Applied Bionics and Biomechanics最新文献

Background and objective: Commercially available motorized prosthetic legs use exclusively nonbiological signals to control movements, such as those provided by load cells, pressure sensors, and inertial measurement units (IMUs). Although 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 lacking.

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 (preprocessing, feature extraction, and classification) of EMG and nonbiological 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 + pressure sensor + EMG, 2 = EMG, 3 = IMU + pressure sensor). EMG and nonbiological 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 with this publication. LocoD was used for all recording, preprocessing, 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 (IMU + pressure sensor + EMG) 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 + pressure sensor alone (Accuracy = 90.77 ± 4.6) with p-value <0.001.

Conclusions: In this study, we introduced and validated the functionality of LocoD as an open-source and modular platform to research control algorithms for prosthetic legs that incorporate bioelectric signals.

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.

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.

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.

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.

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.

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.

Objective: To investigate the effect of physical function training (PFT) on the improvement of tennis players' baseline movement stroke ability and to verify the effectiveness of PFT.

Method: Using a randomized controlled design, 32 national level 2 and above athletes (16 males and 16 females) from the tennis team of Chengdu Sport University were selected and randomly divided into an experimental group (EG; n = 16) and a control group (CG; n = 16). The EG received 12 weeks of physical training intervention, three times a week for 60 min each time. The CG underwent routine training on tennis balls. Before and after training, functional movement screens (FMSs), specialized physical fitness tests (including 100 m sprint run, standing long jump, fan run, backhand lateral movement, and forehand lateral movement), and baseline movement stroke effect tests (hitting frequency, hitting depth, and hitting accuracy) were conducted on both groups of athletes. The collected data were statistically analyzed using SPSS 26.0 and Microsoft Excel software. Paired samples t-test and independent samples t-test were used to compare the differences between the two groups of athletes and to determine significant differences between the data based on p-values.

Results: According to the FMS test findings, the EG's athletes significantly improved their functional movement capacity on all test items following training (p < 0.05), but the CG did not significantly alter. In the specialized physical fitness test, the EG showed significant training effects in standing long jump, fan run, backhand lateral movement, and forehand lateral movement (p < 0.05). The CG displayed no significant training effect. The baseline movement stroke effect test showed that the EG athletes significantly increased the number of baseline forehand movement hitting, baseline backhand movement strokes, and baseline one forehand and one backhand movement strokes after training (p < 0.05). The depth and accuracy of the stroke also increased significantly (p < 0.05). However, although the number of strokes increased in the CG athletes, the improvement in depth of stroke and accuracy was not significant (p > 0.05).

Conclusion: The 12-week physical training intervention can significantly improve the functional movement quality, specialized movement efficiency, and baseline hitting performance of college tennis players. This provides evidence-based training programs for improving baseline hitting ability.