Search Results (7,192)

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were conducted to evaluate the effects of different air bladder pressure conditions on metabolic rate and muscle activity. Simultaneously, pneumatic data corresponding to six common activities were collected, and a lightweight deep learning model was developed to enable high-precision motion classification. Finally, the model was deployed to an embedded platform to demonstrate its application potential. Results indicate that appropriate air bladder pressure significantly reduces quadriceps muscle activation and average metabolic cost. Furthermore, the deep learning model achieved 99.17% accuracy in recognizing the six activities and was successfully deployed to the embedded platform. This study validates the effectiveness of the intelligent pressurized thigh band in improving locomotor performance under static pressures and demonstrates the potential of air bladder pressure variations as a proxy indicator for movement intent for future closed-loop control. Full article

Remote photoplethysmography (rPPG) enables contactless estimation of cardiovascular signals from video, but most existing studies assume a fixed, stationary camera. This study investigates the feasibility of performing real-time moving-average rPPG (MA-rPPG) using a drone-mounted camera, where platform motion, vibration, and viewing distance introduce additional challenges. Building on our previously validated real-time MA-rPPG smart mirror platform, we reuse the smart mirror interface as a unified frontend for visualization, synchronization, and logging while adapting the MA-rPPG pipeline to operate on live video streamed from an off-the-shelf DJI Tello micro-drone. Feasibility experiments were conducted with 10 participants under controlled indoor lighting and constrained flight conditions, where the drone maintained a stable hover in front of a standing subject and facial video was processed in real time to estimate heart rate from a forehead region of interest. To avoid cross-modality bias and clarify the effect of the aerial imaging platform, drone-derived MA-rPPG outputs were compared against a fixed desktop-camera MA-rPPG reference using the same trained model, enabling a controlled, like-for-like evaluation. The results indicate that continuous heart-rate estimation from a drone camera is feasible in our controlled hover-only setup, while agreement tended to vary with hover stability and effective facial resolution. This work is presented strictly as a feasibility-stage investigation and does not claim clinical validity. The findings provide an experimental baseline and operating-envelope insight for future motion-robust rPPG on mobile and aerial health-sensing platforms. Full article

This paper presents a novel bio-inspired fluid dynamics framework that unifies path planning and control within a single continuous navigation process. Unlike conventional approaches that separate trajectory generation and execution, the proposed method models the robot as a particle immersed in an artificial fluid field, where the goal acts as a sink and obstacles modify the flow to produce collision-free motion. To ensure global optimality and eliminate local minima traps, the framework incorporates a sampling-based enhancement that evaluates multiple trajectories within high-flow regions and selects the optimal path using graph-based optimization. A fluid-based control law directly converts the velocity field into robot motion commands, enabling seamless integration between planning and execution. Theoretical stability is established using Lyapunov analysis, guaranteeing convergence to the goal. Extensive experiments on a Pioneer P3-DX robot demonstrate that the proposed approach achieves execution speeds 1.5 to 9.7 times faster than A*, PRM, and RRT*, while producing paths 3.6% to 29.5% shorter. Furthermore, the unified framework provides smooth and accurate motion with tracking errors within ±0.1 m. These results confirm that the proposed method improves path quality, computational efficiency, and real-time navigation performance. Full article

This research addresses the coupling issue between speed tracking and vertical posture in articulated unmanned mining trucks within unstructured environments. An extension theory-based coordinated control strategy is proposed, incorporating both articulation joint safety and vehicle stability. The control framework employs extension theory to classify operational modes based on articulation angle and velocity deviation. For longitudinal motion, active disturbance rejection control (ADRC) is adopted to mitigate the influence of varying payload mass and road slope on speed tracking performance. For vertical dynamics, a soft actor–critic (SAC) algorithm regulates active suspension to improve ride comfort. Both simulations and hardware-in-the-loop testing results demonstrate the superiority of the proposed strategy: coordinated control maintains speed tracking error below 4%, reduces body acceleration by 16.1%, 11.9%, and 17.5%, and improves articulation angle oscillations by 12.6%, 14.6%, and 15.1% across scenarios, confirming the strategy’s enhanced performance over conventional single-loop control approaches. Full article

