Search Results (371)

Continuous monitoring of lower-limb kinematics in natural environments is essential for gait analysis and rehabilitation but remains challenging due to the limitations of optical systems and the inaccuracy of sparse inertial sensor methods. To address this, we propose a high-precision, minimalist wearable system utilizing only three inertial measurement units placed on the pelvis and shanks. In the data preprocessing stage, engineering modifications are made based on the traditional gradient descent algorithm to implement adaptive channel adjustment on the acceleration and magnetic data of a single IMU, aiming to alleviate the impact of motion acceleration and external magnetic interference on the temporal feature manifold. Subsequently, a pure Transformer neural network is utilized to capture long-range temporal dependencies, reconstructing full lower-limb kinematics without relying on rigid biomechanical assumptions. The model was optimized and deployed on an STM32N647 microcontroller to achieve real-time edge inference with a low latency of approximately 17 ms. Experimental results demonstrate that the proposed method achieves a mean absolute error of 2.41° for level walking, significantly outperforming traditional constrained Kalman filter approaches. Furthermore, it maintains high tracking robustness during complex nonlinear movements such as squatting and lunging. In conclusion, this edge-computing-enabled framework provides an accurate, comfortable, and real-time solution for unconstrained human motion capture in daily scenarios. Full article

Resolving the kinematic redundancy of 7-DoF humanoid arms to generate natural, human-like motions remains a fundamental challenge in biomimetic robotics. This paper presents a hybrid inverse kinematics (IK) framework that learns a pose-dependent redundancy parameter and integrates it into a differential IK solver. Specifically, we employ the stereographic Shoulder–Elbow–Wrist (SEW) angle as a well-conditioned geometric parameterization. This formulation transforms the algorithmic singularity into a unidirectional half-line, which can be oriented outside the typical reachable workspace. To specify the optimal configuration within the self-motion manifold, a motion dataset was collected by teleoperating a humanoid arm via an anthropomorphic wearable exoskeleton. This approach translates operator-specific postural preferences into the robot’s joint space. A lightweight neural network was then trained to learn the mapping from end-effector poses to these operator-specific SEW angles. By incorporating the predicted SEW angle as a dynamic secondary objective in the null space of the primary tracking task, the proposed framework enables natural redundancy resolution while preserving end-effector tracking accuracy. Both simulations and real-robot experiments were conducted to validate the approach. Results show that, compared to the average performance of static fixed-parameter strategies, the proposed method improves the Joint Configuration Quality Index (CQI) by 22.5% and reduces energy costs by 11.3%. Moreover, the sub-millisecond inference latency (0.44 ms) facilitates seamless integration into real-time control pipelines. Full article

In human-robot collaboration (HRC), robots operate alongside humans within a shared workspace. During collaborative handling tasks, human movements are often highly individual and variable. To ensure smooth collaboration, the robot must adapt its trajectory to align with the motion of the human co-worker. Therefore, this work proposes a dynamic motion planner that enables the robot to track a dynamically changing reference trajectory. The motion planner is evaluated based on its ability to track the trajectory while respecting joint velocity and acceleration limits and avoiding kinematic singularities. When these constraints are at risk of being violated, the robot temporarily assumes a dominant role and attempts to approximate the reference trajectory as closely as possible. An evaluation using a KUKA iiwa in a laboratory setup demonstrates that the proposed motion planner can effectively track dynamically changing, physically feasible reference trajectories. Assuming that the reference trajectory can be human motion, the motion planner can harmonize human and robot movements during collaborative handling tasks. Full article

Robotic assistive devices, such as exoskeletons, are increasingly employed in walking rehabilitation. Therefore, the measurement of both movement kinematics and cognitive workload is important to understand this human–robot interaction in real-world contexts. To address this need this study presents the validation of a framework integrating inertial motion capture (Xsens) and eye-tracking sensor (Pupil Neon) within a Mixed Reality (Meta Quest 3) architecture. We developed an overground dual-task paradigm in which holographic numbers appear in the user’s peripheral vision. This setup actively stimulates visuospatial attention while quantifying kinematic and cognitive output. To validate the framework, the protocol has been tested on 30 healthy subjects across repeated exoskeleton training sessions. Statistical analyses revealed that the Coefficient of Multiple Correlation (CMC) and Spectral Arc Length (SPARC), calculated on the shank angular velocity, together with the Step Length Variability, exhibited significant time effects (p < 0.01), mapping the transition toward automated gait. Concurrently, pupillometric data demonstrated a measurable reduction in neurocognitive demand; specifically, the Task-Evoked Pupillary Response (TEPR) decreased significantly across progressive training sessions (p < 0.05). With this work, we validated a measurement protocol that aims to provide a novel methodology for objectively evaluating motor and cognitive adaptation in wearable assistive devices. Full article

