Search Results (365)

Human pose estimation (HPE) in privacy-sensitive environments such as healthcare facilities and smart homes demands non-visual sensing solutions. Millimeter-wave (mmWave) radar emerges as a promising alternative, yet its development is hindered by the scarcity of high-fidelity datasets with accurate annotations. This paper introduces mmFree-Pose, the first dedicated mmWave radar dataset specifically designed for privacy-preserving HPE. Collected through a novel visual-free framework that synchronizes mmWave radar with VDSuit-Full motion-capture sensors, our dataset covers 10+ actions, from basic gestures to complex falls. Each sample provides (i) raw 3D point clouds with Doppler velocity and intensity, (ii) precise 23-joint skeletal annotations, and (iii) full-body motion sequences in privacy-critical scenarios. Crucially, all data is captured without the use of visual sensors, ensuring fundamental privacy protection by design. Unlike conventional approaches that rely on RGB or depth cameras, our framework eliminates the risk of visual data leakage while maintaining high annotation fidelity. The dataset also incorporates scenarios involving occlusions, different viewing angles, and multiple subject variations to enhance generalization in real-world applications. By providing a high-quality and privacy-compliant dataset, mmFree-Pose bridges the gap between RF sensing and home monitoring applications, where safeguarding personal identity and behavior remains a critical concern. Full article

This work focuses on time and frequency domain analyses of IMU-based orientation estimation algorithms, including indirect Kalman (IKF), Madgwick (MF), and complementary (CF) filters. Euler angles and quaternions are used for orientation representation. A 6-DoF IMU is attached to a 6-joint UR5e robotic arm, with the robot’s orientation serving as the reference. Robotic arm data is obtained via an RTDE interface and IMU data via a CAN bus. Test signals include pose sequences, which are big-amplitude, slowly changing signals used to evaluate stationary and low-dynamics responses in the time domain, and small-amplitude, fast-changing generalized binary noise (GBN) signals used to evaluate dynamic responses in the frequency domain. To prevent poor filters’ performance, their parameters are tuned. In the time domain, RMSE and MaxAE are calculated for roll and pitch. In the frequency domain, composite frequency response and coherence are calculated using the Ockier method. RMSEs are computed for response magnitude and coherence, and averaged equivalent time delay (AETD) is derived from the response phase. In the time domain, MF and CF show the best overall performance. In the frequency domain, they again perform similarly well. IKF consistently performs the worst in both domains but achieves the lowest AETD. Full article

Knee joint stiffness occurs and severely limits its range of motion (ROM) after facture around the knee. During mobility training, knee joints need to be flexed to the maximum angle position (maxAP) that can induce pain at an appropriate level in order to pull apart intra-articular adhesive structures while avoiding secondary injuries. However, the maxAP varies with training and is mostly determined by the pain level of patients. In this study, the feasibility of utilizing electromyogram (EMG) activities to detect maxAP was investigated. Specifically, the maxAP detection was converted into a binary classification between pain level three of the numerical rating scales (pain) and below (painless) according to clinical requirements. Firstly, 12 post-fracture patients with knee joint stiffness participated in Experiment I, with a therapist performing routine mobility training and EMG signals being recorded from knee flexors and extensors. The results showed that the extracted EMG features were significantly different between the pain and painless states. Then, the maxAP estimation performance was tested on a knee rehabilitation robot in Experiment II, with another seven patients being involved. The support vector machine and random forest models were used to classify between pain and painless states and obtained a mean accuracy of 87.90% ± 4.55% and 89.10% ± 4.39%, respectively, leading to an average estimation bias of 6.5° ± 5.1° and 4.5° ± 3.5°. These results indicated that the pain-induced EMG can be used to accurately classify pain states for the maxAP estimation in post-fracture mobility training, which can potentially facilitate the application of robotic techniques in fracture rehabilitation. Full article

