Musculoskeletal simulation and dynamic modeling programs have been used to gain insight into lower-limb musculoskeletal injury mechanisms. In addition to the temporospatial, kinematic, and kinetic data obtained from motion analysis systems, musculoskeletal simulation programs also can provide information on joint contact and muscle forces, musculotendinous lengths and velocities, and muscle activation levels. Musculoskeletal simulation platforms may help in the assessment of risk factors for sports-related injuries. Using musculoskeletal simulations for injury prevention programs may help lower the incidence of sports injuries, and may allow for fast recovery from injury. In this review, injury mechanisms and risk factors of some of the most common lower-limb musculoskeletal injuries, including anterior cruciate ligament, patellofemoral, and hamstring injuries were summarized from a biomechanical perspective. Also, the efficacy of musculoskeletal modeling and dynamic simulation tools in helping our understanding of these injury mechanisms was discussed.

Line following robots have ability to track a given path autonomously using feedback mechanisms. The path is usually a black line on a white surface or a white line on a black surface. Today, line following robots are used in medical, industrial and automotive industries. Therefore, the studies on the line following robots have been increased recently. In this study, a robust, non-chattering sliding mode control (SMC) is designed and applied for a line following robot. The mobile robot is designed to sense the straight or curved path with its infrared sensors mounted on the robot. Therefore, these infrared sensors provide continuous streaming of the defined path to guide or direct changes in robot by activating motors on right wheel or/and left wheel. The control strategy is curial to track complex paths accurately and to have a fast, stable and accurate line following robot. Thus, for comparison, conventional proportional-integral-derivative (PID) is also applied to robot. The main purpose of this study is to investigate performance of sliding mode control during path tracking. For this, numerical solution and experimental study were carried out. From the results, it was understood that sliding mode controller is highly efficient in tracking the path.

Inverse dynamics analysis is commonly used to estimate the net loads at a joint during human motion. Most lower‐limb models of movement represent the knee as a simple hinge joint when calculating muscle forces. This approach is limited because it neglects the contributions from tibiofemoral joint contact forces and may therefore lead to errors in estimated muscle forces. The aim of this study was to quantify the contributions of tibiofemoral joint contact loads to the net knee loads calculated from inverse dynamics for multiple subjects and multiple gait patterns. Tibiofemoral joint contact loads were measured in four subjects with instrumented implants as each subject walked at their preferred speed (normal gait) and performed prescribed gait modifications designed to treat medial knee osteoarthritis. Tibiofemoral contact loads contributed substantially to the net knee extension and knee adduction moments in normal gait with mean values of 16% and 54%, respectively. These findings suggest that knee‐contact kinematics and loads should be included in lower‐limb models of movement for more accurate determination of muscle forces. The results of this study may be used to guide the development of more realistic lower‐limb models that account for the effects of tibiofemoral joint contact at the knee. © 2015 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 33:1054–1060, 2015.

Earthquake-induced soil liquefaction represents a critical geotechnical challenge due to its nonlinear soil–seismic interactions and its impact on structural safety. Traditional empirical methods often rely on simplified assumptions, limiting their predictive capability. This study develops and compares six machine learning (ML) classifiers—namely, Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB)—to evaluate liquefaction susceptibility using an original dataset of 461 soil layers obtained from borehole penetration tests in the Edremit region (Balıkesir, NW Turkey). The models were trained and validated using normalized geotechnical and seismic parameters, and their performance was assessed based on accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUC). Results demonstrate that SVM, ANN, and kNN consistently outperformed other models, achieving test accuracies above 93%, F1 scores exceeding 98%, and AUC values between 0.933 and 0.953. In contrast, DT and NB exhibited limited generalization (test accuracy of 84–88% and AUC of 0.78–0.82), while RF showed partial overfitting. In contrast, DT and NB exhibited weaker generalization, with test accuracies of 84% and 88% and AUC values of 0.78 and 0.82, respectively, while RF indicated partial overfitting. The findings confirm the superior capability of advanced ML models, particularly SVM, ANN, and kNN, in capturing complex nonlinear patterns in soil liquefaction. This study provides a robust framework and original dataset that enhance predictive reliability for seismic hazard assessment in earthquake-prone regions.