Ground vehicles navigating uneven terrain must simultaneously guarantee motion safety and efficiency. Safety requires that the planned waypoints lie in highly traversable terrain, while ensuring vehicle reachability to these waypoints, which must be kinematically feasible. Efficiency demands fewer detours and smoother paths that avoid excessive vehicle acceleration and steering. However, existing path planning research for uneven terrain fails to comprehensively integrate vehicle kinematic constraints, terrain factors, path smoothness, rollover risk, and total path length. To address this problem, this paper proposes a novel navigation framework. It first integrates terrain slope, flatness, elevation variation, and sparsity to generate a 2D global terrain traversability cost map. Subsequently, a three-phase path planning algorithm integrates A*, guided Rapidly-exploring Random Tree (RRT), and our proposed Kinematic and Terrain-Aware Probabilistic Roadmap (KT-PRM) local re-planning algorithm, which jointly considers multiple factors including ground vehicle kinematic constraints, terrain factors, path smoothness, rollover risk, and path length. This three-phase combination delivers safe, smooth, and short global paths over uneven terrain within a relatively short planning time. Finally, Nonlinear Model Predictive Control (NMPC) is employed for path tracking in the framework. Experiments were conducted in both simulated and real-world uneven terrain environments. The results demonstrated that the three-phase path planning algorithm integrated with our proposed KT-PRM algorithm achieves comprehensive performance in generating safer, smoother, and shorter paths. Our proposed navigation framework achieves safer and more efficient navigation compared with existing navigation frameworks. Full article

Background: Modern electromechanical technologies can be integrated into strength training machines to regulate the magnitude, direction, and point of application of resistance during exercise, either through preprogrammed settings or adaptively in response to real-time kinematic data. However, this potential remains largely unexplored. The objective of this study was to investigate how these new-generation devices may be managed to enable precise control of the mechanical load applied to specific joint structures during strengthening exercises. Methods: A foundational framework of biomechanical equations was developed to establish the functional relationships between joint reaction forces and key variables, including kinematic parameters (joint angle, angular velocity, and angular acceleration) and resistance characteristics (magnitude, direction, and point of application). The analysis focused on analytically determined single-joint exercises, which are commonly employed in early-stage rehabilitation and athletic conditioning programs. Results: Application of the model to single-joint knee extension exercises demonstrated that the anterior cruciate ligament (ACL)-loading shear tibiofemoral force can be entirely eliminated throughout the full range of knee motion, without increasing either the tibiofemoral compressive force or the posterior cruciate ligament (PCL)-loading shear component, while preserving the desired peak and profile of the resistance torque. Conclusion: The proposed analytical framework enables a comprehensive understanding of how to regulate resistance parameters through advanced electromechanical technologies to minimize joint stress during single-joint strengthening exercises. Precise control of joint reaction forces during exercise is critical for the design of therapeutic and safety-enhanced training protocols. Full article

Background: Toe motor control contributes to postural stability and walking, yet clinical assessments have focused on toe-grip strength. The kinematics of selective toe extension under conditions requiring non-target toes to remain in contact with the ground remain poorly quantified. The aim of the present study was to characterize the kinematics and reliability of selective toe extension tasks using three-dimensional motion capture and to compare young and older adults. Methods: A total of 40 participants (20 young adults and 20 older adults) performed three tasks twice: all-toe extension, selective hallux extension, and selective four-toe extension (toes 2–5), with non-target toes required to remain in contact with the ground during selective tasks. Extension angles of the hallux, second, and fifth toes were quantified, and toe-grip strength was measured. Reliability was assessed using the intraclass correlation coefficient (ICC(1,2)). Toe angles were analyzed using two-way analysis of variance (group × condition, including resting and task conditions). Results: Toe angles demonstrated moderate to excellent reliability (ICC(1,2) = 0.81–0.95; 95% CI: 0.637–0.974). Compared with all-toe extension, both selective tasks showed reduced extension ranges, indicating an incomplete extension phenomenon in both groups. Significant group × condition interactions were observed for the hallux and second toes. During selective tasks, older adults exhibited greater unintended extension of non-target toes. Toe-grip strength was significantly lower in older adults (p < 0.001, Cohen’s d = 2.51). Conclusions: Selective toe extension tasks provide reliable kinematic indices of inter-toe motor control by quantifying incomplete extension and associated movements. Older adults showed greater associated movements under ground-contact constraints, suggesting age-related declines in motor coordination and possible reductions in toe flexor strength. Full article

