Background:This study aimed to examine the effects of a 12-week cadence control training program on patellofemoral joint mechanics and lower-limb joint work distribution in runners, thereby providing a theoretical basis for optimizing running strategies and reducing running-related injury (RRI) risk. Methods:A total of 44 recreational runners were randomly assigned to a control (n = 22) and an experimental group (EG; n = 22). The former performed conventional running training, while the latter underwent cadence control training for 12 weeks. Three-dimensional (3D) kinematic and kinetic data were collected before and after the intervention using the Simi Motion capture system and Kistler force platforms, respectively. Patellofemoral joint mechanical parameters and joint work contribution ratios were subsequently calculated. Results:Posttraining, the EG exhibited a significant increase in cadence and positive ankle joint work contribution and a significant reduction in patellofemoral joint stress (PFJS), compared with pretraining values. Conclusions:Twelve weeks of cadence control training significantly modified the patellofemoral joint mechanics and joint work distribution in runners. An increased cadence reduced patellofemoral joint loading and induced a distal shift in the relative contribution of lower-limb joint work from proximal to distal segments, which may represent a biomechanical mechanism underlying the alleviation of knee joint load. Therefore, cadence-increasing running strategies are recommended, particularly for runners experiencing patellofemoral pain (PFP), to mitigate the risk of knee joint injury.
This study is the first to extensively analyze the application of artificial intelligence (AI) to human activity recognition (HAR) in the field of sports and exercise through bibliometrics and visualization methods. We used CiteSpace, VOSviewer, and Bibliometrix R‐package software to perform a bibliometrics and visual analysis of 432 articles on the application of AI in HAR in sports and exercise from 2001 to 2024. During the period 2001–2024, the number of publications on AI in HAR in sports and exercise showed a steady and then rapid growth, and the number of articles published after 2018 rose year by year. The top 10 journals have 157 publications, accounting for 36.3%, of which SENSORS ranks first in terms of publications and citations. Jalal and Khan tied for first place in terms of number of publications. China ranked first in the number of publications, accounting for 44.7% of the total. Beijing Sport University and Princess Nora Bint Abdulrahman University are tied for the top spot in terms of the number of articles published in this field. High‐frequency keywords include “deep learning,“ “human activity recognition,” “action recognition,” “machine learning,” “wearable sensors,” and so on. Technical performance analysis of highly cited literature reveals that deep learning models achieve over 90% recognition accuracy on sports‐specific datasets, significantly outperforming traditional methods. Future research should focus on further improving the accuracy and real‐time performance of HAR technology, especially the ability to be applied in complex scenarios. Technical adaptation solutions for scenarios including individual routine sports, team confrontations, and equipment‐involved sports must be refined to overcome practical bottlenecks such as multimodal data synchronization and small‐sample data scarcity. At the same time, interdisciplinary cooperation should be strengthened to combine AI technology with sports science, psychology, physiology, and other fields to explore the application of HAR in emerging fields, while promoting the synergistic optimization between algorithm lightweight and wearable hardware performance. In addition, attention should be paid to AI ethics and data privacy protection to ensure the safety and legality of the technology application, promoting the sustainable development and widespread application of HAR technology in the sports field.
The irregular surface morphology of the turtle shell can be applied to the design of the floating plate of a rice transplanter to mitigate the serious problems of mud and water resistance. An adult Brazilian turtle was selected as the research object in this study. A 7-axis absolute arm measuring machine and Geomagic Studio software were used to acquire and process point cloud data of the turtle’s plastron. Based on the structural characteristics of the turtle shell, four curved surfaces with dense point cloud distributions were segmented, filtered using CATIA software, and exported as three-dimensional coordinate data. MATLAB was used to perform polynomial fitting of the three-dimensional point cloud data. The fitting equations for the four surfaces, as well as the sum of squared errors (SSEs), root mean square error (RMSE), and coefficient of determination (R2), were obtained. The results showed that the maximum relative errors between the fitted and actual values for the front, rear, and side models were 8.96%, 9.36%, and 5.86%, respectively. The corresponding mean relative errors were 4.58%, 4.67%, and 2.98%, respectively. These mean relative errors fall within the ±5% tolerance permitted in engineering design, thereby verifying the validity of the models and enabling the transformation of the turtle plastron surface from a biological form into a mathematical model. This study provides a theoretical foundation for the bionic application of the turtle plastron surface morphology and offers a reference for the bionic design of floating plates for rice transplanters.
