Search Results (139)

Musculoskeletal disorders such as rheumatoid arthritis and osteoarthritis affect millions worldwide and are projected to rise sharply by 2050, highlighting the importance of scalable telerehabilitation. This paper introduces Rehabi, a mobile, user-friendly tele-rehabilitation platform that centrally integrates markerless motion for biomechanical assessment and monitoring. Rehabi development followed a user-centered methodology, combining questionnaires, interviews, and natural language processing to elicit requirements from patients and clinicians. The system architecture was implemented in accordance with Clean Architecture principles to ensure modularity and scalability. In a pilot clinical validation of the markerless motion algorithm integrated into Rehabi, 14 post-arthroplasty patients showed moderate agreement for hip flexion (ICC = 0.686) and good agreement for knee flexion (ICC = 0.801). Although the sample was small, the results show a promising trend suggesting that mobile markerless motion capture may be a viable option for remote assessment and monitoring. Full article

Background and aim: Traditional marker-based optical motion capture systems are costly, time-consuming to operate, and constrained by laboratory environments, limiting their broader adoption in clinical practice and naturalistic settings. Markerless motion capture based on a sums-of-Gaussians (SoG) body model is a potential alternative; however, its metrological properties for kinematic assessment during walking and slow running remain insufficiently validated. Using a conventional marker-based Vicon system as the reference, this study evaluated the reliability and concurrent validity of an SoG-based markerless system (MocapGS) for bilateral lower-limb joint range of motion (ROM) during gait. Methods: Thirty-six healthy adults completed self-selected-pace speed walking and slow running tasks while both systems synchronously acquired bilateral lower-limb kinematics. The intraclass correlation coefficient (ICC), standard error of measurement (SEM), SEM percentage (SEM%), minimal detectable change (MDC), MDC percentage (MDC%), and root mean square error (RMSE) were used to assess reliability. Concurrent validity was evaluated using the Pearson correlation coefficient, paired-sample t-tests, and the concordance correlation coefficient (CCC) to compare the ROM. Results: Vicon showed moderate-to-high reliability for ROM in most joints across both tasks. By contrast, the MocapGS achieved acceptable ICC values mainly for the sagittal-plane ROM at the hip and knee. The CCC analysis showed no significant agreement between the two systems. Bland–Altman plots showed systematic biases with spatially heterogeneous random errors. During walking, MocapGS systematically overestimated ROM relative to Vicon at several joint axes; the widest limits of agreement (LOA) occurred at the left knee X-axis and right hip Z-axis. During running, overestimation was consistent across all bilateral joints at the X-axis and the right hip at the Y-axis, while the widest LOA were found at the bilateral hip X-axes. These specific discrepancies highlighted the joint–axis combinations with the greatest measurement variance. In walking, the test–retest reliability of the knee flexion–extension ROM measured by the MocapGS approached that of Vicon; however, the SEM% and MDC% were generally larger for MocapGS than for Vicon. The RMSE exceeded 5 degrees for ROM in most joint planes, especially in the frontal and transverse planes and at distal joints; errors increased further during slow running. Conclusions: MocapGS may be used for coarse monitoring of large-magnitude changes in sagittal-plane kinematics during gait; however, it is currently unlikely to replace Vicon for clinical decision-making or detecting subtle gait changes, and its outputs should be interpreted with caution, particularly for ankle kinematics and non-sagittal-plane motion. Full article

