Journal of Bionic Engineering最新文献

Recently, wearable gait-assist robots have been evolving towards using soft materials designed for the elderly rather than individuals with disabilities, which emphasize modularization, simplification, and weight reduction. Thus, synchronizing the robotic assistive force with that of the user’s leg movements is crucial for usability, which requires accurate recognition of the user’s gait intent. In this study, we propose a deep learning model capable of identifying not only gait mode and gait phase but also phase progression. Utilizing data from five inertial measurement units placed on the body, the proposed two-stage architecture incorporates a bidirectional long short-term memory-based model for robust classification of locomotion modes and phases. Subsequently, phase progression is estimated through 1D convolutional neural network-based regressors, each dedicated to a specific phase. The model was evaluated on a diverse dataset encompassing level walking, stair ascent and descent, and sit-to-stand activities from 10 healthy participants. The results demonstrate its ability to accurately classify locomotion phases and estimate phase progression. Accurate phase progression estimation is essential due to the age-related variability in gait phase durations, particularly evident in older adults, the primary demographic for gait-assist robots. These findings underscore the potential to enhance the assistance, comfort, and safety provided by gait-assist robots.

Stroke survivors often face significant challenges when performing daily self-care activities due to upper limb motor impairments. Traditional surface electromyography (sEMG) analysis typically focuses on isolated hand postures, overlooking the complexity of object-interactive behaviors that are crucial for promoting patient independence. This study introduces a novel framework that combines high-density sEMG (HD-sEMG) signals with an improved Whale Optimization Algorithm (IWOA)-optimized Long Short-Term Memory (LSTM) network to address this limitation. The key contributions of this work include: (1) the creation of a specialized HD-sEMG dataset that captures nine continuous self-care behaviors, along with time and posture markers, to better reflect real-world patient interactions; (2) the development of a multi-channel feature fusion module based on Pascal’s theorem, which enables efficient signal segmentation and spatial–temporal feature extraction; and (3) the enhancement of the IWOA algorithm, which integrates optimal point set initialization, a diversity-driven pooling mechanism, and cosine-based differential evolution to optimize LSTM hyperparameters, thereby improving convergence and global search capabilities. Experimental results demonstrate superior performance, achieving 99.58% accuracy in self-care behavior recognition and 86.19% accuracy for 17 continuous gestures on the Ninapro db2 benchmark. The framework operates with low latency, meeting the real-time requirements for assistive devices. By enabling precise, context-aware recognition of daily activities, this work advances personalized rehabilitation technologies, empowering stroke patients to regain autonomy in self-care tasks. The proposed methodology offers a robust, scalable solution for clinical applications, bridging the gap between laboratory-based gesture recognition and practical, patient-centered care.

In recent years, significant research attention has been directed towards swarm intelligence. The Milling behavior of fish schools, a prime example of swarm intelligence, shows how simple rules followed by individual agents lead to complex collective behaviors. This paper studies Multi-Agent Reinforcement Learning to simulate fish schooling behavior, overcoming the challenges of tuning parameters in traditional models and addressing the limitations of single-agent methods in multi-agent environments. Based on this foundation, a novel Graph Convolutional Networks (GCN)-Critic MADDPG algorithm leveraging GCN is proposed to enhance cooperation among agents in a multi-agent system. Simulation experiments demonstrate that, compared to traditional single-agent algorithms, the proposed method not only exhibits significant advantages in terms of convergence speed and stability but also achieves tighter group formations and more naturally aligned Milling behavior. Additionally, a fish school self-organizing behavior research platform based on an event-triggered mechanism has been developed, providing a robust tool for exploring dynamic behavioral changes under various conditions.

