Search Results (443)

Background: Right ventricular (RV) dysfunction is common in advanced lung disease due to chronic pressure overload and altered pulmonary vascular mechanics. Lung transplantation (LTx) reduces RV afterload, and pulmonary rehabilitation (PR) may further enhance functional recovery. However, the combined effects of LTx and structured PR on RV myocardial deformation—particularly using speckle-tracking echocardiography (STE)—remain insufficiently characterized. Methods: This single-arm pre–post study included 20 bilateral lung transplant recipients who completed an 8-week, twice-weekly supervised outpatient PR program. Echocardiographic evaluation—including 2D measurements, M-mode, tissue Doppler imaging (TDI), and STE-derived strain parameters—was performed immediately post-discharge (baseline) and after PR. RV global longitudinal strain (RVGLS) and RV free-wall longitudinal strain (RVFWS) served as primary functional outcomes. Results: Improvements were observed in RV myocardial deformation after PR. RVGLS improved from a median of 15.52% to 16.64% (p = 0.004), and RVFWS increased from 15.82% to 17.10% (p = 0.001). RV mid-cavity diameter decreased significantly (p = 0.042), reflecting favorably altered RV geometry. Conventional parameters—including TAPSE, S′ velocity, RVEDA, and FAC—showed no statistically significant changes. These findings indicate that STE parameters are more sensitive than traditional indices for detecting early RV remodeling in the post-transplant period. Conclusions: Lung transplantation combined with a structured PR program was associated with early improvements in RV deformation indices measurable by STE, even when traditional echocardiographic indices remained within normal limits. STE may therefore serve as a sensitive tool for monitoring subclinical RV recovery after LTx and for assessing the additive benefits of PR. Full article

This study introduced Softball Pitching Plane (SPP), a best-fit geometric plane designed to characterize the throwing arm spatial trajectory during the windmill softball pitch. The purpose was to evaluate the reliability of this planar representation and determine whether deviations from the SPP were associated with ball velocity. Forty-nine adolescent softball pitchers each performed 15 drop-ball pitches (735 total pitches). Kinematics were recorded using a 15-sensor electromagnetic tracking system. A weighted orthogonal least-squares algorithm was applied to compute the best-fit plane across three intervals (WU–BR, TOP–BR, and DS–BR). Reliability was assessed using within-subject variability, leave-one-trial-out error, and ICCs. Linear mixed-effects models were used to examine associations between SPP parameters and ball velocity. The downswing–ball release interval of the wrist trajectory showed the most stable planar pattern (RMS = 0.053 m). SPP parameters demonstrated high reliability (CV ≤ 4.2%; ICC = 0.81–0.90). RMS deviation negatively predicted ball velocity at both within-pitcher (−0.11 km·h⁻¹ per cm, p = 0.003) and between-pitcher levels (−0.40 km·h⁻¹ per cm, p = 0.03). These findings indicate that, in adolescent softball pitchers, the SPP provides a reliable geometric description of throwing-arm motion during the downswing–ball release phase, with reduced deviation associated with higher pitch velocity. Full article

This paper proposes Social-only Particle Swarm Optimization-based Iterative Learning Control (SO-PSO-ILC) to address the limitations of conventional Iterative Learning Control (ILC) in model dependency and manual parameter tuning. The proposed method autonomously optimizes the learning gain using a social-only PSO variant. Comparative results on four distinct trajectories demonstrate superior performance: SO-PSO-ILC achieved a final RMSE of 0.0008 m in the linear path test and a precision 4.6 times higher than the baseline in the waveform path test. It also exhibits the fastest convergence rate, outperforming PSO-ILC in tracking accuracy and computational complexity while avoiding the convergence issues observed in WSA-ILC. The simulation results validate that swarm-optimized ILC provides a robust framework for repetitive tasks requiring high accuracy. Full article

Equipped with one degree of freedom in one-dimensional translation of the base, a mobile dual-arm robot (MDAR) is proposed in this paper, and the two arms and the base move simultaneously. As a result, the motion of the base has a significant influence on the motion of both end-effectors at the same time, and the relative positions of the two end-effectors change all the time. Therefore, this paper focuses on the main issues related to the presented MDAR in two key areas: the relative dynamics and relative force/position hybrid control. First, based on the D-H parametric method, the relative kinematics of the proposed MDAR is established, and the relative Jacobian matrix of the robot is derived. Secondly, the dynamic model of the proposed MDAR is constructed using the Lagrangian method. Furthermore, a closed-loop control strategy for relative force/position hybrid control of the MDAR based on the relative dynamics is proposed to enable the two end-effectors of the MDAR to track the planned trajectory accurately. Finally, a simulation is carried out on a dual-arm cutting robot (DACR) for a coal mine to prove the effectiveness of the proposed relative dynamics and the proposed relative force/position hybrid control law in terms of the absolute error (AE) and root mean square error (RMSE). The results show that the proposed relative dynamic model and relative force/position hybrid control can significantly reduce error of the DACR, effectively improve the adaptability and operation accuracy of the system to complex environment, and verify the feasibility and superiority of the method in practical application. Full article