High-fidelity 3D reconstruction of human-sized objects typically requires multi-sensor scanning systems that are expensive, complex, and rely on proprietary hardware configurations. Existing low-cost approaches often rely on handheld scanning, which is inherently unstructured and operator-dependent, leading to inconsistent coverage and variable reconstruction quality. This limitation necessitates the need for a controlled, repeatable, and affordable scanning method that can generate high-quality data without requiring multi-sensor hardware or external tracking markers. This study presents a marker-less scanning platform designed for human-scale reconstruction. The system consists of a single structured-light sensor mounted on a vertical linear actuator, synchronised with a motorised turntable that rotates the subject. This constrained kinematic setup ensures a repeatable cylindrical acquisition trajectory. To address the geometric ambiguity often found in vertical translational symmetry (i.e., where distinct elevation steps appear identical), the system employs a sensor-assisted initialisation strategy, where feedback from the rotary encoder and linear drive serves as constraints for the registration pipeline. The captured frames are reconstructed into a complete model through a two-step Iterative Closest Point (ICP) procedure that eliminates the vertical drift and model collapse (often referred to as “telescoping”) common in unconstrained scanning. To evaluate system performance, a modular anthropometric benchmark object representing a human-sized target (1.6 m) was scanned. The reconstructed model was assessed in terms of surface coverage and volumetric fidelity relative to a CAD reference. The results demonstrate high sampling stability, achieving a mean surface density of $0.760{\mathrm{points}/\mathrm{mm}}^2$ on front-facing surfaces. Geometric deviation analysis revealed a mean signed error of −1.54 mm ($\sigma =$ 2.27 mm), corresponding to a relative volumetric error of approximately 0.096% over the full vertical span. These findings confirm that a single-sensor system, when guided by precise kinematics, can mitigate the non-linear bending and drift artefacts of handheld acquisition, providing an accessible yet rigorously accurate alternative to industrial multi-sensor systems. Full article

Quantitative gait analysis is essential for assessing motor function, as altered walking patterns are linked to functional decline and increased fall risk. Although recent advances in markerless motion analysis and human pose estimation enable gait feature extraction from low-cost video systems compared to expensive motion analysis laboratories, clinical translation remains limited by fragmented descriptors or approaches that directly regress clinical scores, often reducing interpretability and generalizability. We propose the Gait Alteration Index (GAI), an interpretable index that quantifies gait abnormality as a functional deviation from typical walking patterns, independently of specific pathologies. The GAI is computed from a small set of gait parameters and integrates three complementary domains: spatio-temporal characteristics, surrogates of dynamic stability, and arm swing behaviour, providing both a global index and domain-specific sub-indices. Preliminary evaluation on a heterogeneous cohort using clinician-derived assessments showed that the GAI captures clinically meaningful gait alterations (Spearman’s $\rho =0.65$), with the strongest agreement for spatio-temporal features ($\rho =0.77$). These results suggest that the GAI is a promising low-cost, and interpretable tool for objective gait assessment, screening, and longitudinal monitoring. Full article

The landmine press is a reliable and valid test for assessing upper-body push strength. However, its application is constrained by the limitations of current mainstream monitoring technologies, such as linear position transducers (LPTs). These devices require physical attachment to the barbell, they rely on proprietary software, and their measurement accuracy can degrade under high-load conditions due to sensor drift and electromechanical noise. To address these limitations, this study developed a markerless, non-contact, and vision-based system using an enhanced YOLOv8-OBB model and a mathematical modeling framework to measure four kinematic indicators during the concentric phase of the landmine press. By integrating a polarized self-attention mechanism, an improved C3k2 module, and an optimized SPPF structure, the system significantly enhanced detection accuracy and robustness for the small targets at both ends of the barbell, achieving an mAP@0.5 of 0.995 on the test set. A method comparison study was conducted against a widely used LPT device (GymAware) across four loads (20–35 kg) in 247 trials. The results showed strong correlations (r > 0.85) for peak velocity, mean velocity, peak power, and mean power. Although the vision-based method systematically overestimated velocity metrics, the bias was predictable. Notably, under the highest load (35 kg), where LPT limitations are pronounced, the vision system demonstrated comparative stability, suggesting its potential advantage in mitigating sensor-related errors. The findings demonstrate that this vision-based system offers a reliable and practical alternative for monitoring landmine press kinematics, suitable for both training and scientific research. Full article