Visual body-tracking techniques have seen widespread adoption in applications such as motion analysis, human–machine interaction, tele-robotics and extended reality (XR). These systems typically provide 2D landmark coordinates corresponding to key limb positions. However, to construct a meaningful 3D kinematic model for body joint reconstruction, a mapping must be established between these visual landmarks and the underlying joint parameters of individual body parts. This paper presents a method for constructing a 3D kinematic model of the human hand using calibrated 2D landmark-tracking data augmented with depth information. The proposed approach builds a hierarchical model in which the palm serves as the root coordinate frame, and finger landmarks are used to compute both forward and inverse kinematic solutions. Through step-by-step examples, we demonstrate how measured hand landmark coordinates are used to define the palm reference frame and solve for joint angles for each finger. These solutions are then used in a visualization framework to qualitatively assess the accuracy of the reconstructed hand motion. As a future work, the proposed model offers a foundation for model-based hand kinematic estimation and has utility in scenarios involving occlusion or missing data. In such cases, the hierarchical structure and kinematic solutions can be used as generative priors in an optimization framework to estimate unobserved landmark positions and joint configurations. The novelty of this work lies in its model-based approach using real sensor data, without relying on wearable devices or synthetic assumptions. Although current validation is qualitative, the framework provides a foundation for future robust estimation under occlusion or sensor noise. It may also serve as a generative prior for optimization-based methods and be quantitatively compared with joint measurements from wearable motion-capture systems. Full article

The purpose of this study was to identify differences between patellofemoral joint stress (PFJS), patellofemoral joint reaction force (PFJRF), quadriceps force, trunk and knee flexion angles, and horizontal position of applied load relative to the knee and heel between the front squat (FS) and back squat (BS) exercises at two depths (60 and 80% of leg length, where 60% represents a lower squat depth). Twenty-two healthy college-aged females (age: 22.23 ± 1.86 years, mass: 67.65 ± 9.60 kg, height: 171.34 ± 6.38 cm) participated in this study. Mechanical variables were measured or estimated using a 15-camera 3D motion analysis (180 Hz) system and force platforms (1800 Hz). Five repetitions of each squatting technique at each depth were performed. Multivariate testing showed a difference in patellofemoral loading variables, trunk and knee kinematics, and bar position relative to the heel and knee (p = 0.00) between squat depths. There was no difference between techniques, no interaction between depth and techniques (p > 0.05). Follow-up univariate analyses showed differences in PFJS, PFJRF, quadriceps force, horizontal bar position relative to the heel and knee, and knee and trunk flexion between squat depths. The similar joint stress observed between FS and BS may be explained by compensatory trunk mechanics or the use of a light external load. Full article

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

Background: Gait kinematics address the analysis of joint angles and segment movements during walking. Although there is work in the literature to solve the problems of forward (FK) and inverse kinematics (IK), there are still problems related to the accuracy of the estimation of Cartesian and joint variables, singularities, and modeling complexity on gait analysis approaches. Objective: In this work, we propose a framework for two-dimensional gait analysis addressing the singularities in the estimation of the joint variables using quaternion-based kinematic modeling. Methods: To solve the forward and inverse kinematics problems we use the dual quaternions’ composition and Damped Least Square (DLS) Jacobian method, respectively. We assess the performance of the proposed methods with three gait patterns including normal, toe-walking, and heel-walking using the RMSE value in both Cartesian and joint spaces. Results: The main results demonstrate that the forward and inverse kinematics methods are capable of calculating the posture and the joint angles of the three-DoF kinematic chain representing a lower limb. Conclusions: This framework could be extended for modeling the full or partial human body as a kinematic chain with more degrees of freedom and multiple end-effectors. Finally, these methods are useful for both diagnostic disease and performance evaluation in clinical gait analysis environments. Full article

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of $2.96\pm {0.24}^{\circ }$, Root Mean Square Error (RMSE) of $4.41\pm {0.45}^{\circ }$, Coefficient of Determination ($R^2$) of $0.9924\pm 0.0020$, and Correlation Coefficient (CC) of $0.9963\pm 0.0010$. It achieved a MAE of ${3.30}^{\circ }$, RMSE of ${4.75}^{\circ }$, $R^2$ of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of $15.53\pm {1.80}^{\circ }$, RMSE of $21.72\pm {2.85}^{\circ }$, $R^2$ of $0.8141\pm 0.0540$, and CC of $0.9100\pm 0.0306$ on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