Objective: Explore the effects of animal biomimetic training on improving the physical fitness (PF) of track and field athletes, 100 m special test scores, running steps, ankle joint angles, rear stepping angles, angles between the two thighs, swinging legs and thigh forward lifting angles, trunk flexion angles, knee joint angles (KJAs), and tibial contact angles. Methods: Using the randomization principle, 50 track and field players from six sports colleges in Southwest China were chosen and split into an experimental group (EG) and a control group (CG). Among them, the EG received animal biomimetic training, while the CG received conventional training. The effectiveness of animal biomimetic training was analyzed by observing athletes' PF, 100 m special test scores, running steps, ankle joint angles, rear stepping angles, angles between the two thighs, swinging legs and thigh forward lifting angles, trunk flexion angles, KJAs, and tibial contact angles. Results: After animal biomimetic training, the results of sitting forward bending and physical agility tests in the PF index of the EG were significantly different from those of the CG (p<0.05). The ankle joint angles of the athletes in the EG were significantly higher than those of the CG (p<0.001). Both training methods had a positive effect on improving athletes' specialized skills (p<0.05). The rear stepping angle, swinging legs and thigh forward lifting angle, trunk flexion angles, and angle between the two thighs of the athletes in the EG were significantly lower than those of the CG (p<0.05). The KJA and tibial contact angles of the athletes in the EG were significantly lower than those of the CG (p<0.001). Conclusion: Animal biomimetic training can improve the PF of track and field athletes, 100 m special test results, running steps, ankle joint angles, rear stepping angles, angles between the two thighs, swinging legs and thigh forward lifting angles, trunk flexion angles, KJAs, and tibial contact angles. This type of training enhances the specialized skills of track and field athletes and provides technical support for performance increase.
This study uses finite element analysis (FEA) to investigate different muscle activation times on the cervical spine biomechanical responses in pilot ejection. A validated C0-T1 cervical spine model, incorporating vertebrae, intervertebral discs, ligaments, and 13 major active muscles, was subjected to simulated ejection conditions of 10G vertical acceleration over 150 ms. Activation times ranging from 26 to 92 ms were analyzed to evaluate their impact on vertebral rotation, disc stress, and injury risk. Results demonstrated that shorter activation times (26-46 ms) reduced excessive flexion at C3-C4 and C4-C5; these earlier muscle engagements enhance spinal stability. Conversely, longer delays (76-92 ms) increased rotational angles at C5-C7, exacerbating hyperflexion-related injury risks. An optimal activation time of approximately 46 ms minimized flexion without inducing compensatory hyperextension, balancing load distribution across cervical segments. These findings emphasize the critical role of neuromuscular response timing in mitigating cervical spine injuries during high-G ejection. The study provides insights for optimizing pilot safety through tailored muscle activation strategies, sophisticated artificial intelligence (AI) protective equipment design, and training protocols.
Canine animals excel at running and jumping, exhibiting remarkable agility and making them popular bionic models for legged robots. In this study, we utilized motion capture equipment to record a Malinois dog jumping over hurdles and circular holes while running, obtaining motion trajectory data of the trunk, limbs, head, and tail. Subsequently, a digital model of both the Malinois and the environment was constructed, and the limb motion data, as well as the spatial relationships between the dog and the obstacles, were analyzed in both temporal and spatial dimensions. From this analysis, we summarized the behavioral strategies and kinematic patterns underlying the Malinois’ running and obstacle-crossing process. The main findings are as follows: (1) The obstacle-crossing process follows a specific footfall sequence and can be divided into three major phases: takeoff phase, flight phase, and landing phase. (2) During the takeoff phase, the pitch angle of the trunk at liftoff exhibits an arctangent function relationship with the height and distance of the obstacle, while the liftoff velocity is determined by the obstacle height and distance. (3) In the flight phase, the head and tail movements contribute to adjusting trunk posture. (4) During the landing phase, the forelimbs touch the ground first, and the virtual leg formed by the hip and foot generates a spring-like effect upon contact. Finally, we applied data retargeting and optimization methods to reproduce the Malinois’ obstacle-crossing behavior on a quadruped robot, verifying the practicality of our data and research findings. Additionally, we proposed a performance evaluation method for animals and legged robots in jumping over obstacles while running.
