Applied Bionics and Biomechanics

This study proposes a mecanum-built perturbation-based balance training device aimed at improving motor adaptive skills for fall prevention in individuals with neurological disorders or the elderly. Incorporating multidirectional fall simulations in line with modified constraint-induced movement therapy, the device’s efficacy was evaluated by measuring the distance traveled and peak acceleration under different static loads (20, 30, and 40 kg) and input accelerations (1, 2, and 3 m/s²). A pilot study with 10 subjects was conducted to assess device performance, utilizing repeated measures analysis of variance and Bonferroni’s post hoc analysis. Results indicated a load-dependent reduction in distance traveled, with an average mean difference of 0.74–1.23 cm between the 20 and 40 kg loads for trials of 9 and 18 cm, respectively. Despite varying loads, the device consistently achieved near-anticipated peak accelerations, suggesting its capability to induce effective perturbations. The study also observed a significant lateral movement preference, suggesting adjustments to pulse width modulation and time period may optimize lateral movement performance.

Objective. This study aimed to compare the impacts of different dual-task paradigms on the postural control ability and dynamic stability of the youth during stair descent. Method. Twenty young adults without regular exercise habits were randomly recruited to perform stair descent tasks with three different paradigms: single-task, cognitive dual-task, and manual dual-task. Kinematic and dynamic data were collected using an 8 Vicon motion analysis system and a Kistler force plate to evaluate postural control ability and dynamic stability during stair descent. Results. The variation trends of lower limb joint moment were similar under the three task models. Compared with a single-task, both dual-task paradigms significantly reduced the mechanical parameters and dynamic stability during stair descent. Conclusion. The dual-task paradigm increases the risk of stair-related falls. Both cognitive and manual tasks have similar impacts on postural control ability and dynamic stability during stair walking. It is recommended that people avoid performing dual tasks during stair descent.

This paper develops an adaptive nonsingular fast terminal sliding-mode control (ANFTSMC) scheme for the continuum robot subjected to uncertain dynamics, external disturbances, and input nonlinearities (e.g., actuator deadzones/faults). Concretely, a function approximation technique (FAT) is utilized to estimate unknown robot dynamics and actuator deadzones/faults online. Furthermore, a disturbance observer (DO) is devised to make up for unknown external disturbances. Then, an ANFTSMC scheme combined with FAT and DO is developed, to expedite the restoration of the stability for the continuum robot. The proposed ANFTSMC not only can retain the benefits of traditional terminal sliding-mode control (TSMC), containing easy enforcement, quick response, and robustness to uncertainties but also dispose of the latent singularity for traditional faster TSMC designs. Afterward, the simulation results show that the proposed controller can effectively improve the trajectory tracking accuracy of the continuum robot, and the tracking root-mean-square errors are 0.0115 and 0.0128 rad. Finally, the effectiveness of ANFTSMC scheme is validated by experiments.

A soft robot is composed of soft materials, which exhibit continuous deformation and driving structure integration and can arbitrarily change shapes and sizes over wide ranges. It shows strong adaptability to unstructured environments and has broad application prospects in military reconnaissance, medical rescues, agricultural production, etc. Soft robots based on ionic electroactive polymers (EAPs) have low-driving voltages, large-actuation displacements, fast responses, light weights, and low powers and have become a hot research field of bionic robots. Ionic liquid gels (ILGs) are new ionic EAPs. In this study, a new soft actuator was designed based on an ILG, and the electromechanical coupling model of an ILG soft actuator was studied in detail. Based on the system transfer function method, a mechatronic coupling model for the soft actuator was developed. According to the material characteristics and current response law of the ILG-containing EAP, an equivalent circuit model was used to describe transfer of the output current and input voltage. Based on the equivalent transformer model for ionic polymer–metal composite (IPMC) actuators proposed by Claudia Bonomo, the electromechanical coupling equation and a driving equation of the ILG soft actuator were established. The least-squares method was used with the coupling model of an ILG soft actuator to identify the system parameters for the model, and the effects of the structural parameters on the end displacement and driving force of the soft actuator were analyzed.

Step length estimation (SLE) is the core process for pedestrian dead reckoning (PDR) for indoor positioning. Original SLE requires accurate estimations of pedestrian characteristic parameter (PCP) by the linear update, which may cause large distance errors. To enhance SLE, this paper proposes the Sage–Husa adaptive Kalman filtering-based PCP update (SHAKF-PU) mechanism for enhancing SLE in PDR. SHAKF has the characteristic of predicting the trend of historical data; the estimated PCP is closer to the true value than the linear update. Since different kinds of pedestrians can influence the PCP estimation, adaptive PCP estimation is required. Compared with the classical Kalman filter, SHAKF updates its Q and R parameters in each update period so the estimated PCP can be more accurate than other existing methods. The experimental results show that SHAKF-PU reduces the error by 24.86% compared to the linear update, and thus, the SHAKF-PU enhances the indoor positioning accuracy for PDR.

Nanodrug delivery systems have been used in the diagnosis and treatment of ischemic stroke. However, the delivery mechanisms of nanoparticles within microvascular after cerebral ischemia have not been systematically revealed. This study aims to investigate the binding of different nanoparticles to the walls of ischemic brain microvascular through numerical simulations. In this study, 3D models of cerebral microvascular based on ischemic pathological changes are constructed. After building the mesh of microvascular, computational fluid dynamics is used to simulate blood flow and nanoparticle delivery. The simulation results show that the total amount of binding nanoparticles with small size is higher than that with large size. The large-sized nanoparticles are more easily delivered to the stenosis. The density of the nanoparticles has no significant effect on delivery. Furthermore, the study finds that the presence of red blood cells can significantly enhance the delivery efficiency of nanoparticles. In addition to evaluating the forces exerted on the nanoparticles, the impact of the binding affinity of the modified ligand on nanoparticles to the target receptor on delivery is investigated. In summary, selecting suitable nanoparticles according to different targets will improve the delivery efficiency of nanodrugs. The microvascular delivery model of nanoparticles proposed in this study may be helpful in the design of nanoparticles for diagnosis and treatment of cerebral ischemia.