gait initiation in the early stage of MS could be a useful measure to characterize balance. These results suggest that it is possible to use the gait initiation evaluation as an effective method for the rehabilitation treatment of these patients.
MS是一种中枢神经系统的自身免疫性疾病(1)。体位平衡障碍经常被描述为MS的初始症状之一，也是最具损害性的MS症状之一(2,3)。步态障碍是MS最常见的适应症之一，并影响生活质量(4)。尽管安静姿势的研究对于分析平衡障碍至关重要，更多具有挑战性的情况，如步态起始(GI)可能会让我们更深入地了解动态姿势控制的机制(5)。因此，本研究的目的是从时空、运动学和动力学参数方面评估MS患者在步态起始阶段的步态模式。检索2019年至2022年12月的PubMed、Cochrane、Science of Direct、Web of Science、Scopus和Google学者数据库。研究包括:患有多发性硬化症的个体，检查步态起始阶段，评估压力中心(COP)，预期姿势调整(APA)，时空，运动学和动力学参数以及肌肉协同作用作为评估结果的指标。纳入的研究由两位作者独立审查，使用修改的Downs和Black检查表进行质量评估。由于荟萃分析是不可能的，研究是描述性的。最终选择了10篇文章进行分析。研究共纳入231例MS患者和132名健康受试者，年龄从18岁到76岁不等。研究结果显示，与健康参与者相比，MS患者在步态开始时的COP位置更靠前，每个关节的净肌力矩在APA阶段有显著差异，且变化更平稳。据报道，APA期间肌肉活动量较低。与健康人相比，多发性硬化症患者的第一步要短一些。在MS早期阶段的步态起始可能是表征平衡的有用措施。这些结果表明，步态启动评估可以作为这些患者康复治疗的有效方法。
{"title":"Examining the gait pattern in terms of spatio-temporal, kinematic and kinetic parameters during gait initiation in MS patients","authors":"Narges Jahantigh Akbari, Mohammad Yousefi","doi":"10.1016/j.gaitpost.2023.07.110","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.110","url":null,"abstract":"MS is an autoimmune disease of the central nervous system (1). Postural balance impairment is frequently depicted as one of the initial symptoms of MS and one of the most impairing MS symptoms (2, 3). the gait impairment is one of the most common indications of MS and influences the quality of life (4). Although investigation of the quiet stance is critical for analyzing the balance impairments, more challenging situations such as gait initiation (GI) may give a deeper knowledge into understanding of the mechanisms of dynamic postural control (5). Therefore, the aim of this study was to evaluate the gait pattern in terms of spatio-temporal, kinematic and kinetic parameters during the gait initiation phase in people with MS. What are the influencing factors on the gait pattern in gait initiation phase in MS patients? Search in PubMed, Cochrane, Science of Direct, Web of science, Scopus, and Google scholar Databases from 2019 to December 2022 was conducted. Studies were included if: individuals with multiple sclerosis disease, examination the gait initiation phase, and assessment of center of pressure(COP), anticipatory postural adjustments (APA), spatio-temporal, kinematic and kinetic parameters and muscular synergies as outcome measure were evaluated. Included studies were independently reviewed by two authors for their quality evaluation using modified Downs and Black checklist. Since meta-analysis was not possible, studies were descriptively presented. Ten articles were selected for final analysis. A total of 231 MS patients and 132 healthy subjects were included with ages ranging from 18 to 76 years old. The findings of studies showed COP position at gait initiation was more anterior and net muscular moments for each joint were significantly different during the APA phase with smoother variations in patients with MS than healthy participants. A lower amount of muscle activity during APA were reported. The first step was shorter in MS patients compared to healthy people. Gait initiation in the early stage of MS could be a useful measure to characterize balance. These results suggest that it is possible to use the gait initiation evaluation as an effective method for the rehabilitation treatment of these patients.","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":"135298044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The human body is a complex system of interconnected muscles and joints that work together to produce movement. The hip joint is particularly important in this regard, as it is responsible for supporting the weight of the body and facilitating movement of the legs. The flexor and extensor muscles of the hip joint play a crucial role in this process, as they are responsible for moving the leg forward and backward, respectively. Does the swing of the arm affect the activity of the