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Home service robots operating indoors, such as inside houses and offices, require the real-time and accurate identification and location of target objects to perform service tasks efficiently. However, images captured by visual sensors while in motion states usually contain varying degrees of blurriness, presenting a significant challenge for object detection. In particular, daily life scenes contain small objects like fruits and tableware, which are often occluded, further complicating object recognition and positioning. A dynamic and real-time object detection algorithm is proposed for home service robots. This is composed of an image deblurring algorithm and an object detection algorithm. To improve the clarity of motion-blurred images, the DA-Multi-DCGAN algorithm is proposed. It comprises an embedded dynamic adjustment mechanism and a multimodal multiscale fusion structure based on robot motion and surrounding environmental information, enabling the deblurring processing of images that are captured under different motion states. Compared with DeblurGAN, DA-Multi-DCGAN had a 5.07 improvement in Peak Signal-to-Noise Ratio (PSNR) and a 0.022 improvement in Structural Similarity (SSIM). An AT-LI-YOLO method is proposed for small and occluded object detection. Based on depthwise separable convolution, this method highlights key areas and integrates salient features by embedding the attention module in the AT-Resblock to improve the sensitivity and detection precision of small objects and partially occluded objects. It also employs a lightweight network unit Lightblock to reduce the network’s parameters and computational complexity, which improves its computational efficiency. Compared with YOLOv3, the mean average precision (mAP) of AT-LI-YOLO increased by 3.19%, and the detection precision of small objects, such as apples and oranges and partially occluded objects, increased by 19.12% and 29.52%, respectively. Moreover, the model inference efficiency had a 7 ms reduction in processing time. Based on the typical home activities of older people and children, the dataset Grasp-17 was established for the training and testing of the proposed method. Using the TensorRT neural network inference engine of the developed service robot prototype, the proposed dynamic and real-time object detection algorithm required 29 ms, which meets the real-time requirement of smooth vision.

PurposeThis paper aims to present an adaptive robust sliding mode tracking controller for a 6 degree-of-freedom industrial assembly robot with parametric uncertainties and external disturbances. The controller is used to achieve both stringent trajectory tracking, accurate parameter estimations and robustness against external disturbances.Design/methodology/approachThe controller is designed based on the combination of sliding mode control, adaptive and robust controls and hence has good adaptation and robustness abilities to parametric variations and uncertainties. The unknown parameter estimates are updated online based on a discontinuous projection adaptation law. The robotic dynamics is first formulated in both joint spaces and workspace of the robot’s end-effector. Then, the design procedure of the adaptive robust sliding mode tracking controller and the parameter update law is detailed.FindingsComparative tests are also conducted to verify the effectiveness of the proposed controller, which show that the proposed controller achieves significantly better dynamic trajectory tracking performances as compared with conventional proportional derivative controller and sliding mode controller under the same conditions.Originality/valueThis is a new innovation for industrial assembly robot to improve assembly automation.

Multi-fingered dexterous hands provide superior dexterity in complex manipulation tasks due to their high degrees of freedom (DOFs) and biomimetic structures. Inspired by the anatomical structure of human tendons and muscles, numerous robotic hands powered by pneumatic artificial muscles (PAMs) have been created to replicate the compliant and adaptable features of biological hands. Nonetheless, PAMs have inherent nonlinear and hysteresis behaviors that create considerable challenges to achieving real-time control accuracy and stability in dexterous hands. In order to address these challenges, this paper proposes a hierarchical control architecture that employs a fuzzy PID strategy to optimize the nonlinear control of pneumatic artificial muscles (PAMs). The FPGA-based hardware integrates a multi-channel digital-to-analog converter (DAC) and a multiplexed acquisition module, facilitating the independent actuation of 20 PAMs and the real-time monitoring of 20 joints. The software implements a fuzzy PID algorithm that dynamically adjusts PID parameters based on both the error and the error rate, thereby effectively managing the nonlinear behaviors of the hand. Experimental results demonstrate that the designed control system achieves high precision in controlling the angle of a single finger joint, with errors maintained within ±1°. In scenarios involving multi-finger cooperative grasping and biomimetic motion demonstrations, the system exhibits excellent synchronization and real-time performance. These results validate the efficacy of the fuzzy PID control strategy and confirm that the proposed system fulfills the precision and stability requirements for complex operational tasks, providing robust support for the application of PAM-driven multi-fingered dexterous hands.

