Search Results (101)

Camellia oleifera is one of the four largest woody oil species in the world, with more than 5 million hectares planted in China alone. Reducing bud damage and improving harvesting net rate and efficiency have become the key challenges to mechanized harvesting of Camellia oleifera fruits. This paper presents a novel comb-spiral impact harvesting device primarily composed of four parts, which are lifting mechanism, picking mechanism, rotating mechanism, and tracked chassis. The workspace of the four-degree-of-freedom lifting mechanism was simulated, and the harvesting reachable area was maximized using MATLAB R2021a software. The picking mechanism, which includes dozens of spirally arranged impact pillars, achieves high harvesting efficiency through impacting, brushing, and dragging, while maintaining a low bud shedding rate. The rotary mechanism provides effective harvesting actions, and the tracked chassis guarantees free movement of the equipment. Simulation experiments and field validation experiments indicate that optimal performance can be achieved when the brushing speed is set to 21.45 r/min, the picking finger speed is set to 341.27 r/min, and the picking device tilt angle is set to 1.0°. With these parameters, the harvesting quantity of Camellia oleifera fruits is 119.75 kg/h, fruit shedding rate 92.30%, and bud shedding rate as low as 9.16%. This new model for fruit shedding and the comb-spiral impact harvesting principle shows promise as a mechanized harvesting solution for nut-like fruits. Full article

Monitoring forearm muscle contraction force in home-based rehabilitation remains challenging. Electromyography (EMG), as a standard technique, is considered impractical and complex for independent use by patients at home, which poses a risk of device misattachment and inaccurate recorded data. Considering the muscle-related modality, several studies have demonstrated an excellent correlation between stretch sensors and EMG, which provides significant potential for addressing the monitoring issue at home. Additionally, due to its flexible nature, it can be attached to the finger, which facilitates the logging of the kinematic mechanisms of a finger. This study proposes a method for estimating forearm muscle contraction in a cylinder grasping environment during home-based rehabilitation using a stretch-sensor glove. This study employed support vector machine (SVM), multi-layer perceptron (MLP), and random forest (RF) to construct the estimation model. The root mean square (RMS) of the EMG signal, representing the muscle contraction force, was collected from 10 participants as the target learning for the stretch-sensor glove. This study constructed an experimental design based on a home-based therapy protocol known as the graded repetitive arm supplementary program (GRASP). Six cylinders with varying diameters and weights were employed as the grasping object. The results demonstrated that the RF model achieved the lowest root mean square error (RMSE) score, which differed significantly from the SVM and MLP models. The time series waveform comparison revealed that the RF model yields a similar estimation output to the ground truth, which incorporates the contraction–relaxation phases and the muscle’s contraction force. Additionally, despite the subjectivity of the participants’ grasping power, the RF model could produce similar trends in the muscle contraction forces of several participants. Utilizing a stretch-sensor glove, the proposed method demonstrated great potential as an alternative modality for monitoring forearm muscle contraction force, thereby improving the practicality for patients to self-implement home-based rehabilitation. Full article

The advent of mixed reality (MR) systems has revolutionized human–computer interactions by seamlessly integrating virtual elements with the real world. Devices like the HoloLens 2 (HL2) enable intuitive, hands-free interactions through advanced hand-tracking technology, making them valuable in fields such as education, healthcare, engineering, and training simulations. However, despite the growing adoption of MR, there is a noticeable lack of comprehensive comparisons between the hand-tracking accuracy of the HL2 and high-precision benchmarks like motion capture systems. Such evaluations are essential to assess the reliability of MR interactions, identify potential tracking limitations, and improve the overall precision of hand-based input in immersive applications. This study aims to assess the accuracy of HL2 in tracking hand position and measuring kinematic hand parameters, including joint angles and lateral pinch span (distance between thumb and index fingertips), using its tracking data. To achieve this, the Vicon motion capture system (VM) was used as a gold-standard reference. Three tasks were designed: (1) finger tracing of a 2D pattern in 3D space, (2) grasping various common objects, and (3) lateral pinching of objects with varying sizes. Task 1 tests fingertip tracking, Task 2 evaluates joint angle accuracy, and Task 3 examines the accuracy of pinch span measurement. In all tasks, HL2 and VM simultaneously recorded hand positions and movements. The data captured in Task 1 were analyzed to evaluate HL2’s hand-tracking capabilities against VM. Finger rotation angles from Task 2 and lateral pinch span from Task 3 were then used to assess HL2’s accuracy compared to VM. The results indicate that the HL2 exhibits millimeter-level errors compared to Vicon’s tracking system in Task 1, spanning in a range from 2 mm to 4 mm, suggesting that HL2’s hand-tracking system demonstrates good accuracy. Additionally, the reconstructed grasping positions in Task 2 from both systems show a strong correlation and an average error of 5°, while in Task 3, the accuracy of the HL2 is comparable to that of VM, improving performance as the object thickness increases. Full article