flexor and extensor muscles of the hip joint?Download : Download high-res image (89KB)Download : Download full-size image To investigate the relationship between different swings of the arm and the effect on the flexor and extensor muscles of the hip joint, we utilized the OpenSim software. We created a model of the human body in OpenSim by motion capture data and used it to measuring the activity of the flexor and extensor muscles of the hip joint. sample consisted of 20 healthy adults (10 males, 10 females) between the ages of 18 and 30. All participants were free of any musculoskeletal disorders or injuries that could affect their hip joint function. Results showed that there was a significant difference in muscle activity between arm swings. Specifically, when the arm was swung forward, the activity in the flexor muscles of the hip joint was significantly higher than when the arm was swung backward (p < 0.001). Conversely, when the arm was swung backward, the activity in the extensor muscles of the hip joint was significantly higher than when the arm was swung forward (p < 0.001). We also found that the difference in muscle activity between arm swings was more pronounced in males compared to females (p = 0.03). This suggests that there may be gender-specific differences in the way the hip joint muscles are activated during arm swings. We studied the three types of long, short and normal swing (p = 0.02). These findings provide valuable insight into the relationship between arm swings and hip joint muscle activation, which can be useful for individuals seeking to optimize their hip joint function. This study provides insight into the relationship between different arm swings and the activity of the flexor and extensor muscles of the hip joint. Arm swings can have a significant effect on the activation of these muscles, and this effect may differ between males and females. This information can be useful for individuals seeking to optimize their hip joint function, such as athletes or those undergoing rehabilitation after hip joint injuries.
{"title":"Modeling of different arm swing and the effect on hip flexors and extensors","authors":"Sadegh Madadi, Mostafa Rostami, Afshin Taheri Azam","doi":"10.1016/j.gaitpost.2023.07.145","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.145","url":null,"abstract":"The human body is a complex system of interconnected muscles and joints that work together to produce movement. The hip joint is particularly important in this regard, as it is responsible for supporting the weight of the body and facilitating movement of the legs. The flexor and extensor muscles of the hip joint play a crucial role in this process, as they are responsible for moving the leg forward and backward, respectively. Does the swing of the arm affect the activity of the flexor and extensor muscles of the hip joint?Download : Download high-res image (89KB)Download : Download full-size image To investigate the relationship between different swings of the arm and the effect on the flexor and extensor muscles of the hip joint, we utilized the OpenSim software. We created a model of the human body in OpenSim by motion capture data and used it to measuring the activity of the flexor and extensor muscles of the hip joint. sample consisted of 20 healthy adults (10 males, 10 females) between the ages of 18 and 30. All participants were free of any musculoskeletal disorders or injuries that could affect their hip joint function. Results showed that there was a significant difference in muscle activity between arm swings. Specifically, when the arm was swung forward, the activity in the flexor muscles of the hip joint was significantly higher than when the arm was swung backward (p < 0.001). Conversely, when the arm was swung backward, the activity in the extensor muscles of the hip joint was significantly higher than when the arm was swung forward (p < 0.001). We also found that the difference in muscle activity between arm swings was more pronounced in males compared to females (p = 0.03). This suggests that there may be gender-specific differences in the way the hip joint muscles are activated during arm swings. We studied the three types of long, short and normal swing (p = 0.02). These findings provide valuable insight into the relationship between arm swings and hip joint muscle activation, which can be useful for individuals seeking to optimize their hip joint function. This study provides insight into the relationship between different arm swings and the activity of the flexor and extensor muscles of the hip joint. Arm swings can have a significant effect on the activation of these muscles, and this effect may differ between males and females. This information can be useful for individuals seeking to optimize their hip joint function, such as athletes or those undergoing rehabilitation after hip joint injuries.","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":"135298051","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.2023.07.274
Qiang Zhang, Leichao Liang, Weiping Zhu, Xie Wu
Postural compensations with backpack may cause considerable body strains, resulting in fatigue, pain, and injury [1]. Backpack’s position can influence carrier’s posture and dynamic performance [2]. Characterizing the backpack’s position, namely the position of its centre of mass (COM) with respect to the carrier’s body, allows modelling its dynamic loading towards revealing the moment and moment of inertia it renders on the carrier. These knowledges will provide novel insights into the carrier’s postural compensations and musculoskeletal injury [3]. Despite of the importance, there is a lack of an easy approach that can determine and track the movement of a backpack’s COM during dynamic activities. How to determine the position of a backpack’s COM and track its movements in walking with the backpack? A backpack was tightly filled with sandbags, resulting in a total weight of 10 kg. Using a 3D motion capture system (Vicon, UK), we created the backpack’s local coordinate system (CS) with the three reflective markers attached on it. A directional cosine matrix was established for coordinate transformations between the backpack’s and the lab’s CS. A mannequin was then placed on an integrated force plate (Kistler, Switzerland), and its weight and centre of pressure were measured. This measurement was repeated after placing the backpack on the mannequin (two positions, Fig. 1a), and the horizontal coordinates of the backpack’s COM were calculated according to the Varignon's Theorem. Fig. 1. Experiments and outcomes: a) Measuring centre of pressure in backpack’s two postures; b) Displacement of backpack’s and subject’s COM during walking. Download : Download high-res image (81KB)Download : Download full-size image As the coordinates of the backpack’s COM in the backpack’s local CS remained unchanged, an equation could be established to calculate the vertical coordinate of the backpack’s COM with its horizontal coordinates. Finally, the coordinates of the backpack’s COM in the backpack’s local CS were determined through coordinate transformation. Afterwards, a healthy young subject was instrumented with full-body marker set, and then performed walking with the backpack at 5 km/h. Using Visual 3D (C-Motion, USA), a virtual marker was created according to above outcomes, and the marker’s movements were computed from the gait trials. The results indicated that the vertical displacement magnitude of backpack’s and subject’s COM was similar (Fig. 1b), with a small temporal difference. In the mediolateral direction, the displacement of the backpack’s COM was much greater than that of the subject’s COM. A clear lag effect was observed in their mediolateral displacement during walking, where the backpack’s COM reached its ultimate mediolateral positions later than the subject’s COM did. Our approach can be applied to easily determine a backpack’s COM in 3D motion analysis, towards quantifying backpack’s loading effects and studying carrier’s postural adaptation
