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This paper aims to investigate how implying movement in food packaging imagery may affect product liking. Furthermore, the underlying mechanism is investigated by studying the effect of implied motion visuals on design appeal and naturalness perception.

Two packages of pineapple juice were designed in which the implied motion depicted in their imagery was manipulated, and a tasting experiment was conducted in which two samples of the same juice were evaluated.

The results show that the effect of packaging imagery on product liking occurs indirectly through both design appeal and the product naturalness perception. The results of a parallel multiple-mediator analysis show that (1) depicting implied motion made the package be perceived as more appealing, (2) the product corresponding to the package depicting implied motion was perceived as being more natural, and (3) both effects equally contributed to the positive effect of visuals depicting implied motion on product liking.

Overall, these findings widen our understanding of the effects of packaging design on product liking and may help both designers and manufacturers design more appropriate packaging for their products.

The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test.

To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details.

The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests.

A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.

Compared to quad-rotor unmanned aerial vehicle (UAV), the tilting dual-rotor UAV is more prone to instability during exercises and disturbances. The purpose of this paper is using an active balance tail to enhance the hovering stability and motion smoothness of tilting dual-rotor UAV.

A balance tail is proposed and integrated into the tilting dual-rotor UAV to enhance hovering stability and motion smoothness. By strategically moving, the balance tail generates additional force and moment, which can promote the rapid stability of the UAV. Subsequently, the control strategy of the UAV is designed, and the influence of the swing of the balance tail at different installation positions with different masses on the dual-rotor UAV is analyzed through simulation. The accompany motion law and the active control, which is based on cascade Proportion Integration Differentiation (PID) control to enhance the hovering stability and motion smoothness of the UAV, are proposed.

The results demonstrate that active control has obvious adjustment effectiveness when the UAV moves to the target position or makes an emergency stop compared with the results of balance tail no swing and accompany motion.

The balance tail offers a straightforward means to enhance the motion smoothness of tilting dual-rotor UAV, rendering it safer and more reliable for practical applications.

The novelty of this works comes from the application of an active balance tail to improve the stability and motion smoothness of dual-rotor UAV.

Traditional robot arm trajectory planning methods have problems such as insufficient generalization performance and low adaptability. This paper aims to propose a method to plan the robot arm’s trajectory using the trajectory learning and generalization characteristics of dynamic motion primitives (DMPs).

This study aligns multiple demonstration motion primitives using dynamic time warping; use the Gaussian mixture model and Gaussian mixture regression methods to obtain the ideal primitive trajectory actions. By establishing a system model that improves DMPs, the parameters of the nonlinear function are learned based on the ideal primitive trajectory actions of the robotic arm, and the robotic arm motion trajectory is reproduced and generalized.

Experiments have proven that the robot arm motion trajectory learned by the method proposed in this article can not only learn to generalize and demonstrate the movement trend of the primitive trajectory, but also can better generate ideal motion trajectories and avoid obstacles when there are obstacles. The maximum Euclidean distance between the generated trajectory and the demonstration primitive trajectory is reduced by 29.9%, and the average Euclidean distance is reduced by 54.2%. This illustrates the feasibility of this method for robot arm trajectory planning.

It provides a new method for the trajectory planning of robotic arms in unstructured environments while improving the adaptability and generalization performance of robotic arms in trajectory planning.

This study aims to explore the impact of multisource deformation errors on the oil film contact surface, which arise from manufacturing, assembly, oil pressure and thermal influences, on the motion accuracy of hydrostatic guideway.

Using thermal-structural coupling simulations, this research investigates the effects of assembly, oil pressure and thermal factors on deformation errors of the oil film contact surface. By integrating these with manufacturing errors, a profile error model for the oil film contact surface is developed, characterizing the cumulative effect of these errors. Using kinematic theory and progressive Mengen flow controller characteristics, the motion error at any position of the hydrostatic guideway is quantified, examining how surface error traits impact motion accuracy.

The error averaging effect is affected by the profile error of oil film contact surface. Meanwhile, the motion accuracy of hydrostatic guideway is highly sensitive to the oil film contact surface error amplitude.

This approach allows for precise prediction and analysis of motion accuracy in hydrostatic guideways during the design and manufacturing stages. It also provides guidance for planning process tolerances.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-03-2024-0063/

There is coupling between the branches of mobile parallel robots, similar to traditional parallel mechanisms, but there is currently relatively little research on the coupling problem between the branches of mobile parallel robots.