Objective: To investigate the effect of the Cross Fit training method on the enhancement of physical fitness qualities of basketball players. Methods: The study selected 100 male students from the 2021 Basketball Specialization Class at Wuhan Sport University as experimental subjects and randomly divided them into an experimental group (Group A, n = 50) and a control group (Group B, n = 50). Group A uses a combination of regular basketball training and CrossFit training, while Group B only uses the regular basketball training mode. The experimental period is 16 weeks, with three training sessions per week, each lasting 40 min. Before and after the experiment, endurance, explosive power, maximum muscle strength, body composition, and sensitivity were tested on two groups of subjects. Results: After 16 weeks of training, the experimental group athletes achieved significant improvement in all tests. In terms of endurance, the average improvement in the 10,000 m run and 12 min run is about 5% and 7%, respectively. In terms of explosive power, the average improvement in standing long jump, standing vertical jump, and run-up height was about 5.4%, 4.4%, and 3.8%, respectively. In terms of maximum muscle strength, the average increase in maximum load for squats, hard pulls, and bench presses is about 5%, 13.5%, and 8%, respectively. The 3/4 field sprint and T-shaped agility test were shortened by similar to 23.3% and 7.5%, respectively. The above statistical differences were significant (p < 0.001). Although the control group athletes showed some improvement in most of the above indicators, the magnitude of the improvement was significantly lower than that of the experimental group, and the statistical difference was not significant (p > 0.05). Conclusion: Cross Fit training mode is significantly better than the conventional basketball training mode. It is feasible and reliable to introduce the Cross Fit training method into the training of basketball players, and it can effectively improve the athletes' physical fitness.
Wearable robots for rehabilitation have dramatically advanced the medical field regarding helping patients suffering from lower limb impairments to regain mobility and ameliorate their range of motion (ROM). However, to further optimize control mechanisms within these robots, conventional methods cannot adapt to the different needs of patients in their walking, and the complex patterns of human gait. As a result of such limitations, the functionality of ROM training is constricted. To cope with these problems, the present article will design and validate an adaptive control system for the lower limbs with Particle Swarm Optimization (PSO): Model Reference Adaptive Control (MRAC)-PSO. By introducing the adaptation mechanism of MRAC-PSO, as well as its capabilities in optimization, this research aims to further improve the adaptability and effectiveness in ROM training to provide a better rehabilitation process for the patient. Moreover, the performance of the MRAC-PSO controller is compared with that of a traditional MRAC system and a classical Proportional-Integral-Derivative controller optimized via the Ziegler-Nichols (Z-N) method. This comparison is performed to underline the benefits and possible improvements brought by the adaptive and optimized control approach. The novelty of this article lies in the first systematic integration of PSO optimization with MRAC for wearable lower limb rehabilitation (WLLR) ROM training, achieving significant performance improvements over conventional methods: 89% faster risetime and 98.9% lower steady-state error (SSE) compared to PID-ZN control. This research advances wearable robotics by demonstrating that the synergy between adaptive control and bioinspired optimization can substantially improve rehabilitation robot performance, safety, and clinical viability. The synthesis and overcoming analysis of MRAC-PSO, conventional MRAC, and PID-ZN controllers are carried out with the aim to overcome existing ROM training deficiencies, making the rehabilitation strategy more adaptable and effective. In this regard, this research outcome would likely open ways for further development in rehabilitation technology to improve the living standard of people suffering from lower limb disabilities.