Axial postural abnormalities in Parkinson’s Disease (PD) are traditionally assessed using clinical rating scales, although picture-based assessment is considered the gold standard. This study evaluates the reliability and clinical relevance of two markerless body-tracking frameworks, the RGB-D-based Microsoft Azure Kinect (providing the reference KIN_3D model) and the RGB-only Google MediaPipe Pose (MP), using a synchronous dual-camera setup. Forty PD patients performed a 60 s static standing task. We compared KIN_3D with three MP models (at different complexity levels) across horizontal, vertical, sagittal, and 3D joint angles. Results show that lower-complexity MP models achieved high congruence with KIN_3D for trunk and shoulder alignment (ρ > 0.75), while the lateral view significantly improved tracking of sagittal angles (ρ ≥ 0.72). Conversely, the high-complexity model introduced significant skeletal distortions. Clinically, several angular parameters emerged as robust metrics for postural assessment and global motor impairments, while sagittal angles correlated with motor complications. Unexpectedly, a more upright frontal alignment was associated with greater freezing of gait severity, suggesting that static postural metrics may serve as proxies for dynamic gait performance. In addition, both RGB-only and RGB-D frameworks effectively discriminated between postural severity clusters. While the higher-complexity MP model should be avoided due to inaccurate 3D reconstructions, our findings demonstrate that low- and medium-complexity MP models represent a reliable alternative to RGB-D sensors for objective postural assessment in PD, facilitating the widespread application of objective posture measurements in clinical contexts. Full article

Robust global point-cloud registration remains a key challenge in robotic neurosurgery, particularly for markerless patient registration, where anatomical surface acquisition can be incomplete and noisy. This paper proposes practical pre-processing steps, defines performance criteria, and evaluates the keypoint-based 4-Points Congruent Set (K4PCS) and Super4PCS algorithms for global registration. Experiments are conducted on surface point clouds segmented from real patient head CT scans, while all measurement errors are synthetically simulated by applying clinically relevant perturbations, including large initial misalignment, Gaussian (CT-like) and non-Gaussian (camera-like) noise injection, and partial scans, across 30 different poses. Registration performance is quantified using pose errors and noise-aware surface-distance/overlap measures, while run-time is assessed under a newly developed selective downsampling strategy and compared to standard voxel downsampling. Results show that both algorithms reliably converge from substantial misalignment and remain robust after noise injection, with computation times ranging from 0.1 s to >15 min. Partial-to-whole registration achieves accuracy comparable to whole-to-whole registration (errors $<{10}^{-3}$ mm), but typically exceeds real-time run-times. Selective downsampling provides a clear improvement in precision and, in most cases, also improves speed compared to the voxel-based downsampling method. Overall, the results indicate that robust and real-time markerless head registration is feasible under clinical conditions. Full article

Stroke frequently results in persistent upper limb impairments, which are often accompanied by compensatory movement strategies that are not fully captured by conventional clinical assessment scales. Quantitative kinematic analyses may provide more objective and sensitive measures of motor dysfunction. In this study, we propose a probabilistic, distribution-based analysis of upper limb kinematics to quantify motor disability in post-stroke patients. We analyzed reaching movement data acquired with a markerless Kinect V2 system from 36 post-stroke patients and age-matched healthy controls. Wrist velocity profiles were characterized using distribution metrics, including variance, skewness, kurtosis, and entropy, and divergence measures (Hellinger distance, Kullback–Leibler divergence, and Jensen–Shannon divergence). Group differences between patients and controls, as well as across impairment levels stratified by the Fugl-Meyer (FM) score, were evaluated. Several distribution metrics significantly discriminated patients from controls and scaled with motor impairment severity. In particular, divergence-based measures showed a strong association with FM scores, indicating increasing deviation from normative movement patterns with greater impairment. These findings demonstrate that distribution-based metrics focusing on kinematic analysis provide a clinically meaningful, objective descriptor of motor dysfunction and complement conventional biomechanical assessments, offering a sensitive framework for quantifying motor disability after stroke. Full article

Markerless motion capture is increasingly used in pediatric neurorehabilitation, yet its ability to detect patient-specific gait abnormalities in small and heterogeneous cohorts remains unclear. This study evaluated a smartphone-based markerless workflow (OpenCap integrated with OpenSim) as a clinical assessment tool to support individualized planning in the context of robot-assisted gait rehabilitation (RAGT) by characterizing individualized gait deviations in four pediatric patients with neurological gait disorders, referenced against normative data from 30 healthy individuals. Sagittal hip, knee, and ankle kinematics were extracted, normalized, and converted into gait-cycle–dependent Z-scores. Group-level comparisons using one-sample Statistical Parametric Mapping (SPM) revealed no significant deviations between patient-group means and normative trajectories (p ≥ 0.05). In contrast, individualized deviation profiling—including Z-score heatmaps, phase-wise Z-score analysis, and per-patient kinematic overlays—identified distinct, clinically meaningful abnormalities in every patient, such as excessive swing-phase hip and knee flexion, mid-stance knee extension deficits, reduced terminal-stance hip extension, and markedly diminished late-stance ankle plantarflexion and push-off. Several deviations exceeded |2–5| SD from the normative dataset, indicating substantial impairments that were obscured by group averaging. These individualized patterns were consistent with each patient’s clinical presentation and could be interpreted in relation to modifiable gait features that are commonly considered during planning and phase-specific adjustment of robot-assisted gait rehabilitation, rather than serving as direct evidence of therapeutic efficacy. Overall, the findings demonstrate that smartphone-based markerless motion capture enables sensitive, individualized gait assessment even when group-level statistics remain nonsignificant, supporting its use as an exploratory, decision-support framework rather than as an outcome measure of RAGT. Full article