Soft actuators are inherently flexible and compliant, traits that enhance their adaptability to diverse environments and tasks. However, their low structural stiffness can lead to unpredictable and uncontrollable complex deformations when substantial force is required, compromising their load-bearing capacity. This work proposes a novel method that uses gecko setae-inspired adhesives as interlayer films to construct a layer jamming structure to adjust the stiffness of soft actuators. The mechanical behavior of a single tilted microcylinder was analyzed using the energy method to determine the adhesion force of the adhesives. The gecko-inspired adhesive was designed under the guidance of the adhesion force model. Testing under various loads and directions revealed that the tilted characteristic of microcylinders can enhance the adhesion force in its grasping direction. The adhesive demonstrated excellent adhesion performance compared to other typical adhesives. A tunable stiffness actuator using gecko setae-inspired adhesives (TSAGA), was developed with these adhesives serving as interlayer films. The stiffness model of TSAGA was derived by analyzing its axial compression force. The results of stiffness test indicate that the adhesives serve as interlayer films can adjust the stiffness in response to applied load. TSAGA was compared with other typical soft actuators in order to evaluate the stiffness performance, and the results indicate that TSAGA exhibits the highest stiffness and the widest tunable stiffness range. This demonstrates the superior performance of the setae-inspired adhesives as interlayer films in terms of stiffness adjustment.

Quadruped robots with body joints exhibit enhanced mobility, however, in outdoor environments, the energy that the robot can carry is limited, necessitating optimization of energy consumption to accomplish more tasks within these constraints. Inspired by quadruped animals, this paper proposes an energy-saving strategy for a body joint quadruped robot based on Central Pattern Generator (CPG) with multi-sensor fusion bio-reflexes. First, an energy consumption model for the robot is established, and energy characteristic tests are conducted under different gait parameters. Based on these energy characteristics, optimal energy-efficient gait parameters are determined for various environmental conditions. Second, biological reflex mechanisms are studied, and a motion control model based on multi-sensor fusion biological reflexes is established using CPG as the foundation. By integrating the reflex model and gait parameters, real-time adaptive adjustments to the robot’s motion gait are achieved on complex terrains, reducing energy loss caused by terrain disturbances. Finally, a prototype of the body joint quadruped robot is built for experimental verification. Simulation and experimental results demonstrate that the proposed algorithm effectively reduces the robot’s Cost of Transport (COT) and significantly improves energy efficiency. The related research results can provide a useful reference for the research on energy efficiency of quadruped robots on complex terrain.

Due to the limited regeneration capacity of myocardial tissue after infarction, designing tissue engineering scaffolds are in demand. In the present study, electrospun nanofibrous scaffolds were made out of polyurethane, collagen and gold nanoparticles with random and aligned nanofiber morphologies. The nanoparticles were green-synthesized using saffron extract. Nanoparticle characterizations with UV-Vis. spectroscopy and DLS illustrated theoretical and hydrodynamic diameters of around 7 and 13 nm, respectively, having zeta potential of − 37 mV. SEM and TEM micrographs showed the morphology and diameters of obtained nanofibers. Also, further characterization were done by ATR-FTIR, XRD and TGA investigations and degradation studies. Contact angle measurements showed hydrophilic nature of the scaffolds (59 ± 0.6° for aligned PU/Col/Au50 nanofibers compared to 120 ± 2.6° for random PU nanofibers). Mechanical testing demonstrated appropriate tensile properties of the scaffolds for cardiac tissue engineering (Young’s modulus: 1.53 ± 0.07 MPa for aligned PU/Col/Au50 nanofibers compared to 0.4 ± 0.05 MPa for random PU nanofibers). Finally, alamar blue assay revealed proper survival of the cells of HUVEC cell line on the prepared scaffolds, where the highest percentages were observed for random and aligned PU/Col/Au50 nanofibers. According to the findings, the fabricated PU/Col/AuNPs nanofibrous scaffolds could be considered as potential cardiac patches.