Purpose The full-body kinematic chain plays a crucial role in golf swings, and trunk muscle fatigue may significantly affect swing mechanics. This study aimed to investigate how trunk muscle fatigue influences trunk and lower limb kinematics during golf swings. Methods Eleven healthy adult golfers (mean age: 20.3 +/- 0.8 years) participated in a pre-post experimental study conducted in a biomechanics laboratory with a grass-simulated hitting surface. Participants performed golf swings with a 7-iron before and after undergoing a trunk muscle fatigue protocol involving plank exercises. Three-dimensional kinematic data of the trunk and lead-side lower limbs were recorded. Results Following the fatigue protocol, trunk sagittal plane stability significantly decreased (pre: 47.9 degrees, post: 43.4 degrees, p < 0.01). Ankle range of motion also declined in both the sagittal plane (34.5 degrees to 30.0 degrees, p < 0.05) and frontal plane (15.8 degrees to 12.3 degrees, p < 0.01). Although total lower limb joint moments remained unchanged, the relative contribution of frontal-plane ankle moments significantly decreased (15.6% 12.6%, p < 0.05). Conclusion Trunk muscle fatigue reduces trunk stability and ankle mobility during golf swings. Increased lower limb stiffness may act as a compensatory strategy to maintain swing mechanics under fatigued conditions.
In recent years, small ground robots have demonstrated significant potential in the exploration of complex environments and in rescue operations. However, the rigid structure of conventional robots affects the adaptability and popularity of robots. The research team was inspired by features such as the miniaturization and transport costs of origami mechanisms. Simultaneously considering the high flexibility and self-stability characteristics of a tensegrity structure, a strategy is proposed to integrate the tensegrity structure and the origami mechanism. Based on this integration strategy, this article firstly designs the body structure of an origami robot based on X-type two-bar three-cable. Then the four-bar drive mechanism and variable friction mechanism were designed. Finally, the construction of the origami robot is completed. The experiment results show that the crawling height range of the origami robot is 7.5-10.5 cm, and the adaptive height is 29% of the robot's height. The vertical jump height of the origami robot is 8.0 cm, which is 76% of the robot's height. The integration structure proposed in this article has stable structural characteristics. The origami robot with tensegrity characteristics has good mobility, load-bearing capacity, environmental adaptability, and impact resistance.
Cochlear implant (CI) electrode arrays must navigate the delicate, spiraling microanatomy of the human cochlea. Optimizing their intrinsic mechanical properties is crucial for ensuring smooth surgical insertion and preventing extracochlear buckling. This study presents a parametric, bench-type biomechanical evaluation of tapered cochlear electrode arrays, combining three-dimensional (3D) finite element analysis (FEA) with a design of experiments (DOE) methodology. The array was modeled as a heterogeneous composite, comprising platinum-iridium (Pt-Ir) conductors embedded in a polydimethylsiloxane (PDMS) (silicone) matrix and evaluated as a free-space cantilever under simulated surgical deflection conditions of up to 30°. This approach isolates the intrinsic bending stiffness and longitudinal column strength independent of complex tribological friction. A 15-run factorial design varying apical radius, basal radius, and array length was utilized to quantify their interactive effects on tip deflection and reaction force. The FEA results demonstrated that across the parametric sweeps, maximum tip deflection ranged from 6.16 to 8.27 mm, while the reaction force varied between 1.096 and 4.66 mN. Peak Von Mises stress localized at the fixed basal end at 205.86 MPa, operating safely within the elastic limit of the composite's alloy. Analysis of variance (ANOVA) revealed that array length is the dominant driver of tip deflection, whereas the basal radius governs reaction force due to its fourth-power scaling of the area moment of inertia. Predictive regression models achieved adjusted R-squared values approaching unity; as the experimental runs are derived from deterministic FEA simulations rather than stochastic physical trials, this near-perfect fit reflects exact mathematical mapping of the response surface rather than real-world physical variance. To validate this deterministic numerical framework, the outputs were successfully correlated against previously published experimental data using optical fibers as structural proxies under precision force measurement. Ultimately, these findings provide an efficient, predictive parametric design framework for benchmarking and comparing tapered electrode array geometries, utilizing flexural rigidity and reaction forces as fundamental proxies for safe surgical handling and structural trackability.
