Journal of Bionic Engineering最新文献

The problem of disturbance rejection in humanoid robots has been properly studied, with most prior work focusing on hip-ankle-stepping compliance control strategies or whole-body inverse dynamics control. This paper presents an adaptive disturbance rejection balance controller based on a Variable-inertia Centroidal Model Predictive Control (ViC-MPC) approach, designed to address both minor disturbances that affect standing balance and major disturbances requiring stepping adjustments. The controller also facilitates reliable balance recovery after stepping adjustments. The humanoid robot is modeled as a spatial variable-inertia ellipsoid, representing the distribution of centroidal dynamics, with the contact wrenches optimized in real-time through a customized MPC formulation. Inspired by capturability-based constraints, we propose an adaptive dynamic stability transition strategy. This strategy is activated based on the Retrospective Horizon Average Centroidal Velocity (RHACV) and the Capture Point (CP), ensuring effective stepping adjustments and disturbance rejection. With the torque-controlled humanoid robot BHR8P, extensive simulation and experimental results demonstrate the effectiveness of the proposed method, highlighting its capability to adapt to and recover from various disturbances with improved stability.

Tissue engineering holds promise in developing materials for biological applications, such as bone tissue repair. This study focuses on bioabsorbable and biocompatible polymers like Poly(L-lactic acid) (PLLA), Polyurethane (PU), and Polycaprolactone (PCL), along with nanohydroxyapatite (nHA), an essential osteoconductive ceramic. The main objective was the development and characterization of scaffolds obtained by Rotary Jet Spinning (RJS) using PLLA, PU, and PCL incorporated with nHA, for bone-related applications. The resulting scaffolds exhibited uniform fiber morphology and a rough surface, ideal for effective bone-tissue interaction. The crystallinity indicated the scaffolds’ bioactivity by apatite deposition in simulated body fluid. In addition, in vitro biological assays using preosteoblastic cells showed the biocompatibility of cells based on cell viability and adhesion parameters on the scaffolds. The results underscore the capacity of scaffolds incorporating nHA to promote both cell proliferation and osteoconduction, which are key elements essential for achieving effective bone regeneration.

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered a global health crisis, necessitating accurate predictive models to forecast disease severity and aid in clinical decision-making. This study introduces an innovative machine learning approach, the bDWPLO-FKNN model, designed to predict the severity of COVID-19 pneumonia in patients. The model incorporates the Differential Weibull Polar Lights Optimizer (DWPLO), an enhancement of the Polar Lights Optimizer (PLO) with the differential evolution operator and the Weibull flight operator, to perform effective feature selection. The DWPLO’s performance was rigorously tested against IEEE CEC 2017 benchmark functions, demonstrating its robust optimization capabilities. The binary version of DWPLO (bDWPLO) was then integrated with the Fuzzy K-Nearest Neighbors (FKNN) algorithm to form the predictive model. Using a dataset from the People’s Hospital Affiliated with Ningbo University, the model was trained to identify patients at risk of developing severe pneumonia due to COVID-19. The bDWPLO-FKNN model exhibited exceptional predictive accuracy, with an accuracy of 84.036% and a specificity of 88.564%. The analysis revealed key predictors, including albumin, albumin to globulin ratio, lactate dehydrogenase, urea nitrogen, gamma-glutamyl transferase, and inorganic phosphorus, which were significantly associated with disease severity. The integration of DWPLO with FKNN not only enhances feature selection but also bolsters the model’s predictive power, providing a valuable tool for clinicians to assess patient risk and allocate healthcare resources effectively during the COVID-19 pandemic.