Effective upper-extremity rehabilitation requires intensive and precise movement training, yet conventional therapies lack accurate motion tracking. Robotic exoskeletons address this limitation but are often hindered by ergonomic misalignment and limited adaptability. The AssistOn-Arm, a novel self-aligning exoskeleton, integrates ergonomic design and back-drivable actuation to enhance comfort and facilitate natural user interaction. This study aimed to assess the usability and ergonomics of the device in healthy participants and to conduct a pilot clinical evaluation in individuals with upper-extremity impairments. Thirty healthy participants and twelve patients with shoulder impairments performed predefined tasks under participant-active and device-active conditions. Kinematic data captured concurrently with AssistOn-Arm and Xsens MVN demonstrated strong agreement between conditions. Quantitative analysis revealed no significant differences (p > 0.05) in flexion, elevation, abduction–adduction, and external rotation, indicating reliable alignment with natural joint axes. Significant differences (p < 0.05) were observed only in sagittal hyperextension and internal rotation, reflecting device mechanical constraints. The study confirms the clinical feasibility of AssistOn-Arm as a sensor-driven, self-aligning exoskeleton that bridges engineering innovation and precision rehabilitation, paving the way for its integration into clinical practice. Full article

In recent years, active inference has gained attention in robot control owing to its adaptability to environmental changes. However, its reliance on gradient descent of variational free energy offers no guarantee of convergence to an optimal solution. In this study, we propose an approach that applies a smoothing filter to a pixel-based active inference controller to mitigate the risk of local minima. By smoothing the observed, predicted, and target values, the free energy function becomes smoother, yielding a broader distribution of gradients toward the target, thereby reducing the risk of being trapped in the local minima. In addition, in order to prevent excessive smoothing from eliminating the gradient of the free energy function, we also proposed a method for dynamically adjusting the intensity of smoothing based on prediction and target errors. To evaluate the effectiveness of our method, we applied it to two simulation environments: a simple object-tracking task using a 3-degrees-of-freedom camera, and a robot control task using a 2-degrees-of-freedom robotic arm, and compared it with the conventional active inference controller as a baseline. The experimental results demonstrate that the proposed approach achieves improved convergence performance over the conventional method. Full article

This paper mainly studies tax inspection decision-making technology, aiming to improve the accuracy and robustness of target recognition, state estimation, and autonomous decision making in complex environments by constructing an application that integrates visual, radar, and inertial navigation information. Tax inspection is a universally complex phenomenon, but little is known about the use of innovative technology to arm tax auditors with tools in monitoring it. Thus, based on the legitimacy theory, there is an agreement between taxpayers and the tax authorities regarding adequate compliance with tax legislation. The use of systemic controls by tax authorities is essential to track stakeholders’ contracts and ensure the upholding of this mandate. The case study is exploratory, using participant observation, and interventionist approach to a tax auditing. The results indicated that partnership between experienced tax auditors and IT tax auditors offered several tangible benefits to the in-house development and monitoring of an innovative application. It also indicates that OCR supports a data lake for inspectors in which stored information is available on standby during inspection. Furthermore, auditors’ use of mobile applications programmed with intelligent perception and tracking resources instead of using searches on mainframes streamlined the inspection process. The integration of professional skepticism, empathy among users, and technological innovation created a surge in independence among tax auditors and ensured focus. This paper’s contribution lies in the discussion of the enhancement of tax inspection through target recognition, drawing on legitimacy theory to rethink the relationship between taxpayers and tax authorities regarding adequate compliance with tax legislation, and presenting an exploratory case study using a participant observation, interventionist approach focused on a tax auditor. The implications of this study for policy makers, auditors, and academics are only the peak of the iceberg, as innovation in public administration presupposes efficiency. As a suggestion for future dimensions of research, we recommend the infusion of AI into these tools for further efficacy and effectiveness to mitigate fraud in the undue appropriation of taxes and undue competition. Full article

Real-time path planning for autonomous Unmanned Aerial Vehicles (UAVs) under strict hardware limitations remains a central challenge in embedded robotics. This study presents a refined Rapidly-Exploring Random Tree (RRT) algorithm implemented within an onboard embedded system based on a 32-bit STM32 microcontroller, demonstrating that real-time autonomous navigation can be achieved under low-power computation constraints. The proposed framework integrates a three-stage process—path pruning, Bézier curve smoothing, and iterative optimization—designed to minimize computational overhead while maintaining flight stability. By leveraging the STM32’s limited 72 MHz ARM Cortex-M3 core and 20 KB SRAM, the system performs all planning stages directly on the microcontroller without external computation. Experimental flight tests verify that the UAV can autonomously generate and follow smooth, collision-free trajectories across static obstacle fields with high tracking accuracy. The results confirm the feasibility of executing a full RRT-based planner on an STM32-class embedded platform, establishing a practical pathway for resource-efficient, onboard UAV autonomy. Full article