Background and Objective: Commercially available motorized prosthetic legs use exclusively non-biological signals to control movements, such as those provided by load cells, pressure sensors, and inertial measurement units (IMUs). Despite that the use of biological signals of neuromuscular origin can provide more natural control of leg prostheses, these signals cannot yet be captured and decoded reliably enough to be used in daily life. Indeed, decoding motor intention from bioelectric signals obtained from the residual limb holds great potential, and therefore the study of decoding algorithms has increased in the past years with standardized methods yet to be established.Methods: In the absence of shared tools to record and process lower limb bioelectric signals, such as electromyography (EMG), we developed an open-source software platform to unify the recording and processing (pre-processing, feature extraction, and classification) of EMG and non-biological signals amongst researchers with the goal of investigating and benchmarking control algorithms. We validated our locomotion decoding (LocoD) software by comparing the accuracy in the classification of locomotion mode using three different combinations of sensors (1 = IMU+EMG, 2 = EMG, 3 = IMU). EMG and non-biological signals (from the IMU and pressure sensor) were recorded while able-bodied participants (n = 21) walked on different surfaces such as stairs and ramps, and this data set is also released publicly along this publication. LocoD was used for all recording, pre-processing, feature extraction, and classification of the recorded signals. We tested the statistical hypothesis that there was a difference in predicted locomotion mode accuracy between sensor combinations using the Wilcoxon signed-rank test.Results: We found that the sensor combination 1 (EMG+IMU) led to significantly more accurate and improved locomotion mode prediction (Accuracy=93.4 ± 3.9) than using EMG (Accuracy= 74.56 ± 5.8) or IMU alone (Accuracy=90.77 ± 4.6) with p-value < 0.001.Conclusions: Our results support previous research and validate the functionality of LocoD as an open-source and modular platform to research control algorithms for prosthetic legs that incorporate bioelectric signals.
Running coordination, quantified using continuous relative phase (CRP) and its variability, plays a key role in adapting to dynamic environments; however, how these measures behave during long-distance running on different surfaces remains unclear. This study compared lower-limb coordination and variability during prolonged running across treadmill and over-ground, focusing on how surface and duration affect movement patterns in sagittal-plane. Eleven healthy adults (nine males) completed 31-min runs at their preferred speed on both surfaces, on separate days, while data were collected using seven Opal Movement Monitoring inertial measurement units. CRP and its variability were examined across two-time intervals (initial and final 5 min) and two running surfaces, both over the full gait cycle and within the stance and swing phases. Overall, running duration and surface did not significantly affect coordination across the full gait cycle. However, ankle-knee coordination increased in the final 5 min during stance. Surface-by-duration interactions were observed in knee-hip and ankle-knee couplings during over-ground running. During the swing phase, ankle-hip coordination increased in the final 5 min on both surfaces, with additional interactions appearing in ankle-hip coupling during treadmill running. Coordination variability showed no significant differences across the gait cycle or within stance and swing phases. These findings suggest that lower-limb coordination patterns, rather than variability, are more sensitive to changes in running duration and surface. The results underscore the importance of considering external running conditions when evaluating coordination and optimizing gait performance in biomechanical assessments.
Objective:This study explores the application of Eurasian eagle-owl wing characteristics to the design of folding wings for flying cars. By analyzing the aerodynamics of the eagle-owl wing, we aim to innovate folding wing configurations to improve lift, reduce drag, enhance flight stability, and ultimately increase the overall energy efficiency and safety of flying cars. Methods:First, a comparative analysis of aerodynamic performance data across multiple owl species was conducted, leading to the selection of the Eurasian eagle-owl wing as the bionic prototype. Then, reverse engineering modeling was performed using image-based photogrammetry. A three-dimensional shape error measurement method was applied for quantitative error analysis of the reconstructed model. High-precision point cloud data of the wing were obtained and sliced at equal intervals. The extracted airfoil cross-sections were fitted using polynomial equations and simulated in XFOIL. Sections exhibiting superior aerodynamic performance were selected as bionic airfoils. Next, using coupled extension analysis method and a comprehensive coupling degree evaluation function from coupled bionics, the coupling bionic feature vectors and eigenvalues between the folding wing and the bionic reference were analyzed. A coupled extension matrix model was established to guide the bionic design based on eagle-owl wing morphology. Finally, fluid simulations were performed using Fluent software, and a comparative analysis of aerodynamic performance was conducted. Results:The results reveal that the folding wing design inspired by the Eurasian eagle-owl significantly improves lift, reduces drag, and enhances flight stability compared to traditional wing designs. Conclusion:The bionic design of flying car folding wings based on the Eurasian eagle-owl wing proves effective in enhancing aerodynamic performance.