Assistive technologies, particularly multi-fingered robotic hands (MFRHs), are critical for enhancing the quality of life for individuals with upper-limb disabilities. However, achieving precise and stable control of such systems remains a significant challenge. This study proposes an Improved Grey Wolf Optimization (IGWO)-tuned Linear Quadratic Regulator (LQR) to enhance the control performance of an MFRH. The MFRH was modeled using Denavit–Hartenberg kinematics and Euler–Lagrange dynamics, with micro-DC motors selected based on computed torque requirements. The LQR controller, optimized via IGWO to systematically determine weighting matrices, was benchmarked against PID and PID-PSO controllers under diverse input scenarios. For step input, the IGWO-LQR achieved a settling time of 0.018 s with zero overshoot for Joint 1, outperforming PID (settling time: 0.0721 s; overshoot: 6.58%) and PID-PSO (settling time: 0.042 s; overshoot: 2.1%). Similar improvements were observed across all joints, with Joint 3 recording an IAE of 0.001334 for IGWO-LQR versus 0.004695 for PID. Evaluations under square-wave, sine, and sigmoid inputs further validated the controller’s robustness, with IGWO-LQR consistently delivering minimal tracking errors and rapid stabilization. These results demonstrate that the IGWO-LQR framework significantly enhances precision and dynamic response. Full article

Piano-based occupational therapy has emerged as an engaging and effective rehabilitation strategy for improving upper limb motor functions. However, a lack of comprehensive biomechanical modeling, objective rehabilitation assessment, and real-time fatigue monitoring has limited its clinical optimization. This study developed a comprehensive “key–finger–exoskeleton” biomechanical model based on Hill-type muscle dynamics and rigid-body kinematics. A three-dimensional muscle synergy analysis method using non-negative tensor factorization (NTF) was proposed to quantitatively assess rehabilitation effectiveness. Furthermore, a real-time Comprehensive Muscle Fatigue Index (CMFI) based on multi-muscle coordination was designed for fatigue monitoring during therapy. Experimental validations demonstrated that the biomechanical model accurately predicted interaction forces during piano-playing tasks. After three weeks of therapy, patients exhibited increased synergy modes and significantly improved similarities with healthy subjects across spatial, temporal, and frequency domains, particularly in the temporal domain. The CMFI showed strong correlation (r > 0.83, p < 0.001) with subjective fatigue ratings, confirming its effectiveness in real-time fatigue assessment and training adjustment. The integration of biomechanical modeling, synergy-based rehabilitation evaluation, and real-time fatigue monitoring offers an objective, quantitative framework for optimizing piano-based rehabilitation. These findings provide important foundations for developing intelligent, adaptive rehabilitation systems. Full article

This study presents the design, fabrication, and experimental validation of a two-finger robotic gripper featuring a 135° V-shaped fingertip profile tailored for lightweight waste collection in laboratory-scale environmental robotics. The gripper was developed with a strong emphasis on cost-effectiveness and manufacturability, utilizing a desktop 3D printer and off-the-shelf servomotors. A four-bar linkage mechanism enables parallel jaw motion and ensures stable surface contact during grasping, achieving a maximum opening range of 71.5 mm to accommodate common cylindrical objects. To validate structural integrity, finite element analysis (FEA) was conducted under a 0.6 kg load, yielding a safety factor of 3.5 and a peak von Mises stress of 12.75 MPa—well below the material yield limit of PLA. Experimental testing demonstrated grasp success rates of up to 80 percent for typical waste items, including bottles, disposable cups, and plastic bags. While the gripper performs reliably with rigid and semi-rigid objects, further improvements are needed for handling highly deformable materials such as thin films or soft bags. The proposed design offers significant advantages in terms of rapid prototyping (a print time of approximately 10 h), modularity, and low manufacturing cost (with an estimated in-house material cost of USD 20 to 40). It provides a practical and accessible solution for small-scale robotic waste-collection tasks and serves as a foundation for future developments in affordable, application-specific grippers. Full article

Glove-based human–machine interfaces (HMIs) offer a natural, intuitive way to capture finger motions for gesture recognition, virtual interaction, and robotic control. However, many existing systems suffer from complex fabrication, limited sensitivity, and reliance on external power. Here, we present a flexible, self-powered glove HMI based on a minimalist triboelectric nanogenerator (TENG) sensor composed of a conductive fabric electrode and textured Ecoflex layer. Surface micro-structuring via 3D-printed molds enhances triboelectric performance without added complexity, achieving a peak power density of 75.02 μW/cm² and stable operation over 13,000 cycles. The glove system enables real-time LED brightness control via finger-bending kinematics and supports intelligent recognition applications. A convolutional neural network (CNN) achieves 99.2% accuracy in user identification and 97.0% in object classification. By combining energy autonomy, mechanical simplicity, and machine learning capabilities, this work advances scalable, multi-functional HMIs for applications in assistive robotics, augmented reality (AR)/(virtual reality) VR environments, and secure interactive systems. Full article