This study optimizes the coupling analysis method of traditional parallel mechanisms, treats the mobile parallel mechanism as a whole, takes the motion of the active pair as input and the overall motion of the mobile parallel mechanism as output and analyzes the input–output characteristics of the mobile parallel mechanism. Moreover, this study applies this theory to a mobile parallel mechanism, designs control logic and finally conducts simulation and physical verification.

This study proposes a coupling analysis method suitable for parallel mobile robots and designs the control logic of their active pair based on the results of their coupling analysis. This study designs a multimode variable coupling parallel mobile robot, which can change the coupling of the mechanism by changing its own branch chain structure, so that it can switch between different coupling configurations to meet the different needs brought by different terrains.

The work presented in this paper propose a method for analyzing the coupling of mobile parallel robots and simplify their control logic by applying coupling theory to the design of mobile parallel robots. This study conducts simulation and physical experiments, thereby filling the gap in the coupling analysis of parallel mobile robots and laying the foundation for the research of uncoupled parallel mobile robots.

The electromechanical planetary transmission system has the advantages of high transmission power and fast running speed, which is one of the important development directions in the future. However, during the operation of the electromechanical planetary transmission system, friction and other factors will lead to an increase in gear temperature and thermal deformation, which will affect the transmission performance of the system, and it is of great significance to study the influence of the temperature effect on the nonlinear dynamics of the electromechanical planetary system.

The effects of temperature change, motor speed, time-varying meshing stiffness, meshing damping ratio and error amplitude on the nonlinear dynamic characteristics of electromechanical planetary systems are studied by using bifurcation diagrams, time-domain diagrams, phase diagrams, Poincaré cross-sectional diagrams, spectra, etc.

The results show that when the temperature rise is less than 70 °C, the system will exhibit chaotic motion. When the motor speed is greater than 900r/min, the system enters a chaotic state. The changes in time-varying meshing stiffness, meshing damping ratio, and error amplitude will also make the system exhibit abundant bifurcation characteristics.

Based on the principle of thermal deformation, taking into account the temperature effect and nonlinear parameters, including time-varying meshing stiffness and tooth side clearance as well as comprehensive errors, a dynamic model of the electromechanical planetary gear system was established.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

This study aims to introduce a novel noise filter module designed for LiDAR simultaneous localization and mapping (SLAM) systems. The primary objective is to enhance pose estimation accuracy and improve the overall system performance in outdoor environments.

Distinct from traditional approaches, MCFilter emphasizes enhancing point cloud data quality at the pixel level. This framework hinges on two primary elements. First, the D-Tracker, a tracking algorithm, is grounded on multiresolution three-dimensional (3D) descriptors and adeptly maintains a balance between precision and efficiency. Second, the R-Filter introduces a pixel-level attribute named motion-correlation, which effectively identifies and removes dynamic points. Furthermore, designed as a modular component, MCFilter ensures seamless integration into existing LiDAR SLAM systems.

Based on rigorous testing with public data sets and real-world conditions, the MCFilter reported an increase in average accuracy of 12.39% and reduced processing time by 24.18%. These outcomes emphasize the method’s effectiveness in refining the performance of current LiDAR SLAM systems.

In this study, the authors present a novel 3D descriptor tracker designed for consistent feature point matching across successive frames. The authors also propose an innovative attribute to detect and eliminate noise points. Experimental results demonstrate that integrating this method into existing LiDAR SLAM systems yields state-of-the-art performance.

The purpose of this paper is to design a new trajectory error compensation method to improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction motion.

A trajectory error compensation method based on admittance-extended Kalman filter (AEKF) error fusion for human–exoskeleton interaction control. The admittance controller is used to calculate the trajectory error adjustment through the feedback human–exoskeleton interaction force, and the actual trajectory error is obtained through the encoder feedback of exoskeleton and the designed trajectory. By using the fusion and prediction characteristics of EKF, the calculated trajectory error adjustment and the actual error are fused to obtain a new trajectory error compensation, which is feedback to the knee exoskeleton controller. This method is designed to be capable of improving the trajectory tracking performance of the knee exoskeleton and enhancing the compliance of knee exoskeleton interaction.

Six volunteers conducted comparative experiments on four different motion frequencies. The experimental results show that this method can effectively improve the trajectory tracking performance and compliance of the knee exoskeleton in human–exoskeleton interaction.

The AEKF method first uses the data fusion idea to fuse the estimated error with measurement errors, obtaining more accurate trajectory error compensation for the knee exoskeleton motion control. This work provides great benefits for the trajectory tracking performance and compliance of lower limb exoskeletons in human–exoskeleton interaction movements.