The unidirectional flow of lymphatic fluid depends significantly on the valve structure within the lymphatic system, thus impacting tumor cell metastasis via the lymphatic system. However, existing microdevices for studying tumor lymphatic metastasis have overlooked the impact of open-close valve structures on the lymphatic flow field. This paper presents a novel biomimetic lymphatic valve structure, which innovatively incorporates the thin-shell theory into the modeling of lymphatic-mimicking structures. Through finite element simulations, we have systematically analyzed the influence of valve thickness and elasticity on its deformation characteristics. Materials closely matching the actual properties of biological tissues are synthesized. And the soft-etching technique was used to fabricate lymphomimetic microchannels, which were then tested to evaluate their capability in intercepting unidirectional flow. The results showed that the lymphomimetic valve structure had no observable leaks and effectively intercepted unidirectional flow. Our study not only elucidates the mechanism of lymphatic circulation but also presents a dependable biomimetic model that could facilitate additional biological investigations and phenotypic drug screening.

Aluminum (Al) alloys are widely used in aerospace, automotive, and electronics industries due to their light weight and durability. However, their poor corrosion resistance has hindered further development, especially when in contact with oily solutions. In this work, a simple method was adopted to superoleophobic Al alloys with hierarchical micro/nano structures, including plasma electrolytic oxidation, hydrothermal treatment, and fluorination modification. The Contact Angle (CA) and Sliding Angle (SA) of water on the surface were measured to be 164.1° and 0.6°, and those of ethylene glycol-water solutions were 157.1° and 0.7°, respectively. The surface exhibited excellent low adhesion and self-cleaning properties. The electrochemical test results showed that compared with bare Al alloy, the corrosion current density of the superoleophobic surface decreased by 2 orders of magnitude, the charge transfer resistance (R_ct) increased by 3 orders of magnitude, and the corrosion inhibition efficiency reached 98.11%. Additionally, the superoleophobic surface still exhibited good corrosion resistance after 45 days of immersion. We believe that this work provides a novel perspective and theoretical support for the long-term durability of Al alloys in the ethylene glycol-water solution environment.

Muscle strength training can effectively reduce muscle atrophy, activate muscle tissue and promote muscle strength recovery and growth. Based on our previous research, we developed four muscle strength training strategies by further imitating the clinical muscle strength training methods, namely, Isokinetic centriPetal-centriPetal Exercise (IPPE), Isokinetic centriPetal-centriFuge exercise (IPFE), Isokinetic centriFuge-centriPetal Exercise (IFPE) and Isokinetic centriFuge-centriFuge Exercise (IFFE). To quantitatively evaluate the performance of the developed strategies, experiments were carried out with elbow and knee joints as examples, and muscle Endurance Ratio (ER), Flexion and Extension torque ratio (F/E) and the degree of muscle activation were extracted and calculated based on angle/torque and Surface ElectroMyoGraphy (sEMG) signals. Experimental results showed that the ER value of IFFE was significantly reduced compared with IPPE, while the F/E value of IFPE was significantly increased; this suggests that muscle centrifugation corresponds to higher training intensity; In addition, flexor and extensor muscle groups showed different levels of muscle activation in different training strategies. The results reveal that combining different muscle movement characteristics, isokinetic exercise can exert special muscle strength training effects. The study can lay the foundation for exploring subject-specific adaptive muscle strength training strategies to better adapt to different levels of muscle strength.

Polyvinyl alcohol was used as a halochromic carrier material for anthocyanins extracted from bracts of Euphorbia pulcherrima. Including anthocyanins in halochromic carrier improved the thickness, solubility, swelling capacity, water vapor permeability, antioxidant properties, and tensile strength of the films. The high levels of anthocyanins decreased the water contact angle to 60.48°. Adding anthocyanins to films altered the color of halochromic films in response to exposure to ammonia emission or pH changes, allowing for the creation of smart sensors. The artificial biosensors altered the color linked to decreased freshness of the minced meat primarily because of ammonia generation and pH changes as the meat decays. One key point is that halochromic sensors can biodegrade and break down under simulated environmental conditions in just 30 days. In general, the created intelligent biosensors could be utilized as halochromic materials to detect the initial stages of food spoilage.