Virtual reality (VR) is a promising tool in spinal cord injury (SCI) rehabilitation, particularly through virtual adaptations of functional tests like the Box and Block test (BBT). However, a comprehensive dynamic comparison between real and virtual BBT is lacking. This study investigates the kinematic and electromyographic (EMG) differences between healthy individuals and SCI patients performing both real (RBBT) and virtual (VBBT) versions of the BBT. An electromagnetic motion-tracking system, an instrumented glove, and surface EMG electrodes were used to capture hand trajectories, joint angles, and forearm muscle activation. The analysis included cycle-averaged and temporal kinematic and EMG parameters. Our findings reveal that both groups showed increased trajectory length and velocity peaks during the VBBT, with more pronounced increases in SCI patients. Unlike healthy individuals, SCI patients also showed increased finger and thumb flexion during VBBT. Cycle-averaged EMG values were lower in healthy participants during VBBT, likely due to reduced motor demands and lack of real grasping. Conversely, SCI patients exhibited higher muscle activity, suggesting impaired coordination and compensatory overactivation. Healthy individuals showed consistent temporal kinematic synergies and muscle activation, whereas they were altered in SCI patients, especially during reaching. These findings highlight the need for rehabilitation strategies to improve motor control and feedback integration. Full article

Soft anthropomorphic robotic grippers are attractive because of their inherent compliance, allowing them to adapt to the shape of grasped objects and the overload protection needed for safe human–robot interaction or gripping delicate objects with sophisticated control. The anthropomorphic design allows the gripper to benefit from the biological evolution of the human hand to create a multi-functional robotic end effector. Entirely soft grippers could be more efficient because they yield under high loads. A trending solution is a hybrid gripper combining soft and rigid elements. This work describes a prototype of an anthropomorphic, underactuated five-finger gripper with a direct pneumatic drive from soft bending actuators and an integrated resistive tactile sensor array. It is a hybrid construction with soft robotic structures and rigid skeletal elements, which reinforce the body, focus the direction of the actuator’s movement, and make the finger joints follow the forward kinematics. The hand is equipped with a resistive tactile dielectric elastomer sensor array that directly triggers the hand’s actuation in the sense of reflexes. The hand can execute precision grips with two and three fingers, as well as lateral grip and strong grip types. The softness of the actuation allows the finger to adapt to the shape of the objects. Full article

To address the limitations of single-chamber soft grippers, such as constant curvature, insufficient motion flexibility, and restricted fingertip movement, this study proposes a soft gripper inspired by the structure of the human hand. The designed soft gripper consists of three fingers, each comprising three soft joints and four phalanges. The air chambers in each joint are independently actuated, enabling flexible grasping by adjusting the joint air pressure. The constraint layer is composed of a composite material with a mass ratio of 5:1:0.75 of PDMS base, PDMS curing agent, and PTFE, which enhances the overall finger stiffness and fingertip load capacity. A nonlinear mathematical model is established to describe the relationship between the joint bending angle and actuation pressure based on the constant curvature assumption. Additionally, the kinematic model of the finger is developed using the D–H parameter method. Finite element simulations using ABAQUS analyze the effects of different joint pressures and phalange lengths on the grasping range, as well as the fingertip force under varying actuation pressures. Bending performance and fingertip force tests were conducted on the soft finger actuator, with the maximum fingertip force reaching 2.21 N. The experimental results show good agreement with theoretical and simulation results. Grasping experiments with variously sized fruits and everyday objects demonstrate that, compared to traditional single-chamber soft grippers, the proposed humanoid joint-inspired soft gripper significantly expands the grasping range and improves grasping force by four times, achieving a maximum grasp weight of 0.92 kg. These findings validate its superior grasping performance and potential for practical applications. Full article

Dexterous robotic grasping with multifingered hands remains a critical challenge in non-visual environments, where diverse object geometries and material properties demand adaptive force modulation and tactile-aware manipulation. To address this, we propose the Reinforcement Learning-Based Multimodal Perception (RLMP) framework, which integrates human-like grasping intuition through operator-worn gloves with tactile-guided reinforcement learning. The framework’s key innovation lies in its Tactile-Driven DCNN architecture—a lightweight convolutional network achieving 98.5% object recognition accuracy using spatiotemporal pressure patterns—coupled with an RL policy refinement mechanism that dynamically correlates finger kinematics with real-time tactile feedback. Experimental results demonstrate reliable grasping performance across deformable and rigid objects while maintaining force precision critical for fragile targets. By bridging human teleoperation with autonomous tactile adaptation, RLMP eliminates dependency on visual input and predefined object models, establishing a new paradigm for robotic dexterity in occlusion-rich scenarios. Full article