Background and purposes: Evidence regarding patients’ perception of fall risk is scarce. This study aimed to investigate the current situation and the associated factors of patients’ perception of fall risk, explore the mechanisms, and identify the subgroup of patients who may be at greater risk of having an erroneous perception. Methods: Participants were recruited from three wards of two tertiary general hospitals in China. Three-step multiple linear regression analyses were conducted including the demographic characteristics, health status factors, and fall-related factors as independent variables and Fall Risk Perception Questionnaire scores as dependent variable. The interactions of age, gender, and ward with fall-related factors were explored to test the potential moderating effects. We also examined the mediating role of fear of falling in the relationship of previous falls and age and perception of fall risk. Results: Patients who were in their older age, female, from endocrinology ward, had comorbidities, ambulatory aids, and fear of falling demonstrated a higher perception of fall risk which may or may not align with their actual risk of fall. The impact of previous fall injuries on perception of fall risk was significantly higher in older adults. The effects of fall-related training on patients’ perception of fall risk varied across wards. The fear of falling is a significant mediator between age and perception of fall risk. Conclusion: By understanding the self-perception of fall risk, health professionals would identify the population at higher risk of having an erroneous perception of their fall risk. This study increases health professional’s awareness and informs administrators to design and implement effective intervention and strategies that target patients’ perception of fall risk to promote patient safety.

Tactile sensing is essentially required for dexterous manipulation in robotic applications. Mimicking human perception of softness identification in a non-invasive fashion, thus achieving satisfactory interaction with fragile objects remains a grand challenge. Here, a scatheless measuring methodology based on the multisensory electronic skins to quantify the elastic coefficient of soft materials is reported. This recognition approach lies in the preliminary classification of softness by piezoelectric signals with a modified machine learning algorithm, contributing to an appropriate contact force assignment for subsequent quantitative measurements via strain sensing feedback. The integration of multifunctional sensing system allows the manipulator to hold capabilities of self-sensing and adaptive grasping motility in response to objects with the various softness (i.e., kPa-MPa). As a proof-of-concept demonstration, the biomimetic manipulator cooperates with the robotic arm to realize the intelligent sorting of oranges varying in freshness, paving the way for the development of microsurgery robots, human-machine interfacing, and advanced prosthetics.

ObjectiveTo evaluate the accuracy of self-perceived risk of falls in hospitalised adults and explore factors associated with the differences.DesignCross-sectional study.SettingWe conducted the study in two tertiary general hospitals located in Zhejiang province and Shandong province in China.Participants339 patients were recruited using convenient sampling. The majority of them were men (54%), aged 61–70 (40.1%) and had received secondary school education or lower (82%).Outcome measuresThe Fall Risk Perception Questionnaire and the Morse Fall Scale (MFS) were used to measure patients’ self-perceived risk of falls and nurses’ assessment. Other risk factors of falls were assessed to identify the determinants of disparities.ResultsMost patients (74.6%) had a high risk of falls according to MFS. Only 61.9% of the patients’ perceived risk matched with the assessment of nurses. Nearly one-third (27.5%) underestimated their fall risk, while the remaining (10.6%) overestimated. Multivariable logistic regression analyses revealed that older age, lower number of comorbidities, not having fear of falling and emergency department were the significant factors associated with underestimated risk of falls (p<0.05). Besides, endocrine department and having fall-related injuries were significantly associated with overestimated risk of falls (p<0.05).ConclusionHospitalised patients were proven to be poor at recognising their risk of falls. Measurement of patients’ self-perceived and health professionals’ assessment of fall risk should be conducted to evaluate the disparity. This study provides a solid foundation to raise medical staff’s awareness of the targeted population, identify the underlying factors and implement tailored fall prevention strategies and education.