Inspecting the inner walls of large pressure vessels requires accurate weld seam recognition, complete coverage, and precise path tracking, particularly in low-feature environments. This paper presents a fully autonomous mobile robotic system that integrates weld seam detection, localization, and tracking to support ultrasonic testing. An improved Differentiable Binarization Network (DBNet) combined with the Spatially Variant Transformer (SVTR) model enhances digital stamp recognition, while weld paths are reconstructed from three-dimensional position data acquired via binocular stereo vision. To ensure complete traversal and accurate tracking, a global–local hierarchical planning strategy is implemented: the A-star (A*) algorithm performs global path planning, the Rapidly Exploring Random Tree Connect (RRT-Connect) algorithm handles local path generation, and point cloud normal–based spherical interpolation produces smooth tracking trajectories for robotic arm motion control. Experimental validation demonstrates a 94.7% digital stamp recognition rate, 95.8% localization success, 1.65 mm average weld tracking error, 2.12° normal fitting error, 98.2% seam coverage, and a tracking speed of 96 mm/s. These results confirm the system’s capability to automate weld seam inspection and provide a reliable foundation for subsequent ultrasonic testing in pressure vessel applications. Full article

Gas-insulated switchgear (GIS) serves as the core equipment in substations. Its partial discharge detection requires ultrasonic sensors to be precisely aligned with millimeter-level measurement points. However, existing technologies face three major bottlenecks: the lack of surface texture on GIS makes visual feature extraction difficult; strong electromagnetic interference in substations causes image noise and loss of feature point tracking; and fixed gain control easily leads to end-effector jitter, reducing positioning accuracy. To address these challenges, this paper first employs AprilTag visual markers to define GIS measurement point features, establishing an image-based visual servo model that integrates GIS surface curvature constraints. Second, it proposes an adaptive gain algorithm based on model predictive control, dynamically adjusting gain in real-time according to visual error, electromagnetic interference intensity, and contact force feedback, balancing convergence speed and motion stability. Finally, experiments conducted on a GIS inspection platform built using a Franka Panda robotic arm demonstrate that the proposed algorithm reduces positioning errors, increases positioning speed, and improves positioning accuracy compared to fixed-gain algorithms, providing technical support for the engineering application of GIS partial discharge detection robots. Full article

Current upper-limb rehabilitation robots primarily focus on training tasks involving free movements or static interactions with rigid objects. These paradigms lack simulation of complex object manipulation tasks encountered in daily life, thereby limiting the training of patients’ high-level sensorimotor integration capabilities. To address this gap, this study proposes an innovative robotic rehabilitation training system designed for functional occupational therapy. Specifically, the task of transporting a water cup was abstracted into a cup–ball system integrated with a robotic arm. The ball was modeled as a point mass, and kinematic and dynamic analyses of the system were conducted. A visual tracking method was employed to monitor the ball’s motion and calculate its slosh angle. Owing to the impaired fine motor control in stroke patients, a sloshing suppression control strategy integrating exponential filtering, feedforward force compensation, and impedance control was proposed to prevent the ball from spilling. Experiments validated the effectiveness of the proposed method. The results indicated that with full compensation, the oscillation rate of the ball was significantly reduced, and the smoothness of the hand force was markedly improved. This study presents an effective method for addressing dynamic uncertainty in rehabilitation robot training, thus significantly improving the functional relevance of the training. Full article

Flapping-wing micro-air vehicles (FWMAVs) exhibit unique aerodynamic characteristics that differ fundamentally from other aircraft, yet little is known about their dynamic stability derivatives. This study aims to identify pitch-rate stability derivatives of an in-house prototype, CKopter-1, to advance the modeling and control of bio-inspired flight. Experiments were conducted using a robotic-arm fan-array system that enabled prescribed pitching motions under controlled inflow. Aerodynamic forces and moments were measured with a six-axis load cell, while vehicle kinematics were captured using motion tracking and synchronized during post-processing. Tests consisted of quasi-static cycles and dynamic cycles at pitch rates of 35°/s, 58.8°/s, and 68.4°/s. The results revealed static instability below an angle of attack of 33°, a trim condition near 58.5°, and positive stability up to 72.5°. Dynamic cases showed clear pitch-rate effects in the longitudinal components, from which the derivatives were extracted. A comparison with previous studies confirmed comparable magnitudes, with systematic differences attributable to wing dihedral and tail length. This study demonstrates that the fan-array robotic-arm method enables stability derivative identification even beyond feasible flight regimes, providing valuable parameters for future flight dynamics modeling and control of FWMAVs. Full article