Conventional pneumatic precision planters still face challenges in combining high-speed operation with accurate seed placement and embryo protection under zero-velocity seeding conditions. This study presents a dual-motor rotating–throwing seed-metering device that simultaneously overcomes these challenges. Instead of relying on conventional imprecise airflow to generate initial velocity, seeds are accelerated and released by a motor-driven spoon with precisely defined kinematic profiles. By accurately controlling seed-throwing velocity and angle, the system compensates for the forward motion of the machine to achieve zero-velocity seeding and accurate landing point control across the full speed range. The elimination of seed tubes prevents frictional embryo damage, particularly benefiting fragile seeds such as cotton or peanuts. High-speed imaging (1000 fps) verified uniform initial seed ejection conditions, stable trajectories, and landing position errors below 1.5 cm at 7–13 km/h. The proposed electromechanical approach provides accurate metering, zero-velocity seeding, and seed protection under high-speed conditions, overcoming the inherent limitations of airflow-dependent systems and offering a robust alternative for precision agriculture. Compared with conventional pneumatic meters, the proposed system reduced seed landing variation by over 50%, demonstrating superior robustness under 7–13 km/h operation. Full article

Practical robotic systems require control methods that remain reliable under limited computational resources, uncertain environments, and frequent changes in operating conditions. Although model-based control forms the foundation of high-performance robotics, real-world deployment is often hindered by model uncertainty, time-varying dynamics, and costly identification. As a result, low-order and intuitive control schemes remain dominant, yet such approaches often fail to sustain consistent performance under disturbances and parameter variations. Robust and adaptive control provide representative paradigms to address this gap, where a Disturbance Observer (DOB) suppresses uncertainty through disturbance rejection and a Parameter Adaptation Algorithm (PAA) improves model fidelity through online identification. However, direct integration of a DOB and a PAA often introduces functional interference, including mutual masking between disturbance compensation and parameter estimation, which compromises closed-loop stability. This paper proposes an Adaptive Disturbance Observer (ADOB) that integrates a DOB with online parameter adaptation. The ADOB updates the nominal model of the DOB in real time using a Recursive Least Squares (RLS)-based PAA, while a dual-filtering structure separates disturbance rejection and parameter identification. Stability is analyzed using hyperstability theory, where a smoothing mechanism enforces the slowly varying parameter assumption. Experiments on a one-Degree-of-Freedom (DOF) electromagnetic actuator and a three-DOF robotic manipulator demonstrate reductions in model uncertainty and tracking error compared with a conventional DOB. Full article

This study examines residual low-order (linear and constant) phase errors in interferometric synthetic aperture radar (InSAR) when compact, high-frequency radar sensors are mounted on commercial uncrewed aerial vehicles (UAVs). Although higher carrier frequencies and shorter standoff ranges enable fine-resolution interferometry, the same characteristics—together with UAV platform instability—make the system highly vulnerable to motion-induced phase errors, which can significantly degrade or even invalidate DEM reconstruction. This paper first quantifies the admissible motion-error bounds for reliable multi-baseline phase-gradient estimation, and then introduces a post-autofocus correction scheme that estimates the residual linear term from the interferometric fringe frequency and refines it via an FFT-based correlation objective, while the constant term is calibrated using ground control points (GCPs). The method is validated through simulations of a 24 GHz UAV demonstrator. To the best of our knowledge, this work provides the first post-autofocus demonstration of linear-and-constant residual-error mitigation for UAV-based high-frequency multi-baseline InSAR. In the considered K-band setting, the proposed approach reduces the DEM error from 42 m to 0.2 m (≈98% improvement). Full article

This paper presents the design, construction, and calibration of a modular low-cost 3D printer based on open-source technologies, developed as part of an academic research project. The printer utilises fused filament fabrication (FFF) and is built using locally available materials and components, including a T-slot aluminium frame, NEMA 23 stepper motors, and an Arduino Mega 2560 with RAMPS 1.4 control board. The system integrates Marlin firmware and CURA slicing software, enabling autonomous operation via an LCD panel and encoder interface. A detailed methodology is provided for mechanical assembly, electronic integration, firmware configuration, and calibration procedures. Special attention is given to the challenges encountered during the initial testing phase, including filament feeding issues, thermal inconsistencies, and mechanical misalignments. Solutions such as replacing inadequate components (e.g., fibreglass bushings with PTFE), adjusting spring tension, and refining firmware parameters are discussed. The results demonstrate successful printing of complex geometries after iterative calibration, validating the printer’s performance and replicability. This work contributes to the democratisation of additive manufacturing by offering a replicable, open-source solution for educational and prototyping purposes. The findings are relevant to machine design, automation, and robotics communities seeking practical insights into low-cost fabrication systems. Full article