Accurate and unobtrusive measurement of upper-limb kinematics is critical for advancing wearable sensing technologies used in industrial ergonomics, human–machine interaction, and real-time biomechanics monitoring. This study evaluates the performance of two soft, flexible wearable sensors—BendLabs biaxial angular displacement sensors and StretchSense capacitive stretch sensors—for quantifying wrist and elbow motions during simulated dynamic industrial tasks. Wrist flexion–extension and radial–ulnar deviation were measured using BendLabs sensors mounted on the dorsal hand, while elbow flexion–extension was captured using StretchSense sensors positioned along the elbow joint. A multi-camera optical motion capture system served as the reference standard. Sensor data were preprocessed using baseline correction, smoothing, denoising, and normalized cross-correlation techniques to support temporal alignment with motion-capture recordings. Across all activities, the BendLabs sensors demonstrated moderate agreement with motion capture for wrist kinematics, with generally better performance for radial–ulnar deviation than for flexion–extension. StretchSense sensors demonstrated stronger agreement with motion capture for elbow flexion–extension, with performance that was generally consistent across task types. These findings support the feasibility of soft wearable sensors for capturing upper-limb kinematics during simulated occupational tasks and highlight their potential for integration into ergonomic assessment, occupational monitoring systems, and future industrial wearable platforms. Full article

Research on human motion representation commonly investigates portable, wearable, and ergonomic sensing systems. Cameras, infrared sensors, and inertial measurement units (IMUs) are widely used to reproduce and validate human movement. Known limitations persist, including increased error during slow movements, the gimbal lock effect in Euler space, and the requirement for one sensor per joint. The objective of this work is twofold: first, to characterize the measurement accuracy of a commercial IMU sensor (BWT901BLE) under controlled conditions using a fixed-arm pendulum model that replicates the single-degree-of-freedom planar kinematics of elbow flexion–extension, comparing angular position, angular velocity, and angular acceleration outputs against a video-based reference system; and second, to describe and publish a complete data processing pipeline—from raw sensor readings to real-time biomechanical motion visualization within OpenSim—demonstrated through upper limb motion recordings from 6 participants, whose data were used to generate motion files and estimate muscle fiber lengths and activation patterns within OpenSim. Regarding sensor characterization, experiments compared sensor data against the video-based reference. The inter-sensor angular position mean error was 0.765° (100 Hz) and 0.445° (200 Hz); angular velocity mean error was 0.124°/s (100 Hz) and 0.277°/s (200 Hz). Direct Euler angle measurements outperformed quaternion-to-Euler conversion (mean RMSE 5.69° vs. 53.1° at 100 Hz; 5.08° vs. 41.8° at 200 Hz). Angular velocity showed the highest agreement with the video-based reference (mean RMSE 0.60 rad/s at 100 Hz and 0.43 rad/s at 200 Hz; mean R = 0.982 and 0.991). Raw accelerometer output showed negligible correlation with the video-based angular acceleration reference (mean R ≈ 0.00–0.05); however, acceleration derived from angular velocity differentiation achieved high accuracy (mean RMSE 4.43 rad/s² at 100 Hz and 3.06 rad/s² at 200 Hz; mean R = 0.976 and 0.989). Regarding the OpenSim integration, the real-time visualization pipeline achieved an effective frame rate of 40–50 fps with an estimated end-to-end latency of 35–50 ms, and the recorded motion data were used to estimate muscle fiber lengths and activation patterns through OpenSim’s analysis tools. These findings confirm that angular velocity is the most reliable output of this sensor class. Full article