Work-related musculoskeletal disorders (MSDs) remain a global occupational health priority, with recognized limitations in current point-in-time assessment methodologies. This research extends prior computer vision ergonomic assessment approaches by implementing biological proprioceptive feedback principles into a continuous, real-time monitoring system. Unlike traditional periodic ergonomic evaluation methods such as “Rapid Upper Limb Assessment” (RULA), our bio-inspired system translates natural proprioceptive mechanisms—which enable continuous postural monitoring through spinal feedback loops operating at 50–150 ms latencies—into automated assessment technology. The system integrates (1) markerless 3D pose estimation via MediaPipe Holistic (33 anatomical landmarks at 30 FPS), (2) depth validation via Orbbec Femto Mega RGB-D camera (640 × 576 resolution, Time-of-Flight sensor), and (3) proprioceptive-inspired alert architecture. Experimental validation with 40 adult participants (age 18–25, n = 26 female, n = 14 male) performing standardized load-lifting tasks (6 kg) demonstrated that 62.5% exhibited critical postural risk (RULA ≥ 5) during dynamic movement versus 7.5% at static rest, with McNemar test $p<0.001$ (Cohen’s $h=1.22$, 95% CI: 0.91–0.97). The system achieved 95% Pearson correlation between risk elevation and alert activation, with response latency of $42.1\pm 8.3$ ms. This work demonstrates technical feasibility for continuous occupational monitoring. However, long-term prospective studies are required to establish whether continuous real-time feedback reduces workplace injury incidence. The biomimetic design framework provides a systematic foundation for translating biological feedback principles into occupational health technology. Full article

Markerless human pose estimation is increasingly used for kinematic assessment, but evidence of its applicability to upper limb movements across different ranges of motion (ROM) remains limited. This study examined the accuracy and reliability of a markerless pose estimation system for shoulder, elbow and wrist flexion–extension analysis under full and partial ROM tasks. Ten healthy participants performed standardized movements which were synchronously recorded, with an optoelectronic motion capture system used as a reference. Joint angles were compared using RMSE, percentage RMSE (%RMSE), accuracy (Acc), intraclass correlation coefficients (ICC), and Pearson correlation of ROM values. The markerless system reproduced the temporal morphology of the movement with high coherence, showing ICC values above 0.91 for the elbow and 0.94 for the shoulder in full ROM trials. Wrist tracking presented the lowest RMSE values and low inter-subject variability. The main critical aspect was a systematic underestimation of maximum flexion, especially at the shoulder, indicating a magnitude bias likely influenced by occlusion and joint geometry rather than by temporal fluctuations. Despite this limitation, the system adapted consistently to different ROM amplitudes, maintaining proportional variations in joint excursion across tasks. Overall, the findings outline the conditions in which markerless pose estimation provides reliable upper limb kinematics and where methodological improvements are still required, particularly in movements involving extreme flexion and occlusion. Full article

