New Insights for the Design of Bionic Robots: Adaptive Motion Adjustment Strategies During Feline Landings

Free-ranging squirrels perform stable, above-branch landings by balancing using leg force and nonprehensile foot torque

For gap-crossing agility, arboreal animals require the ability to stabilize dynamic landings on branches. Despite lacking a prehensile grip, squirrels achieve stable landings using a palmar grasp. We investigated the landing dynamics of free-ranging fox squirrels (Sciurus niger) to uncover strategies for stable, above-branch landings. Using high-speed video and force-torque measurements in the sagittal plane, we quantified landing kinetics across gap distances. Squirrels rapidly managed >80% of the landing energy with their forelimbs. With larger gaps, peak leg force and foot torque increased. Alignment between forelimbs, velocity and force also increased, likely reducing joint moment. We tested control hypotheses based on an extensible pendulum model used in a physical, hopping robot named Salto. Squirrels stabilized off-target landings by modulating leg force and foot torque. To correct for undershooting, squirrels generated pull-up torques and reduced leg force. For overshooting, squirrels generated braking torques and increased leg force. Embodying control principles in leg and foot design can enable stable landings in sparse environments for animals and robots alike, even those lacking prehensile grasps.

Contribution of ankle motion pattern during landing to reduce the knee-related injury risk

BACKGROUND

Single-leg landing (SL) is an essential technique in sports such as basketball, soccer, and volleyball, which is often associated with a high risk of knee-related injury. The ankle motion pattern plays a crucial role in absorbing the load shocks during SL, but the effect on the knee joint is not yet clear. This work aims to explore the effects of different ankle plantarflexion angles during SL on the risk of knee-related injury.

METHODS

Thirty healthy male subjects were recruited to perform SL biomechanics tests, and one standard subject was selected to develop the finite element model of foot-ankle-knee integration. The joint impact force was used to evaluate the impact loads on the knee at various landing angles. The internal load forces (musculoskeletal modeling) and stress (finite element analysis) around the knee joint were simulated and calculated to evaluate the risk of knee-related injury during SL. To more realistically revert and simulate the anterior cruciate ligament (ACL) injury mechanics, we developed a knee musculoskeletal model that reverts the ACL ligament to a nonlinear short-term viscoelastic mechanical mechanism (strain rate-dependent) generated by the dense connective tissue as a function of strain.

RESULTS

As the ankle plantarflexion angle increased during landing, both the peak knee vertical impact force (p = 0.001) and ACL force (p = 0.001) decreased significantly. The maximum von Mises stress of ACL, meniscus, and femoral cartilage decreased as the ankle plantarflexion angle increased. The overall range of variation in ACL stress was small and was mainly distributed in the femoral and tibial attachment regions, as well as in the mid-lateral region.

CONCLUSION

The current findings revealed that the use of larger ankle plantarflexion angles during landing may be an effective solution to reduce knee impact load and the risk of rupture of the medial femoral attachment area in the ACL. The findings of this study have the potential to offer novel perspectives in the optimized application of landing strategies, thus giving crucial theoretical backing for decreasing the risk of knee-related injury.

Multilayer perceptron and support vector regression models for feline parturition date prediction

A crucial challenge in feline obstetric care is the accurate prediction of the parturition date during late pregnancy. The classic simple linear regression (SLR) model, which employed the fetal biparietal diameter (BPD) as the single input feature, was frequently applied for such prediction with limited accuracy. Since Multilayer Perceptron (MLP) and Support Vector Regression (SVR) are now two of the most potent scientific regression models, this study, for the first time, introduced such models as the new promising tools for feline parturition date prediction. The following features were candidate inputs for our models: biparietal diameter (BPD), litter size, and maternal weight. We observed and compared the performance results for each model. As the best-performed model, MLP delivered the highest coefficient score (0.972 ± 0.006), lowest mean absolute error score (1.110 ± 0.060), and lowest mean squared error score (1.540 ± 0.141), respectively. For the first time in this study, BPD, litter size, and maternal weight were considered the essential features for the innovative MLP and SVR modeling. With the optimized model parameters and the described analytical platform, further verification of these advanced models in feline obstetric practices is feasible.

A new method applied for explaining the landing patterns: Interpretability analysis of machine learning

As one of many fundamental sports techniques, the landing maneuver is also frequently used in clinical injury screening and diagnosis. However, the landing patterns are different under different constraints, which will cause great difficulties for clinical experts in clinical diagnosis. Machine learning (ML) have been very successful in solving a variety of clinical diagnosis tasks, but they all have the disadvantage of being black boxes and rarely provide and explain useful information about the reasons for making a particular decision. The current work validates the feasibility of applying an explainable ML (XML) model constructed by Layer-wise Relevance Propagation (LRP) for landing pattern recognition in clinical biomechanics. This study collected 560 groups landing data. By incorporating these landing data into the XML model as input signals, the prediction results were interpreted based on the relevance score (RS) derived from LRP. The interpretation obtained from XML was evaluated comprehensively from the statistical perspective based on Statistical Parametric Mapping (SPM) and Effect Size. The RS has excellent statistical characteristics in the interpretation of landing patterns between classes, and also conforms to the clinical characteristics of landing pattern recognition. The current work highlights the applicability of XML methods that can not only satisfy the traditional decision problem between classes, but also largely solve the lack of transparency in landing pattern recognition. We provide a feasible framework for realizing interpretability of ML decision results in landing analysis, providing a methodological reference and solid foundation for future clinical diagnosis and biomechanical analysis.