This paper investigates the conflicting multiple constraints and safety challenges in humanoid robot teleoperation for nonprehensile transportation tasks. The robot’s complex workspace and high degrees of freedom frequently conflict with highly dynamic task requirements, imposing stringent demands on coordinated motion. To address these issues, this paper proposes a Multiple-Constraint Safety-Critical Control Framework (MC-SCCF) featuring a hierarchical three-layer architecture. The top layer guarantees intrinsic safety against workspace boundaries using a continuously differentiable reachability surrogate model and an improved control barrier function (CBF)-based safe velocity filter for smooth deceleration. The middle layer maps user commands into pose-coupled reference trajectories to ensure task-level object safety, satisfying strict non-slip and non-toppling constraints. The bottom layer utilizes a quadratic programming (QP)-based inverse kinematics solver to achieve self-collision avoidance, coordinated motion, and optimal configuration while strictly enforcing joint and manipulability limits. Simulations and hardware experiments demonstrate that the MC-SCCF achieves real-time, high-precision reachability evaluation and successfully coordinates task dynamics with physical constraints, enhancing operational safety and the human–robot interaction experience. Full article

Upper-limb exoskeletons require precise geometric alignment between the device’s mechanical axes and the user’s anatomical joints to preserve physiological mobility and prevent functional constraints; however, many occupational exoskeleton designs oversimplify scapulothoracic mobility, potentially reducing the functional workspace and leading to kinematic misalignment during arm elevation tasks. In this context, the present study addresses this limitation by developing the design, kinematic modeling, and experimental validation of a 7-DoF passive upper-limb exoskeleton organized into dorsal, shoulder, and elbow modules, where the proposed architecture explicitly incorporates 3-DoFs in the dorsal region to accommodate scapular motion within a unified serial kinematic chain. From a modeling standpoint, the kinematic formulation is established using the Denavit–Hartenberg convention, enabling the analysis of the workspace, the properties of the Jacobian matrix, and the identification of potential singular configurations; simulation results demonstrate a continuous workspace within the evaluated functional range, with no singularities detected in the region of interest. Regarding experimental validation, two complementary approaches are implemented: a 2D video-based analysis using Kinovea compares joint trajectories with and without the exoskeleton, revealing strong kinematic agreement ($RMSE⩽$ 6.11 mm, $R^2\ge$ 0.8746), while a 3D motion-capture validation using the Qualisys system evaluates the kinematic coupling between the human arm and the exoskeleton during assisted movement, yielding high correspondence between both trajectories ($R^2$ = 0.975). Overall, the results confirm the geometric consistency of the proposed architecture and provide a solid methodological foundation for the future development of passive or hybrid upper-limb exoskeletons with integrated dorsal mobility. Full article

The use of prosthetic devices can significantly improve the quality of life of individuals with limb amputations. However, existing prosthetic hands face multiple engineering and manufacturing challenges, making them economically inaccessible to a large portion of the population. This study focuses on the design and analysis of a cost-effective prosthetic hand capable of performing five fundamental grasp types: tripod, cylindrical, spherical, lateral, and pinch. The development process began with a biomechanical analysis of the human hand, followed by the derivation of a kinematic model. To ensure anthropomorphic fidelity, finger trajectories were synthesized using a computer vision-based algorithm that captured natural human motion. These trajectories were then mapped to the prosthetic control system. Experimental validation was conducted through rigorous goniometric analysis of the prototype’s execution. The results demonstrated the system’s effectiveness in replicating functional grasps, with a Root Mean Square Error (RMSE) within acceptable thresholds for assistive tasks. While the prototype achieved high motion correspondence, higher deviations were observed in distal joints due to mechanical transmission resistance and spring-return torque requirements. This work provides a scalable framework for tendon-driven prostheses, balancing advanced trajectory synthesis with a robust and accessible mechanical architecture. Full article