Swimming motion video human motion detection is becoming increasingly important in sports training and event analysis. Existing methods are deficient in dealing with complex underwater environments and rapid changes in swimming movements, and the accuracy and real-time performance of motion detection are low. Therefore, the study proposes a human motion detection method for swimming motion video based on multiscale (MS) separation spatio-temporal attention mechanism (STAM). The encoder-decoder architecture extracts and fuses features of different scales in both spatial and temporal dimensions to realize automatic detection and precise localization of swimming motion. The experimental results indicated that the feature extraction accuracy reached 97.34% after 43 iterations, and the feature importance reached 0.982 after 40 iterations. In terms of recognition accuracy, the average accuracy of the model reached 94.02%, the recall rate was 93.09%, and the F1 score was 93.56%. Adaptive testing of movement changes showed that the detection accuracy generally remained above 89%, and the accuracy in slow and large-sized movements even exceeded 95%. In addition to increasing swimming action detection's precision and resilience, the work offers technological and theoretical backing for the creation of intelligent sports analysis systems.
Background:Gait variability in kinematic and kinetic parameters during stair walking is a key indicator of motor function and fall risk in individuals with knee osteoarthritis (KOA). However, normative reference data and pathological patterns in KOA remain under explored. Methods:This cross-sectional study analyzed retrospective data from 169 participants, including 116 individuals with KOA and 53 matched healthy controls. Each participant performed both stair ascent and descent tasks, during which lower limb kinematic and kinetic gait parameters were obtained using a three-dimensional motion capture (3DMC) system and an instrumented staircase. Intrasubject variability, quantified by coefficient of variation (CV), was calculated for all gait parameters. A mixed between-within subject analysis of variance with aligned rank transformed data was conducted to assess the effects of group (KOA vs. control), condition (stair ascent vs. descent), and their interaction. Results:Individuals with KOA exhibited significantly greater kinematic variability during both stair ascent and descent, whereas greater kinetic variability was observed only in knee and hip joint moments and powers during ascent. Across both groups, variability increased at the knee and distal segments, but decreased at proximal segments (hip and pelvis) during stair ascent compared with descent. KOA individuals displayed distinct adaptation mechanism between stair ascent and descent, but not in kinematic parameters. Conclusion:Individuals with KOA demonstrate significantly increased movement variability compared with healthy controls during stair walking, especially in knee and hip joint moments and powers during ascent. These findings indicates distinct and task-specific adaptation strategies in KOA, reflecting altered stability and joint loading mechanisms.
This study investigates the power consumption of an assistive wearable exoskeleton actuation system using a virtual experimental framework. Different actuation system variants, including rigid, series elastic, and parallel elastic actuation in both single and dual configurations, were analyzed and compared with a mathematical model. The results demonstrated a strong correlation between the virtual and mathematical models, with only minor variations in power consumption across certain transmission system combinations. The study further found that combining harmonic drives with a belt and pulley mechanism resulted in reduced energy usage. Among the configurations analyzed, the dual variable parallel elastic actuation (VPEA) system, featuring harmonic drives at the hip and knee joints and ball screws at the ankle, proved to be the most energy-efficient setup. These findings validate the accuracy of the mathematical model and offer valuable guidelines for optimizing exoskeleton actuation systems to enhance their efficiency and performance.