A new method proposed for realizing human gait pattern recognition: Inspirations for the application of sports and clinical gait analysis

BACKGROUND

Finding the best subset of gait features among biomechanical variables is considered very important because of its ability to identify relevant sports and clinical gait pattern differences to be explored under specific study conditions. This study proposes a new method of metaheuristic optimization-based selection of optimal gait features, and then investigates how much contribution the selected gait features can achieve in gait pattern recognition.

METHODS

Firstly, 800 group gait datasets performed feature extraction to initially eliminate redundant variables. Then, the metaheuristic optimization algorithm model was performed to select the optimal gait feature, and four classification algorithm models were used to recognize the selected gait feature. Meanwhile, the accuracy results were compared with two widely used feature selection methods and previous studies to verify the validity of the new method. Finally, the final selected features were used to reconstruct the data waveform to interpret the biomechanical meaning of the gait feature.

RESULTS

The new method finalized 10 optimal gait features (6 ankle-related and 4-related knee features) based on the extracted 36 gait features (85 % variable explanation) by feature extraction. The accuracy in gait pattern recognition among the optimal gait features selected by the new method (99.81 % ± 0.53 %) was significantly higher than that of the feature-based sorting of effect size (94.69 % ± 2.68 %), the sequential forward selection (95.59 % ± 2.38 %), and the results of previous study. The interval between reconstructed waveform-high and reconstructed waveform-low curves based on the selected feature was larger during the whole stance phase.

SIGNIFICANCE

The selected gait feature based on the proposed new method (metaheuristic optimization-based selection) has a great contribution to gait pattern recognition. Sports and clinical gait pattern recognition can benefit from population-based metaheuristic optimization techniques. The metaheuristic optimization algorithms are expected to provide a practical and elegant solution for sports and clinical biomechanical feature selection with better economy and accuracy.

The Effect of Concave-Side Intertransverse Ligament Laxity on the Stress of AIS Lumbar Spine Based on Finite Element Method

(1) Background: Scoliosis has the mechanical characteristic of asymmetric stress distribution, which is one of the reasons for the aggravation of scoliosis. Bracing therapy is the best treatment for AIS, but it is difficult and costly to operate. Is it possible to reduce pressure in the concave side by relaxing the ITL in the concave side of scoliosis, so as to improve the abnormal stress distribution of scoliosis? In this paper, a finite element method was used to simulate the effect of the relaxation of concave-side ITL on the stress of a lumbar spine with scoliosis, which provides some guidance for the treatment of scoliosis. (2) Methods: Using CT images of a patient with scoliosis whose Cobb Angle was 43° and Lordosis Angle was 45, a scoliosis lumbar was established, and Young's modulus of the ITL of the concave-side lumbar spine was reduced by 95% to simulate ligament relaxation. By comparing the stress condition of the model vertebral body with no ligament relaxation, the effect of concave-side ITL relaxation on the mechanical characteristics of scoliosis lumbar spine was explored. (3) Results: An effective and complete model of the lumbar spine was established. The concave ITL relaxed, which only had a great impact on the bending loads. After the ligament was relaxed, the stability of the spine was reduced. Stress concentration on the concave side of vertebrae and the IVD was aggravated. Under loads on the convex side, the maximum stress on the vertebral body and the IVD increased significantly, making lumbar vertebrae more vulnerable to injury. (4) Conclusions: Laxity of the ITL on the concave side of the AIS lumbar only affects the bending load. Laxity of the concave-side ligament will reduce the stability of the lumbar, aggravate the uneven stress distribution of scoliotic lumbar vertebrae, increase the risk of IVD injury, and be unfavorable for the scoliotic lumbar spine. Relaxation of the concave ITL alone is not an effective way to treat scoliosis.

A new method proposed to explore the feline's paw bones of contributing most to landing pattern recognition when landed under different constraints

Felines are generally acknowledged to have natural athletic ability, especially in jumping and landing. The adage “felines have nine lives” seems applicable when we consider its ability to land safely from heights. Traditional post-processing of finite element analysis (FEA) is usually based on stress distribution trend and maximum stress values, which is often related to the smoothness and morphological characteristics of the finite element model and cannot be used to comprehensively and deeply explore the mechanical mechanism of the bone. Machine learning methods that focus on feature pattern variable analysis have been gradually applied in the field of biomechanics. Therefore, this study investigated the cat forelimb biomechanical characteristics when landing from different heights using FEA and feature engineering techniques for post-processing of FEA. The results suggested that the stress distribution feature of the second, fourth metacarpal, the second, third proximal phalanx are the features that contribute most to landing pattern recognition when cats landed under different constraints. With increments in landing altitude, the variations in landing pattern differences may be a response of the cat's forelimb by adjusting the musculoskeletal structure to reduce the risk of injury with a more optimal landing strategy. The combination of feature engineering techniques can effectively identify the bone's features that contribute most to pattern recognition under different constraints, which is conducive to the grasp of the optimal feature that can reveal intrinsic properties in the field of biomechanics.