Yazar "Wolf, Sebastian Immanuel" seçeneğine göre listele

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    Estimating ground reaction forces from gait kinematics in cerebral palsy : A convolutional neural network approach
    (Springer, 2024) Arslan, Yunus Ziya; Özateş, Mustafa Erkam; Salami, Firooz; Wolf, Sebastian Immanuel
    Purpose While gait analysis is essential for assessing neuromotor disorders like cerebral palsy (CP), capturing accurate ground reaction force (GRF) measurements during natural walking presents challenges, particularly due to variations in gait patterns. Previous studies have explored GRF prediction using machine learning, but specifc focus on patients with CP is lacking. This research aims to address this gap by predicting GRF using joint angles derived from marker data during gait in patients with CP, thereby suggesting a protocol for gait analysis without the need for force plates. Methods The study employed an extensive dataset comprising both typically developed (TD) subjects (n=132) and patients with CP (n=622), captured using motion capture systems and force plates. Kinematic data included lower limb angles in three planes of motion, while GRF data encompassed three axes. A one-dimensional convolutional neural network model was designed to extract features from kinematic time series, followed by densely connected layers for GRF prediction. Evaluation metrics included normalized root mean squared error (nRMSE) and Pearson correlation coefcient (PCC). Results GRFs of patients with CP were predicted with nRMSE values consistently below 20.13% and PCC scores surpassing 0.84. In the TD group, all GRFs were predicted with higher accuracy, showing nRMSE values lower than 12.65% and PCC scores exceeding 0.94. Conclusion The predictions considerably captured the patterns observed in the experimentally obtained GRFs. Despite limitations, including the absence of upper extremity kinematics data and the need for continuous model evolution, the study demonstrates the potential of machine learning in predicting GRFs in patients with CP, albeit with current prediction errors constraining immediate clinical applicability.
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    Machine learning-based prediction of joint moments based on kinematics in patients with cerebral palsy
    (Journal of Biomechanics, 2023) Arslan, Yunus Ziya; Özateş, Mustafa Erkam; Karabulut, Derya; Wolf, Sebastian Immanuel; Salami, Firooz
    Joint moments during gait provide valuable information for clinical decision-making in patients with cerebral palsy (CP). Joint moments are calculated based on ground reaction forces (GRF) using inverse dynamics models. Obtaining GRF from patients with CP is challenging. Typically developed (TD) individuals’ joint moments were predicted from joint angles using machine learning, but no such study has been conducted on patients with CP. Accordingly, we aimed to predict the dorsi-plantar flexion, knee flexion–extension, hip flexion–extension, and hip adduction-abduction moments based on the trunk, pelvis, hip, knee, and ankle kinematics during gait in patients with CP and TD individuals using one-dimensional convolutional neural networks (CNN). The anonymized retrospective gait data of 329 TD (26 years ± 14, mass: 70 kg ± 15, height: 167 cm ± 89) and 917 CP (17 years ± 9, mass:47 kg ± 19, height:153 cm ± 36) individuals were evaluated and after applying inclusion–exclusion criteria, 132 TD and 622 CP patients with spastic diplegia were selected. We trained specific CNN models and evaluated their performance using isolated test subject groups based on normalized root mean square error (nRMSE) and Pearson correlation coefficient (PCC). Joint moments were predicted with nRMSE between 18.02% and 13.58% for the CP and between 12.55% and 8.58% for the TD groups, whereas with PCC between 0.85 and 0.93 for the CP and between 0.94 and 0.98 for the TD groups. Machine learning-based joint moment prediction from kinematics could replace conventional moment calculation in CP patients in the future, but the current level of prediction errors restricts its use for clinical decision-making today.
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    Makine öğrenmesi ile kinematik veriden serebral palsili hastalarınyürüyüş karakteristiğinin analizi
    (2023) Arslan, Yunus Ziya; Özateş, Mustafa Erkam; Wolf, Sebastian Immanuel
    Serebral palsi (SP), nöromotor fonksiyon bozuklarına dayanan bir rahatsızlıktır ve özellikle yürüme kinematiğini etkileyebilir. SP'li hastaların hareket yeteneklerini iyileştirmek için kişiye özgü değerlendirmelere ihtiyaç vardır. Bu değerlendirmeler için laboratuvarlarda kullanılan yer tepki kuvveti (YTK) ölçümleri, SP hastalarının tedavisinde önemli bir rol oynar. Ancak YTK'nin doğal yürüyüş sırasında ölçülmesi zor ve donanım gerektirdiği için maliyetlidir. Bu çalışmada SP'li hastaların YTK'lerini kuvvet platformlarına ihtiyaç duymadan tahmin etmek için makine öğrenimi kullanılarak SP hastalarının yürüyüş analizi için yeni bir yaklaşım sunulmaktadır.Yöntem:Araştırma, 40 sağlıklı birey ve 40 SP hastasının yürüme verisi kullanılmıştır. İlk olarak, bir boyutlu konvolüsyonel sinir ağına (CNN) girdi olarak kullanılan kinematik veriler aracılığıyla sağlıklı ve SP'li bireylerin sınıflandırılabilmesi için bir model geliştirilmiştir. Ardından, SP'li hastaların kinematik verileri ikinci bir CNN modeline beslenerek YTK tahmin edilmiştir. Bulgular:SP hastalarının ve sağlıklı bireylerin yüksek doğruluk oranlarıyla (%98) sınıflandırılabilmesi sağlanmıştır. Ayrıca, SP'li hastalarda YTK, %13.3’lük (± 5.1) normalize edilmiş ortalama karesel hatave 0.88’lik ± (0.07) Pearson korelasyon katsayısı oranları ile kestirilebilmiştir. Bu sonuçlar, laboratuvar ortamında YTK ölçümü yapılamayan hastalar için önemlidir. Sonuç:Bu çalışmada, SP hastalarının tedavisinde kullanışlı ve hızlı bir hareket analizi yöntemi sunulmuş ve SP hastalarının tedavi süreçlerini geliştirmeye yardımcı olacak bir yöntem uygulanmıştır. Bununla birlikte çalışmadan elde edilen sonuçların klinik karar verme süreçleri açısından hataya yer bırakmayacak bir dikkatle ele alınması gerekmektedir.
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    Patellar tendon advancement for the treatment of crouch gait in patients with cerebral palsy
    (2023) Karabulut, Derya; Arslan, Yunus Ziya; Salami, Firooz; Wolf, Sebastian Immanuel; Goetze, Marco
    Crouch gait is a frequent gait pathology in children with cerebral palsy (CP). Patellar tendon advancement (PTA) is a typical surgery among single-event multilevel surgery (SEMLS) for the treatment of crouch gait and performed to enhance the knee joint kinematics and kinetics Patella position had a considerable effect on the torque-producing capacity of the quadriceps and hence, patellar height is a typical metric to assess the influence of PTA on knee function. Patellar height can be assessed by using several radiographic imaging methods. How ever, in the literature, there is no consensus on objective quantification of the patellar height In our study, we aimed to i) objectively quantify the patella position using X-ray data and musculoskeletal modeling and ii) monitor the effect of PTA in combination with SEMLS on the knee kinematics of patients with CP and crouch gait.