This study aimed to compare the knee and ankle joint load characteristics of high and low Tai Chi postures, focusing on three typical Tai Chi movements: Wild Horse Mane (WHM), Repulse Monkey (RM), and Wave Hand in Cloud (WHC). It further explored how different postures affect lower limb loading in practitioners with varying skill levels to provide optimal guidance for Tai Chi practice. A total of 26 male participants were enrolled, divided into the professional group (PG, n = 13) and the interest group (IG, n = 15). A three-dimensional (3D) high-speed motion capture system was employed to record participants' Tai Chi movements, while a force platform was used concurrently to collect kinematic and dynamic data. For the knee joint, both groups exhibited significantly higher peak moments in the sagittal and coronal planes during the low posture than the high posture across all three movements (p < 0.05). During WHM, significant differences in peak ankle moments (sagittal and coronal planes) were noted between the two groups. In RM, the IG showed significantly higher peak ankle moments (sagittal and coronal planes) than the PG (p < 0.05). A significant positive correlation was found between posture/skill level and lower limb joint loading, with the knee joint being most affected. Professional practitioners should strengthen the muscles surrounding the knee and ankle joints to enhance joint protection during high-intensity practice and prevent chronic sports injuries resulting from long-term joint fatigue. For amateurs, a gradual transition from high to low postures is recommended to adapt to and enhance joint load-bearing capacity. Additionally, beginners should prioritize ankle flexibility training to improve ankle stability and lower injury risk.
Background:Previous studies have explored the kinematic relationship between the hip and lumbar spine during daily living activities. However, it is crucial to demonstrate the relationship between the hip, thoracic spine, and lumbar spine against hip kinematics during standing-to-sitting (SD-to-ST) and sitting-to-standing (ST-to-SD) tasks. Objectives:The study aimed to investigate the correlation between hip and thoracic kinematics, as well as hip and lumbar kinematics, during SD-to-ST and ST-to-SD tasks, and compare lumbar values with previous studies. Methods:A convenience-based cohort study design was employed, and 29 males from the Najran University population were recruited (age = 30 years; mass = 73 kg). Double-sided tape was used to attach four sensors to the spinous processes of T1, T12, and S1 and the side of the thigh in order to measure the range of motion (ROM) and velocity of the hip and two spinal regions during SD-to-ST and ST-to-SD. Results:The study found that hip ROM during SD-to-ST and ST-to-SD tasks was consistent at 64°, while thoracic and lumbar ROM were -1° and 48.75° for SD-to-ST and -10° and 47.81° for ST-to-SD, respectively. Hip velocity was similar at 62 and 65° s-1, and thoracic and lumbar velocities were 24 and 49.87° s-1 and 22 and 26.47° s-1, respectively. Interpretation:The study found no correlation between hip and thoracic spine in terms of ROM and velocity, and no correlation between regions. However, ROM and velocity significantly varied between regions. The lumbar spine outcomes were similar to previous research findings.
Knee motion involves intricate coordination among various anatomical structures. Effective treatment of knee pathologies requires precise identification of deformities and accurate surgical interventions, which often involve rapid tissue modification based on established knowledge. However, motion disorders are typically detected long after surgery. To address this, a simulation environment is proposed to plan and analyze surgical impacts on knee motion. Comprehensive knee joint modeling is crucial for a successful simulation. Clinically accepted movement procedures based on passive knee motion make tibiofemoral articulation modeling sufficient. Proposed model tibiofemoral articulation, incorporating 15 ligaments, tibial and femoral bones, and cartilages. Ligaments' tensile, bones', and cartilages' contact forces (CFs) define internal force interactions. Anatomical structures, their shapes, positions, and attachment points are identified from MRI, ensuring patient-specific modeling. Simulation results are compared to cadaver data using passive knee motion. Two rotational and three translational dependent joint motions (JMs) are compared pairwise. The results are highly correlated with the clinical benchmark. Pearson's correlation show a strong association between experimental and simulated passive knee flexions (PKFs; r > 0.89). The comparison is statistically significant with p < 0.05. Anterior-posterior translation showed the highest correlation (R 2 = 0.994). The findings indicate that the simulated model closely replicates actual knee responses.