Inspired by the crucial role of the tail in crocodile locomotion, we propose a novel rigid-flexible coupled tail structure design. The tail design reduces the number of required actuators, enables undulatory propulsion in swimming, and provides additional support during terrestrial crawling. However, when the tail lifts off the ground during land crawling, its flexible underactuated structure tends to oscillate randomly due to minimal damping. These oscillations impart disruptive reaction torques to the body, critically impairing locomotion stability. To tackle this issue, we employed the standard Denavit-Hartenberg (DH) method and Newton-Euler equations to formulate a rigid-flexible coupled dynamic model for the tail, in which distributed elastic forces are embedded as internal forces in the force balance equations. Based on this model, we propose an oscillation suppression strategy based on an energy-optimized Nonlinear Model Predictive Controller (NMPC) with a single joint torque as the control input. This controller solves a constrained multi-objective optimization problem to effectively suppress the underactuated oscillations of the tail. Finally, experimental comparisons validate the accuracy of the dynamic model, and simulations based on this model substantiate the effectiveness of the oscillation suppression strategy.

Mechanical loading constitutes a fundamental determinant in the process of bone remodeling. This modeling encompasses the incorporation of mechanical stimuli, the involvement of cellular and molecular constituents, as well as the utilization of sophisticated computational methodologies. Such an approach is imperative for forecasting bone behaviour across varying environmental conditions. In the present study, key findings from bone mechanobiology are reviewed, along with the possibility that Functionally Graded Materials (FGM) enhances osseointegration and lowers the stress-shielding effect during bone remodeling and compared to titanium, FGM improves periprosthetic bone remodeling. To summarise some of the most important findings from computational models of bone mechanobiology, explaining how modifications to the mechanical environment affect implant design, growth of bone, and bone response. The impact that changes related to the mechanical environment have on bone response is examined using computational models and methods such as surface microtopography to determine how an implant’s bone density has increased over time. This review focuses on the refinement of advanced simulation frameworks and their synergy with imaging technologies to strengthen model validation, ultimately resulting in better clinical outcomes in the context of bone health treatments.

Legged robots have considerable potential for traversing unstructured situations; nonetheless, their inflexible frameworks often constrain adaptability and obstacle negotiation. The study article presents a revolutionary Soft Tri-Legged Robot (STLR) that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle (Bubble Artificial Muscles) and a bio-inspired tactile sensor (TacTip). The STLR is activated by BAMs, which are flexible, pneumatic-driven actuators that provide fine control over forward, backward, and steering movements. Obstacle identification and avoidance are facilitated by the TacTip sensor, which delivers tactile input for traversing unstructured terrains. We delineate the mechanical features of the BAMs, assess the functionality of the robot’s legs, and elaborate on the incorporation of the tactile sensing system. Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism. This study highlights the promise of soft robotics for search and rescue, medical aid, and autonomous exploration, while delineating difficulties and opportunities for future improvements in functionality and efficiency.

Segmenting skin lesions is critical for early skin cancer detection. Existing CNN and Transformer-based methods face challenges such as high computational complexity and limited adaptability to variations in lesion sizes. To overcome these limitations, we introduce MSAMamba-UNet, a lightweight model that integrates two novel architectures: Multi-Scale Mamba (MSMamba) and Adaptive Dynamic Gating Block (ADGB). MSMamba utilizes multi-scale decomposition and a parallel hierarchical structure to enhance the delineation of irregular lesion boundaries and sensitivity to small targets. ADGB dynamically selects convolutional kernels with varying receptive fields based on input features, improving the model’s capacity to accommodate diverse lesion textures and scales. Additionally, we introduce a Mix Attention Fusion Block (MAF) to enhance shallow feature representation by integrating parallel channel and pixel attention mechanisms. Extensive evaluation of MSAMamba-UNet on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets demonstrates competitive segmentation accuracy with only 0.056 M parameters and 0.069 GFLOPs. Our experiments revealed that MSAMamba-UNet achieved IoU scores of 85.53%, 85.47%, and 82.22%, as well as DSC scores of 92.20%, 92.17%, and 90.24%, respectively. These results underscore the lightweight design and effectiveness of MSAMamba-UNet.