Wearable inertial measurement units are widely used for human motion analysis due to their portability; however, most IMU-based motion capture systems rely on dense sensor configurations that increase cost, complexity, and usability challenges in real-world applications. To address this limitation, this paper presents a sparse inertial human motion reconstruction framework that uses only five wearable sensors while maintaining real-time performance and biomechanical plausibility. The proposed framework integrates Movella Xsens DOT IMUs with a learning-based inverse kinematics pipeline and a real-time biomechanical digital twin for motion reconstruction and visualization. The evaluation was conducted in two phases: first, a real-time motion streaming system was established to validate sensor alignment, coordinate frame consistency, and end-to-end latency; second, a sparse inference framework was trained using the Virginia Tech Natural Motion Dataset combined with a custom dataset containing hard negative samples. Experimental results show that the system can accurately reconstruct full-body human motion, excluding head movement, with a local Mean Per-Joint Position Error of 5.96 cm using only five sensors. Comparative ablation studies demonstrate that Transformer-based temporal modeling achieves better geometric accuracy and temporal smoothness than recurrent and convolutional baselines, while physics-informed regularization and hard negative mining significantly improve biomechanical consistency and reduce motion jitter. Real-time experiments further demonstrate that the framework operates within interactive latency limits, highlighting its potential for biomechanical digital twin applications. Full article

Continuous monitoring of the Stand-to-Sit (STS) transition serves as a critical indicator of lower-limb frailty in the elderly, for which millimeter-wave radar provides an ideal privacy-preserving, device-free sensing solution. However, robustly distinguishing between safe Controlled Sits (CSs) and dangerous Uncontrolled Descents (UDs) is severely hindered by the prohibitive cost of subjective expert scoring for fine-grained labels, alongside the pervasive “Clever Hans” effect where existing deep models overfit static environmental clutter rather than learning intrinsic human kinematics. To circumvent these bottlenecks, we formulate STS evaluation as a dynamic boundary detection problem and propose SCA-BiMamba, a linear-complexity bidirectional State-Space Model that utilizes actual fall events as extreme kinematic surrogates for UDs. This forces the network to learn a strict physical boundary between CS and physiological failure without subjective grading. Furthermore, we establish a stringent Real-to-Sim diagnostic audit as a core methodological contribution. By projecting models trained on noisy real-world data onto pure-kinematics simulations—incorporating stochastic temporal phase shifts, kinematic overlaps, and unified physiological tremors—we explicitly quantify feature disentanglement. This protocol serves as a formal ‘probing test’ to expose the ‘Clever Hans’ effect, ensuring the model relies on invariant human physics rather than transient environmental artifacts. Extensive experiments demonstrate that SCA-BiMamba achieves highly robust classification on real-world data (averaging 94.2% Macro F1 with 100.0% Uncontrolled Descent Recall), and achieves a highly robust 99.4% ± 1.1% Macro F1 in the simulated zero-shot transfer. We emphasize that this optimal performance reflects the successful abstraction of extreme kinematic boundaries, rather than a flawless resolution of all clinical complexities. Concurrently, it exhibits strict resistance to shortcut learning and sustains robust real-world scalability using merely 20% of the training data, thereby establishing a promising privacy-preserving boundary-based radar motion classification framework for distinguishing controlled sitting from extreme instability surrogates. Full article

Advanced sensor signal analysis is increasingly important for intelligent health management in human-centered environments, where continuous perception and real-time interpretation of motion-related signals are essential for safe and adaptive assistance. In this study, we propose a vision-based sensor signal analysis framework for automated ergonomic risk assessment of wheelchair users during cabinet interaction. The proposed framework integrates YOLOv11 for human detection, MHFormer for monocular 3D pose reconstruction, and a fuzzy logic-enhanced RULA model for continuous ergonomic risk quantification from video-derived motion signals. To support model development and evaluation, we constructed a dedicated wheelchair cabinet-operation dataset comprising 30 participants, including 14 experienced wheelchair users and 16 trained simulation participants, across five representative cabinet-operation scenarios. The raw dataset contained approximately 5 h of RGB video and about 150,000 original frames. To reduce redundancy caused by highly similar consecutive frames and to mitigate overfitting risk, representative frames were sampled from the continuous video sequences, resulting in 10,000 images for annotation and model development. Based on the proposed framework, raw visual sensor signals are transformed into temporally continuous kinematic representations and ergonomic risk scores, enabling non-contact and real-time health-state interpretation in assistive living environments. The proposed method achieved an average joint-angle estimation RMSE of 7.5°, representing an approximately 60% reduction compared with a Kinect v2-based motion capture baseline (18.6°), which is widely used for low-cost ergonomic evaluation. In benchmark evaluation, the proposed method achieved 84% risk-classification accuracy with a Cohen’s kappa of 0.66, outperforming representative baseline approaches. The results further indicated that low revolving-door and low-drawer operations were associated with higher and more sustained ergonomic risk exposure than sliding-door interaction. These findings demonstrate that vision-based sensor signal analysis can provide an effective solution for intelligent health management, ergonomic monitoring, and perception-driven assessment in accessible and assistive autonomous living systems. Full article