Entropy-based thresholding is a widely used technique for medical image segmentation. Its principle is to determine the optimal threshold by maximizing or minimizing the image’s entropy, dividing the image into different regions or categories. The intensity distributions of objects and backgrounds often overlap and contain many outliers, making segmentation extremely difficult. In this paper, we introduce a novel thresholding method that incorporates the Cauchy distribution into the Tsallis entropy framework based on Bayesian estimation. By introducing Bayesian prior probability estimation to address the overlap in intensity distributions between the two classes, we enhance the estimation of the probability that a pixel belongs to either class. Additionally, we utilize the Cauchy distribution, known for its heavy-tailed characteristics, to fit grayscale pixel distributions with outliers, enhancing tolerance to extreme values. The optimal threshold is derived through the optimization of an information measure formulated using updated Tsallis entropy. Experimental results demonstrate that the proposed method, called Cauchy-TB, achieves significant superiority to existing approaches on two public medical brain image datasets.

Research interest in three-dimensional (3D) printing has been greatly aroused since 1990 due to its outstanding merits, such as freedom of design, mass customization, waste minimization and fast prototyping complex structures. To formally elaborate the research status of the 3D printing field, a bibliometric analysis is applied to evaluate the related publications from 1990 to 2020 based on the Science Citation Index Expanded database and Social Science Citation Index database. The overview with detailed discussions is cataloged by keywords, citation, h-index, year, journal, institution, country, author, patent and review. The statistical results show that the United States plays a dominant role in this research field, followed by China and the UK. Singapore is the most productive country with the highest average citations per publication (ACPP), and the second most cooperative country. Among all the institutions, Chinese Academy of Sciences is most productive, and Harvard University has the highest ACPP and h-index. Among all the journals, Materials ranks first in the number of publications in this field. The most attractive research area is “Materials science, Multidisciplinary”, with 4053 publications. Moreover, the major hot topics derived from authors’ keywords are “3D printing”, “additive manufacturing” and “tissue engineering”. Commercial and medical applications appear to be the initial driving force and end goal for the development of the 3D printing technology.

As the end-effector of a humanoid robot, the dexterous hand plays a crucial role in the process of robot execution. However, due to the complicated and delicate structure of the human hand, it is difficult to replicate human hand functionality, balancing structural complexity, and cost. To address the problem, the article introduces the design and development of a multi-finger synergistic adaptive humanoid dexterous hand with underactuation flexible articulated fingers and integrated pressure sensors. The proposed hand achieves force feedback control, minimizes actuator use while enabling diverse grasping postures, and demonstrates the capability to handle everyday objects. It combines advanced bionics with innovative design to optimize flexibility, ease of manufacturing, and cost-effectiveness.

This study aims to assess hand function recovery in stroke patients during the mid-to-late Brunnstrom stages and to encourage active participation in rehabilitation exercises. To this end, a deep residual network (ResNet) integrated with Focal Loss is employed for gesture recognition, achieving a Macro F1 score of 91.0% and a validation accuracy of 90.9%. Leveraging the millimetre-level precision of Leap Motion 2 hand tracking, a mapping relationship for hand skeletal joint points was established, and a static assessment gesture data set containing 502,401 frames was collected through analysis of the FMA scale. The system implements an immersive augmented reality interaction through the Unity development platform; C# algorithms were designed for real-time motion range quantification. Finally, the paper designs a rehabilitation system framework tailored for home and community environments, including system module workflows, assessment modules, and game logic. Experimental results demonstrate the technical feasibility and high accuracy of the automated system for assessment and rehabilitation training. The system is designed to support stroke patients in home and community settings, with the potential to enhance rehabilitation motivation, interactivity, and self-efficacy. This work presents an integrated research framework encompassing hand modelling and deep learning-based recognition. It offers the possibility of feasible and economical solutions for stroke survivors, laying the foundation for future clinical applications.