We present mmHSE, a two-stage framework for human skeleton estimation using dual millimeter-Wave (mmWave) Frequency-Modulated Continuous-Wave (FMCW) radar signals. To enable data-driven model design and evaluation, we collect and process over 30,000 range–angle maps from 12 users across three representative indoor environments using a dual-node radar acquisition platform. Leveraging the collected data, we develop a two-stage neural architecture for human skeleton estimation. The first stage employs a dual-branch network with depthwise separable convolutions and self-attention to extract multi-scale spatiotemporal features from dual-view radar inputs. A cross-modal attention fusion module is then used to generate initial estimates of 21 skeletal keypoints. The second stage refines these estimates using a skeletal topology module based on graph convolutional networks, which captures spatial dependencies among joints to enhance localization accuracy. Experiments show that mmHSE achieves a Mean Absolute Error (MAE) of 2.78 cm. In cross-domain evaluations, the MAE remains at 3.14 cm, demonstrating the method’s generalization ability and robustness for non-intrusive human pose estimation from mmWave FMCW radar signals. Full article

This paper investigates the problem of tracking targets with unknown but fixed 3D star-convex shapes using point cloud measurements. While existing methods typically model shape parameters as random variables evolving according to predefined prior models, this evolution process is often unknown in practice. We propose a particular approach within the Expectation Conditional Maximization (ECM) framework that circumvents this limitation by treating shape-defining quantities as parameters estimated directly via optimization. The objective is the joint estimation of target kinematics, extent, and orientation in 3D space. Specifically, the 3D shape is modeled using a radial function estimated via double Fourier series (DFS) expansion, and orientation is represented using the compact, singularity-free axis-angle method. The ECM algorithm facilitates this joint estimation: an Unscented Kalman Smoother infers kinematics in the E-step, while the M-step estimates DFS shape parameters and rotation angles by minimizing regularized cost functions, promoting robustness and smoothness. The effectiveness of the proposed algorithm is substantiated through two experimental evaluations. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

Structure-from-motion (SfM) is a foundational technology that facilitates 3D scene understanding and visual localization. However, bundle adjustment (BA)-based SfM is usually very time-consuming, especially when dealing with numerous unknown focal length cameras. To address these limitations, we proposed a novel SfM system based on pose-only adjustment (PA) for intrinsic and extrinsic joint optimization to accelerate computing. Firstly, we propose a base frame selection method based on depth uncertainty, which integrates the focal length and parallax angle under a multi-camera system to provide more stable depth estimation for subsequent optimization. We explicitly derive a global PA of joint intrinsic and extrinsic parameters to reduce the high dimensionality of the parameter space and deal with cameras with unknown focal lengths, improving the efficiency of optimization. Finally, a novel pose-only re-triangulation (PORT) mechanism is proposed for enhanced reconstruction completeness by recovering failed triangulations from incomplete point tracks. The proposed framework has been demonstrated to be both faster and comparable in accuracy to state-of-the-art SfM systems, as evidenced by public benchmarking and analysis of the visitor photo dataset. Full article

Background/Objectives: Rapidly progressive osteoarthritis of the hip (RPOH) has unique diagnostic and surgical challenges due to rapid joint degeneration and acetabular structural alterations. This study aimed to investigate correlations between preoperative spinopelvic geometry and anterior acetabular wall bone stock deficiency in RPOH patients and introduce an advanced imaging measurement techniques for cases with amputated femoral heads. Methods: A prospective observational study was conducted that enrolled 85 patients, comprising 40 with unilateral RPOH (Zazgyva Grade II or III) and 45 controls with primary osteoarthritis (OA). Preoperative spino-pelvic parameters (pelvic tilt—PT, sacral slope—SS, lumbar lordosis—LL, and T1 pelvic angle) and acetabular anterior wall characteristics (anterior center edge angle—ACEA, anterior wall index—AWI, and anterior acetabular surface area—AASA) were measured using standardized radiographic and CT imaging protocols, including a new methodology for acetabular center estimation in femoral head-amputated cases. Results: Significant differences were identified between RPOH and primary OA patients in the PT (22.5° vs. 18.9°, p = 0.032), SS (37.8° vs. 41.1°, p = 0.041), T1 pelvic angle (14.3° vs. 11.8°, p = 0.018), and anterior center edge angle (25.3° vs. 29.7°, p = 0.035). RPOH patients exhibited pronounced spinopelvic misalignment and anterior acetabular deficiencies. Conclusions: RPOH is associated with spinopelvic misalignment and anterior acetabular wall deficiency. Accurate preoperative diagnosis imaging and personalized surgical approaches specifically addressing acetabular bone stock deficiencies are mandatory in these cases. Full article