Recent systems for position and movement monitoring are increasingly enhanced by the integration of advanced AI techniques. Besides traditional analytical and algorithmic approaches, which are still applied in areas such as signal processing, sensor fusion, and kinematic modeling, there is a growing body of research that leverages AI-based methods to improve accuracy, robustness, and real-time decision-making capabilities. Artificial neural networks and deep learning methods are more and more often used for tasks such as predicting movement trajectories, detecting position anomalies, and approximating complex motion patterns. The main aim of this work is to provide the main contributions of the recent publications to the current state of the field. Key trends, challenges, and prospects for their future development are also highlighted. Initial statistical analysis was conducted based on responses to queries formulated for searching engines of leading online databases since 2006. Next, the retrieved articles from the last 6 years were subjected to a more detailed analysis. They were divided into thematic areas, including models for human pose estimation; systems for motion detection and tracking, with special attention to human movement; and, eventually, more specialized applications such as action recognition, autonomous driving, motion analysis, and surveillance. The architectures of the created models, the methods for parameter tuning or training, the input datasets used, and the result evaluation metrics were classified. Finally, some more general conclusions were drawn. Full article

Background: Ancient depictions of Pankration techniques have traditionally been interpreted through qualitative comparison with modern combat sports, without systematic biomechanical evaluation. The present study examines whether postural configurations derived from archeological artifacts are geometrically compatible with a continuous sagittal-plane trajectory under constrained inverse kinematics. Methods: A reduced planar humanoid model with three active rotational degrees of freedom was implemented in MATLAB Simulink(2024b), and artifact-derived initial and terminal postures were treated as boundary conditions. An analytical inverse kinematics solution was used to generate a continuous end-effector trajectory, from which joint kinematics and center-of-gravity displacement were computed. Motion capture data from ten participants were used solely to assess whether the generated trajectory is physically executable within human joint limits. Results: The results demonstrated strong agreement in selected local horizontal joint trajectories, while larger discrepancies were observed in vertical motion and global center-of-gravity behavior, reflecting the limitations of the reduced model. Conclusions: The study provides a reproducible framework for evaluating the kinematic feasibility of artifact-derived movements under explicitly defined constraints, limited to the assessment of geometric compatibility and physical executability. Full article

Robotic gait rehabilitation can increase training intensity and repeatability, yet many systems still rely on fixed assistance or pre-programmed trajectories and provide limited support for functional transitions such as sit-to-stand and stand-to-sit. This study presents RoboREHAB as a system-level integration effort that combines variable-gravity assistance through joint-level torque compensation, a redesigned leg assembly intended to improve knee kinematics, and motion-capture-informed reinforcement learning as a preliminary personalization layer for trajectory tracking. The manuscript defines a unified architecture with explicit module interfaces and signal flow and distinguishes simulation-based evaluation, benchtop prototype evidence, and future validation steps to maintain traceability and align claims with the current level of validation. Simulation-based evaluation under the present configuration indicated reductions in gait trajectory error (66%), balance recovery time (55%), stride deviation (72%), and joint torque variability (66%) relative to a fixed-gravity, predefined-trajectory baseline under matched simulated scenario definitions, together with trajectory tracking within an approximately 5% error margin for the redesigned assembly relative to motion-capture references. Benchtop prototype demonstrations support the subsystem-level feasibility of sensor-driven variable-gravity control and user-adjustable assistance scaling using an embedded control stack. Overall, the present evidence supports the feasibility of the integrated RoboREHAB architecture and defines a staged validation pathway toward future hardware-in-the-loop testing, instrumented full-scale evaluation, and eventual human-subject studies. Full article