使用双肩包进行姿势补偿可能会造成相当大的身体紧张，导致疲劳、疼痛和损伤。背包的位置会影响背包携带者的姿势和动态性能。描述背包的位置，即它的质心(COM)相对于载体的身体的位置，允许建模它的动态负载，以揭示它在载体上呈现的力矩和惯性矩。这些知识将为携带者的姿势代偿和肌肉骨骼损伤提供新的见解。尽管这很重要，但在动态活动期间，缺乏一种简单的方法来确定和跟踪背包的COM的运动。如何确定一个背包的COM的位置和跟踪它的运动与背包走路?背包里塞满了沙袋，总重量达10公斤。使用3D动作捕捉系统(Vicon, UK)，我们用三个反射标记创建了背包的本地坐标系统(CS)。建立了一个方向余弦矩阵，用于背包和实验室CS之间的坐标转换。然后将一个人体模型放在一个综合力板上(奇石乐，瑞士)，测量其重量和压力中心。将背包放在人体模型上(两个位置，图1a)后重复测量，并根据瓦里农定理计算背包的COM的水平坐标。图1所示。实验与结果:a)测量背包两种姿势的压力中心;b)行走过程中背包和受试者COM的位移。下载:下载高分辨率图片(81KB)下载:下载全尺寸图片在背包局部CS中的COM坐标不变的情况下，可以建立方程，计算出背包COM与背包横坐标的纵坐标。最后，通过坐标变换确定背包的COM在背包局部CS中的坐标。随后，对健康的年轻受试者进行全身标记设置，然后以5 km/h的速度背着背包行走。使用Visual 3D (C-Motion, USA)软件，根据上述结果创建虚拟标记，并根据步态试验计算标记的运动。结果表明，背包和受试者COM的垂直位移幅度相似(图1b)，但时间差异较小。在中外侧方向上，背包的COM的位移远远大于受试者的COM。在行走过程中，在他们的中外侧位移中观察到明显的滞后效应，背包的COM到达最终的中外侧位置比受试者的COM晚。该方法可以在三维运动分析中方便地确定背包的COM，量化背包的载荷效应，研究背包的姿态适应和控制策略。
{"title":"A novel method for tracking movements of backpack’s centre of mass in dynamic activities","authors":"Qiang Zhang, Leichao Liang, Weiping Zhu, Xie Wu","doi":"10.1016/j.gaitpost.2023.07.274","DOIUrl":"https://doi.org/10.1016/j.gaitpost.2023.07.274","url":null,"abstract":"Postural compensations with backpack may cause considerable body strains, resulting in fatigue, pain, and injury [1]. Backpack’s position can influence carrier’s posture and dynamic performance [2]. Characterizing the backpack’s position, namely the position of its centre of mass (COM) with respect to the carrier’s body, allows modelling its dynamic loading towards revealing the moment and moment of inertia it renders on the carrier. These knowledges will provide novel insights into the carrier’s postural compensations and musculoskeletal injury [3]. Despite of the importance, there is a lack of an easy approach that can determine and track the movement of a backpack’s COM during dynamic activities. How to determine the position of a backpack’s COM and track its movements in walking with the backpack? A backpack was tightly filled with sandbags, resulting in a total weight of 10 kg. Using a 3D motion capture system (Vicon, UK), we created the backpack’s local coordinate system (CS) with the three reflective markers attached on it. A directional cosine matrix was established for coordinate transformations between the backpack’s and the lab’s CS. A mannequin was then placed on an integrated force plate (Kistler, Switzerland), and its weight and centre of pressure were measured. This measurement was repeated after placing the backpack on the mannequin (two positions, Fig. 1a), and the horizontal coordinates of the backpack’s COM were calculated according to the Varignon's Theorem. Fig. 1. Experiments and outcomes: a) Measuring centre of pressure in backpack’s two postures; b) Displacement of backpack’s and subject’s COM during walking. Download : Download high-res image (81KB)Download : Download full-size image As the coordinates of the backpack’s COM in the backpack’s local CS remained unchanged, an equation could be established to calculate the vertical coordinate of the backpack’s COM with its horizontal coordinates. Finally, the coordinates of the backpack’s COM in the backpack’s local CS were determined through coordinate transformation. Afterwards, a healthy young subject was instrumented with full-body marker set, and then performed walking with the backpack at 5 km/h. Using Visual 3D (C-Motion, USA), a virtual marker was created according to above outcomes, and the marker’s movements were computed from the gait trials. The results indicated that the vertical displacement magnitude of backpack’s and subject’s COM was similar (Fig. 1b), with a small temporal difference. In the mediolateral direction, the displacement of the backpack’s COM was much greater than that of the subject’s COM. A clear lag effect was observed in their mediolateral displacement during walking, where the backpack’s COM reached its ultimate mediolateral positions later than the subject’s COM did. Our approach can be applied to easily determine a backpack’s COM in 3D motion analysis, towards quantifying backpack’s loading effects and studying carrier’s postural adaptation","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":"135298192","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.2023.07.206