Dental caries is one of the most widespread chronic infectious diseases for humans. It results in localized destruction of dental hard tissues and has negative impacts on systemic health. Aims: This study aims to design, model, and control a novel 4-DOF dental robotic manipulator, Dentatron, specifically tailored for dental applications. The objectives were to (1) develop a compact robotic arm optimized for dental workspace constraints, (2) implement and compare three controllers—Computed Torque Control (CTC), Fuzzy Logic Control (FLC), and Neural Network Adaptive Control (NNAC), (3) evaluate tracking accuracy, transient response, and robustness in step and trajectory tasks, and (4) assess the potential of adaptive neural controllers for future clinical integration. Materials and Methods: The Dentatron system integrates a custom-designed robotic manipulator with adaptive controllers. The methodology consists of five main stages: robot modeling, control design, neural network adaptation, training, and evaluation. Simulations were performed to evaluate performance across joint tracking and Cartesian trajectory tasks using MATLAB 2022. Human-inspired trajectory design is fundamental to the Dentatron control and simulation framework to emulate the continuous curvature and minimum jerk characteristics of human upper-limb motion. The desired end-effector paths were formulated using fifth-degree polynomial trajectories that produce bell-shaped velocity profiles with gradual acceleration changes. Results: The study revealed that the Neural Network Adaptive Controller (NNAC) achieved the fastest convergence and lowest tracking error (<3 mm RMSE), consistently outperforming Fuzzy Logic Control (FLC) and Computed Torque Control (CTC). NNAC consistently provided precise joint tracking with minimal overshoot, while FLC ensured smoother but slower responses, and CTC exhibited large overshoot and persistent oscillations, requiring precise modeling to remain competitive. Conclusion: NNAC demonstrated the most robust and accurate control performance, highlighting its promise for safe, precise, and clinically adaptable robotic assistance in dentistry. Dentatron represents a step toward the development of compact dental robots capable of enhancing the precision and efficiency of future dental procedures. Full article

An instrumented total hip replacement (THR) implant capable of remote and continuous monitoring would be an attractive prospect for a surgeon to conveniently track the recovery of their patients. Measuring the relative motion of the prosthesis components would provide insight into joint kinematics and contribute to the detection of adverse events including impingement and subluxation. The aim of this study was to develop a sensing system to measure the relative orientation and translation of the prosthesis components. A tri-axis magnetometer and a permanent magnet were integrated into clinically available THR components, forming a magnetic position and orientation sensing system. A robotic arm was used to articulate the components through controlled motion routines and record the orientation of the components. The output of the robot arm and a camera tracking system were used to validate the performance of the sensing system. The sensing system measured the relative orientation of the components to two degrees of freedom with an RMSE of <4.0° and measured the displacement of the femoral head during an impingement-driven subluxation motion with an RMSE of 0.2 mm. This proof-of-concept work has shown that magnetic sensing technology can track the position and orientation of THR components. With further development, this sensing method could feature within an instrumented THR implant. Full article

As neuromuscular performance assessment has become a fundamental component of athlete monitoring, ensuring strong measurement reliability is essential for supporting accurate data-driven decision-making. Thus, the purpose of this study was twofold: (i) to examine the reliability of countermovement vertical jump (CMJ) force-time metrics obtained using a portable force plate system (Hawkin Dynamics) and (ii) to determine whether absolute and relative reliability scores differ between well-trained individuals (i.e., athletes) and those less familiar with CMJ force-plate testing (i.e., non-athletes). Seventy-four participants volunteered to take part in this investigation, of whom thirty-nine were NCAA Division-I baseball and track-and-field athletes and thirty-five age-matched non-athletes with no prior CMJ testing experience on force plates. After performing a standardized dynamic warm-up, participants performed three CMJs without arm swing while standing on a dual uniaxial force plate system sampling at 1000 Hz. Each jump trial was separated by a 30 s rest interval. Absolute and relative reliability were assessed using the coefficient of variation (CV) and intraclass correlation coefficient (ICC), respectively. The results revealed that 75% of the variables demonstrated excellent reliability. Specifically, absolute (CV < 10%) and relative (ICC > 0.750) reliability values were good to excellent for most force-time metrics of interest, including braking and propulsive phase duration, peak braking force, average propulsive power, reactive strength index-modified, countermovement depth, and jump height. In contrast, average and peak landing force and inter-limb asymmetry measures during the braking and propulsive phases displayed moderate to good reliability, whereas asymmetry-related variables during the landing phase exhibited poor reliability. In addition, athletes demonstrated lower CV and greater ICC across most metrics compared to non-athletes. Full article