Ensuring the accurate tracking of hand and fingers movements is an ongoing challenge for upper limb rehabilitation assessment, as the high number of degrees of freedom and segments in the limited volume of the hand makes this a difficult task. The objective of this study is to evaluate the performance of two markerless approaches (the Leap Motion Controller and the Google MediaPipe API) in comparison to a marker-based one, and to improve the precision of the markerless methods by introducing additional data processing algorithms fusing multiple recording devices. Fifteen healthy participants were instructed to perform five distinct hand movements while being recorded by the three motion capture methods simultaneously. The captured movement data from each device was analyzed using a skeletal model of the hand through the inverse kinematics method of the OpenSim software. Finally, the root mean square errors of the angles formed by each finger segment were calculated for the markerless and marker-based motion capture methods to compare their accuracy. Our results indicate that the MediaPipe-based setup is more accurate than the Leap Motion Controller-based one (average root mean square error of 10.9° versus 14.7°), showing promising results for the use of markerless-based methods in clinical applications. Full article

This study has designed an easy-to-wear parallel continuum robot-based hand rehabilitation system that supports and enhances the finger’s flexion, extension, abduction, and adduction movements. The primary novelty of the proposed system lies in its ability to focus therapeutic exercises on a single joint, a feature not commonly found in existing rehabilitation robots. A kinematic model of the system was developed, and to perform both kinematic and dynamic analyses, a multibody model was constructed in the MATLAB Simulink environment. Joint angle control was implemented using a nominal controller, and to account for individual uncertainties in joint dynamics, a neuroadaptive controller was integrated with the nominal controller. This approach aims for the neural network architecture to learn these uncertainties during control iterations and incorporate them into the control, resulting in a robust controller. Thus, a model reference control approach was proposed for active and passive rehabilitation processes. The system model was tested in a simulation environment, and then all tests were repeated in the physical system. The simulation and real system results include the real system’s open-loop responses, nominal controller responses for each joint, responses, and the results for active, passive, and assistive control modes. Full article

This paper proposes an underactuated dexterous hand with novel bidirectional self-locking joints (BSJs) that enable multiple fingertip motion trajectories. The BSJ design integrates a locking wheel, rack, finger side walls, and a self-holding electromagnetic actuator, combining rack-and-pinion transmission with friction self-locking principles. Building on the BSJ concept, an underactuated dexterous hand is developed. The study begins with an analysis of BSJ’s deviation angle, establishing the minimum deviation angle critical to its operation. A detailed mechanical model of a BSJ is formulated, and its parameters are quantitatively analyzed to determine a safety static friction coefficient (0.177). Five distinct finger motion modes are designed and kinematic analysis focuses on the index finger and the generation of 57 unique fingertip active motion trajectories. Experimental validation included single finger performance tests that confirmed the diversity of fingertip trajectories and the hand’s ability to withstand loading in both forward and reverse directions. Through envelope and precision grasping experiments, the dexterous hand demonstrated its adaptability and ability to grasp objects of various sizes and shapes, such as strawberries, apples, student ID cards, and water bottles. This capability underscores its potential for a wide range of applications, from prosthetic hands for rehabilitation, where precision and adaptability are key, to robotic hands in industrial automation, offering flexibility in diverse tasks. Full article

This study describes the design, simulation, and development process of a rehabilitation glove driven by soft pneumatic actuators. A new, innovative finger soft actuator design has been developed through detailed kinematic and workspace analysis of anatomical fingers and their actuators. The actuator design combines cylindrical and ribbed geometries with a reinforcing element—a thicker, less extensible structure—resulting in an asymmetric cylindrical bellow actuator driven by positive pressure. The performance of the newly designed actuator for the rehabilitation glove was validated through numerical simulation in open-source software. The simulation results indicate actuators’ compatibility with human finger trajectories. Additionally, a rehabilitation glove was 3D-printed from soft materials, and the actuator’s flexibility and airtightness were analyzed across different wall thicknesses. The 0.8 mm wall thickness and thermoplastic polyurethane (TPU) material were chosen for the final design. Experiments confirmed a strong linear relationship between bending angle and pressure variations, as well as joint elongation and pressure changes. Next, pseudo-rigid kinematic models were developed for the index and little finger soft actuators, based solely on pressure and link lengths. The workspace of the soft actuator, derived through forward kinematics, was visually compared to that of the anatomical finger and experimentally recorded data. Finally, an ergonomic assessment of the complete rehabilitation glove in interaction with the human hand was conducted. Full article