Arik Rehani Musagara, Marco Götze, Sebastian I. Wolf
Transversally measured hip rotation can add valuable information in the indication of femoral derotation osteotomy (FDO) (Dreher 2007), which serves as the gold standard in the treatment of internally rotated gait. Typically, studies on gait analyses in the context of FDO relied on the conventional calibration of the knee joint axis (KJA) based on palpation of the epicondyles. However, researchers regularly face discrepancies between planned FDO angle, pre-post changes in anteversion and pre-post changes in hip rotation (Böhm 2015, Putz 2016). Apart from previously discussed factors, we hypothesized that a functional calibration of the KJA might lead to smaller differences between aforementioned parameters and therefore provide more coherent results than the conventional method. Does a functional KJA calibration allow for smaller differences between intraoperative FDO angle and pre-post changes in mean hip rotation in stance (mHipRotSt)? 14 patients (mean age at surgery: 16.2 ± 9.5 years) scheduled for FDO were examined retrospectively in this study. 3D gait analysis including functional KJA calibration and rotational MRIs (available in 8 of 14 patients) for estimating anteversion were measured pre- (1 day) and post-FDO (11.7 ± 3.1 months). Functional calibration included three unassisted, unloaded knee flexion-extension movements in single limb stance and were repeated for both legs. Subsequently the SARA algorithm (Ehrig 2007) was applied. Conventional estimation of the KJA was done with the Knee Alignment Device method. FDO angle was documented during surgery and was measured intraoperatively with a goniometer. Postoperative mHipRotSt was significantly smaller (p <0.001) for both conventional and functional method than before surgery (Table 1). A significant, high correlation was observed between the pre-post FDO change of conventionally measured mHipRotSt and intraoperative external FDO angle (r = 0.62, p < 0.01). For the functionally measured change in mHipRotSt a non-significant, moderate correlation (r = 0.41, p = 0.1) was found. Discrepancies between change in mHipRotSt and mean FDO angle/ change in anteversion were 3.4°/ 1.6° conventionally and 10.4°/ 8.4° functionally measured. Correlation analysis between preoperative Range of motion (ROM) during calibration movement and the change in mHipRotSt revealed a weak, non-significant correlation (r = 0.16, p = 0.549).Download : Download high-res image (110KB)Download : Download full-size image The conventional method is more in alignment with the aimed intraoperative FDO and therefore appears as the preferable option in the decision-making process in the context of FDO. Also changes in the anteversion were closer to conventionally measured values. Analysis on the presumably more restricted ROM did not indicate that it had a strong influence on the differences between pre- and postoperative mHipRotSt.
横向测量髋关节旋转可以为股骨旋转截骨术(FDO)的适应症提供有价值的信息(Dreher 2007)，这是治疗内旋步态的金标准。通常，在FDO背景下的步态分析研究依赖于传统的基于触诊上髁的膝关节轴(KJA)校准。然而，研究人员经常面临计划的FDO角度、前倾前后变化和髋关节旋转前后变化之间的差异(Böhm 2015, Putz 2016)。除了前面讨论的因素外，我们假设KJA的功能校准可能导致上述参数之间的差异较小，因此比传统方法提供更连贯的结果。功能性KJA校准是否允许术中FDO角度与髋位平均旋转(mHipRotSt)前后变化之间的较小差异?本研究回顾性分析了14例计划行FDO的患者(平均手术年龄:16.2±9.5岁)。在fdo前(1天)和fdo后(11.7±3.1个月)测量3D步态分析，包括功能性KJA校准和旋转mri(14例患者中有8例可用)，用于估计前倾。功能校准包括三个无辅助、无负荷的单肢站立膝关节屈伸运动，并在两条腿上重复。随后应用SARA算法(Ehrig 2007)。传统的KJA估计是用膝关节对齐装置方法完成的。术中记录FDO角，术中用测角仪测量。与术前相比，常规方法和功能方法术后mHipRotSt均显著小于术前(p <0.001)(表1)。常规测量的mHipRotSt的FDO前后变化与术中外部FDO角度之间存在显著的高度相关性(r = 0.62, p < 0.01)。对于功能性测量的mHipRotSt变化，发现不显著的中度相关性(r = 0.41, p = 0.1)。mHipRotSt变化与FDO平均角度/前倾变化之间的差异为常规测量的3.4°/ 1.6°和功能测量的10.4°/ 8.4°。校正运动时术前活动范围(ROM)与mHipRotSt变化的相关分析显示，相关性较弱，不显著(r = 0.16, p = 0.549)。下载:下载高分辨率图像(110KB)下载:下载全尺寸图像传统方法更符合术中FDO的目标，因此在FDO的决策过程中似乎是更可取的选择。前倾的变化也更接近常规测量值。对可能更受限的ROM的分析并没有表明它对术前和术后mHipRotSt的差异有很强的影响。
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