Prescribed performance control (PPC) provides an effective framework for explicitly enforcing transient and steady-state performance constraints in autonomous underwater vehicle (AUV) trajectory tracking. However, in practical underwater environments, unavoidable localization errors, model uncertainties, and actuator saturation render conventional PPC schemes unsuitable due to their inherent semi-globality and singularity issues. To address these limitations, this paper proposes a globally nonsingular PPC framework for AUV tracking control under model uncertainties and input saturation. A novel error transformation function is developed, which fundamentally eliminates semi-global and singular behaviors without imposing additional control effort or modifying the initial error. Furthermore, a predefined-time disturbance observer is designed without requiring prior knowledge of disturbance bounds, and a predefined-time saturation compensator is introduced to mitigate actuator limitations. By integrating these components into a backstepping-based control structure, all closed-loop error signals are guaranteed to converge to an arbitrarily small neighborhood of the origin within a predefined time. Numerical simulations validate the effectiveness and superiority of the proposed method. Full article

Background: Restricted ankle dorsiflexion is common in basketball athletes and has been associated with altered lower-limb mechanics and reduced athletic performance. Although ankle joint mobilization is widely used to improve mobility, its effects on athletic performance remain unclear. The aim of this study was to examine whether adding posterior ankle joint mobilization to a structured exercise-based program incorporating eccentric strengthening and stretching improves ankle mobility and athletic performance in basketball athletes with restricted dorsiflexion. Primary outcomes were dorsiflexion range of motion (DF-ROM) and the Weight-Bearing Lunge Test (WBLT); secondary outcomes included jump performance, hop tests, Reactive Strength Index, Fatigue Index, and maximal isometric strength. Methods: In this randomized controlled trial, 38 basketball athletes (mean age 21.26 ± 2.52 years) with unilateral restricted ankle dorsiflexion were randomly allocated to an exercise-only group (n = 19) or to an exercise plus talocrural mobilization group (n = 19). The intervention lasted 5 weeks, with assessments performed at baseline, post-intervention, and at a 3-month follow-up. Results: Both groups improved ankle dorsiflexion; however, greater gains were observed in the intervention group for both dorsiflexion range of motion (DF-ROM; interaction p < 0.001; mean difference [MD] = 3.52° post-intervention and MD = 5.17° at follow-up) and the Weight-Bearing Lunge Test (WBLT; interaction p < 0.001; MD = 1.39 cm and MD = 1.34 cm, respectively). The intervention group showed superior improvements in countermovement jump and Triple Hop Test performance (both p < 0.001), as well as a small but statistically significant advantage in the Single Hop Test (p = 0.015). No between-group differences were found for the 6 m timed hop test, Reactive Strength Index, Fatigue Index, or maximal isometric strength (p > 0.05). Conclusions: Adding ankle joint mobilization to an eccentric strengthening and stretching program produced greater improvements in dorsiflexion and jump performance than exercise alone, without affecting speed, reactive ability, or maximal strength. Ankle mobilization may be a useful adjunct for improving functional mobility and selected performance outcomes in basketball athletes. Full article

Achieving precise sliding perception is crucial for enhancing human–machine interactions. Despite the extensive investigation of tactile sensors for static pressure detection, they still face challenges in detecting dynamic information such as sliding direction, speed, pressure and position in interactive touch scenarios. Herein, we propose a self-powered tactile interface that realizes motion-to-electricity generation by electrostatically regulating the carrier concentration and transport in the semiconductive layer with a top gate in sliding movement. This tactile sliding interface can distinguish various dynamic mechanical information by generating voltage signals related to the sliding direction, speed, pressure, and touch position without external bias voltage. By combining machine-learning algorithms, electrical signals of six representative sliding-touch interactions were accurately classified with a recognition accuracy of 98.33%. Furthermore, by integrating sensors into the smartphone’s side button, customizable functions such as volume control, screen unlocking, and music switching were achieved. This work provides an innovative mechanism for sliding sensing in interactive electronic and intelligent control systems. Full article