Endüstri ve tıp alanında, kendi kendini dengeleyen robotlar hakkında tüm dünyada yapılan çalışmalar son yıllarda hız kazanmıştır. Bu tip robotların kullanım alanının genişlemesiyle birlikte karşılaşılan en büyük sorunun, robotların kontrol mekanizmaları olduğu anlaşılmaktadır. Yapıları gereği kontrol edilmesi zor olan bu robotlar için literatürde çok sayıda çalışma bulunmaktadır. Bu çalışmalar incelendiğinde klasik kontrol metotlarının, bu tip robotları kontrol etmekte yetersiz kaldığı görülmektedir. Fakat klasik kontrolcüler kullanılarak geliştirilen yeni nesil karma kontrol metotlarının robotlara uygulanmasıyla birlikte robotların istenilen hareketleri daha hızlı ve kararlı bir şekilde gerçekleştirdiği gözlemlenmektedir. Bu nedenle, klasik kontrol metotlarından PID kontrolcü parametrelerinin, bulanık mantıklı kontrol metodu ile belirlenerek oluşturulan karma bir kontrol metodu bu çalışmada önerilmekte ve kendi kendini dengeleyen robota uygulanmaktadır. Ayrıca yapılan çalışmalar incelendiğinde, sadece kendi kendini dengeleyen robotların analizinin gerçekleştirildiği görülmektedir. Yapılan bu çalışmada sürücü de sisteme dahil edilerek analiz edilmiştir. İlk olarak iki tekerlekli kendi kendini dengeleyen robot modellenmiş ve bu sistem için sadece PID (oransal, integral ve türevsel) kontrolcü ve Bulanık Mantık-PID (Fuzzy Logic-PID) kontrolcü tasarlanmıştır. Bulanık Mantık-PID kontrolcünün değişken kazanım katsayılarına sahip olması klasik kontrolcülerden daha hızlı tepki vermesini sağlamaktadır. PID ve Bulanık Mantık-PID kontrolcüler ile elde edilen benzetim sonuçları karşılaştırılmıştır. Çalışmanın sonunda Bulanık Mantık-PID kontrolcünün, kontrolcü kazanımlarını kendi kendine ayarlayabildiği ve istenilen hız ve pozisyona ulaşmakta başarılı bir performansa sahip olduğu anlaşılmıştır.

Passive ankle exoskeleton models often assume a constant spring moment arm. Here spring rate, two attachment offsets and clutch threshold were optimized jointly with a geometry-derived moment arm. Data from 19 adults walking at 1.2 m s⁻¹ drove a reduced plantarflexor model with fixed unassisted kinematics; force, travel and moment limits were checked on every stride. In the optimized geometry the moment arm fell 31% between engagement and peak dorsiflexion; a constant-lever approximation overestimated the device moment there by 71%. With active force and travel limits, the spring rate equalled their ratio and several attachment geometries were nearly equivalent (300–1500 N). At a 1000 N limit, the model predicted a 25.7% reduction in its priced plantarflexor energy (muscle-level, not whole-body), ranging from 15% to 40% across muscle-model variants and ±1%-of-cycle timing shifts (central 95% simulation interval 23.3–29.2% under one-frame timing errors). Among five bio-inspired optimizers, covariance matrix adaptation evolution strategy (CMA-ES) was the most reliable population-based method on the single-objective problem. Non-dominated sorting genetic algorithm II (NSGA-II) and S-metric selection evolutionary multi-objective algorithm (SMS-EMOA) approximated the benefit–force trade-off more completely than a budget-matched ε-constraint sweep, whereas constrained optimization by linear approximations (COBYLA) was better in the moment-limited region.