Jaundice, common condition in newborns, is characterized by yellowing of the skin and eyes due to elevated levels of bilirubin in the blood. Timely detection and management of jaundice are crucial to prevent potential complications. Traditional jaundice assessment methods rely on visual inspection or invasive blood tests that are subjective and painful for infants, respectively. Although several automated methods for jaundice detection have been developed during the past few years, a limited number of reviews consolidating these developments have been presented till date, making it essential to systematically evaluate and present the existing advancements. This paper fills this gap by providing a thorough survey of automated methods for jaundice detection in neonates. The primary focus of the survey is to review the existing methodologies, techniques, and technologies used for neonatal jaundice detection. The key findings from the review indicate that image-based bilirubinometers and transcutaneous bilirubinometers are promising non-invasive alternatives, and provide a good trade-off between accuracy and ease of use. However, their effectiveness varies with factors like skin pigmentation, gestational age, and measurement site. Spectroscopic and biosensor-based techniques show high sensitivity but need further clinical validation. Despite advancements, several challenges including device calibration, large-scale validation, and regulatory barriers still haunt the researchers. Standardization, regulatory compliances, and seamless integration into healthcare workflows are the key hurdles to be addressed. By consolidating the current knowledge and discussing the challenges and opportunities in this field, this survey aims to contribute to the advancement of automatic jaundice detection and ultimately improve neonatal care.

Inspired by bacterial motility mechanisms, Magnetic Helical Miniature Robots (MHMRs) exhibit promising applications in biomedical fields due to their efficient locomotion and compatibility with biological tissues. In this review, we systematically survey the basics of MHMRs, from propulsion mechanism, magnetization and control methods to biomedical applications, aiming to provide readers with an easily understandable overview and fundamental knowledge on implementing MHMRs. The MHMRs are actuated by rotating magnetic fields, achieving steering and rotation through magnetic torque, and converting rotation into forward motion through the helical structure. Magnetization methods for MHMRs are reviewed into three types: attaching magnets, magnetic coatings, and magnetic powder doping. Additionally, this review discusses the control methods for MHMRs, covering imaging techniques, path tracking control—including classical control algorithms and increasingly popular learning-based methods, and swarm control. Subsequently, a comprehensive survey is conducted on the biomedical applications of MHMRs in the treatment of vascular diseases, drug delivery, cell delivery, and their integration with catheters. We finally provide a perspective about future challenges in MHMR research, including enhancing functional design capabilities, developing swarm-assisted independent control mechanisms, refining in vivo imaging techniques, and ensuring robust biocompatibility for safe medical use.

Inspired by the aquatic-adapted pit structures of the Cybister beetles that enable high-speed swimming, this study employs warp-knitted technology to fabricate drag-reduction swimwear textiles. Eight distinct fabric morphologies were produced, and a self-developed high-precision dynamic drag measurement device was used to systematically analyze the mechanisms underlying the drag-reduction performance of these biomimetic pit structures. The device incorporates a servomotor, ball screw linkage, and high-precision tension sensor, enabling real-time and accurate detection of fluid drag forces. It effectively overcomes the limitations of traditional indirect measurement methods, including dynamic response lag and insufficient accuracy. Experimental results demonstrate that the hydrophobic small-pit fabric (4^#) achieves an 84% drag reduction at 400 mm/s, outperforming the control sample (warp-knitted fabric 7^#). This significant reduction is attributed to the Cassie state established on the hydrophobic surface, which substantially decreases viscous drag and the microvortices generated by the pit structures, which delay flow separation and effectively minimize pressure drag. Furthermore, small-pit fabrics demonstrate a drag reduction rate 26% to 50% higher than that of large-pit structures, highlighting the critical importance of matching the pit scale to the thickness of the near-wall viscous sublayer for optimal drag reduction. This study establishes a theoretical foundation for the biomimetic design of high-performance drag-reduction swimsuits. The developed drag-measuring device also provides a standardized experimental platform for hydrodynamic studies of flexible materials, supporting a shift from empirical design methodologies to theory-driven approaches in drag-reduction technology and exhibiting significant potential for future advancements.