Transcranial Magnetic Stimulation (TMS) is a non-invasive technique for neurological research and therapy, but its effectiveness depends on accurate and stable coil placement. Manual localization based on anatomical landmarks is time-consuming and operator-dependent, while state-of-the-art robotic and neuronavigation systems achieve high accuracy using optical tracking with head-mounted markers and infrared cameras, at the cost of increased system complexity and setup burden. This study presents a cost-effective, markerless robotic-assisted TMS system that combines a 3D depth camera and textile capacitive sensors to assist coil localization and contact control. Facial landmarks detected by the depth camera are used to estimate the motor cortex (C3) location without external tracking markers, while a dual textile-sensor suspension provides compliant “soft-landing” behavior, contact confirmation, and coil-tilt estimation. Experimental evaluation with five participants showed reliable C3 targeting with valid motor evoked potentials (MEPs) obtained in most trials after initial calibration, and tilt-verification experiments revealed that peak MEP amplitudes occurred near balanced sensor readings in 12 of 15 trials (80%). The system employs a collaborative robot designed in accordance with international human–robot interaction safety standards, including force-limited actuation and monitored stopping. These results suggest that the proposed approach can improve the accessibility, safety, and consistency of TMS procedures while avoiding the complexity of conventional optical tracking systems. Full article

Background: Sitting and standing with conventional hip–knee–ankle–foot (HKAF) prostheses are demanding tasks for hip disarticulation (HD) amputees due to the passive nature of current prosthetic hip joints that cannot assist with moment generation. This study developed a sitting and standing control strategy for a motorized hip joint and evaluated whether providing active assistance reduces the intact side demand of these activities. Methods: A dedicated control strategy was developed and implemented for a motorized hip prosthesis (Power Hip) compatible with existing prosthetic knees, feet, and sockets. One HD participant was trained to perform sitting and standing tasks using the Power Hip. Its performance was compared with the participant’s prescribed passive HKAF prosthesis through measurements of ground reaction forces (GRFs), joint moments, and activity durations. GRFs were collected using force plates, kinematics were captured via Theia3D markerless motion capture, and joint moments were computed in Visual3D. Results: The Power Hip enabled more symmetric limb loading and faster stand-to-sit transitions (1.22 ± 0.08 s vs. 2.62 ± 0.41 s), while slightly prolonging sit-to-stand (1.69 ± 0.49 s vs. 1.22 ± 0.40 s) compared to the passive HKAF. The participant exhibited reduced intact-side loading impulses during stand-to-sit (4.97 ± 0.78 N∙s/kg vs. 15.06 ± 2.90 N∙s/kg) and decreased reliance on upper-limb support. Hip moment asymmetries between the intact and prosthetic sides were also reduced during both sit-to-stand (−0.18 ± 0.09 N/kg vs. −0.69 ± 0.67 N/kg) and stand-to-sit transitions (0.77 ± 0.20 N/kg vs. 2.03 ± 0.58 N/kg). Conclusions: The prototype and control strategy demonstrated promising improvements in sitting and standing performance compared to conventional passive prostheses, reducing the physical demand on the intact limb and upper body. Full article

Background/Objectives: Marker-based motion capture remains widely used for lower limb kinematics due to its high precision, although its application is often constrained by elevated operational costs and the requirement for controlled laboratory environments. Markerless methods, such as MediaPipe offer a promising alternative for extending biomechanical analyses beyond traditional laboratory settings, but evidence supporting their validity in controlled tasks is still limited. This study aimed to validate a pixel-based markerless pipeline for two-dimensional kinematic analysis of hip and knee motion during squatting. Methods: Ten healthy volunteers performed three squats with a maximum depth of 90°. Kinematic data were collected simultaneously using marker-based and markerless systems. For the marker-based method, hip and knee joint angles were calculated from marker trajectories within a fixed coordinate system. For the markerless approach, a custom pixel-based pipeline was developed in MediaPipe 0.10.26 to compute bidimensional joint angles from screen coordinates. A paired t-test was conducted using Statistical Parametric Mapping, and maximum flexion values were compared between systems with Bland–Altman analysis. Total range of motion was also analyzed. Results: The markerless pipeline provided valid estimates of hip and knee motion, despite a systematic tendency to overestimate joint angles compared to the marker-based system, with a mean bias of −17.49° for the right hip (95% LoA: −51.89° to 16.91°). Conclusions: These findings support the use of markerless tools in clinical contexts where cost and accessibility are priorities, provided that systematic biases are taken into account during interpretation. Overall, despite the systematic differences, the 2D MediaPipe-based markerless system demonstrated sufficient consistency to assist clinical decision-making in settings where traditional motion capture is not available. Full article

Accurate extrinsic calibration between radar and camera sensors is essential for reliable multi-modal perception in robotics and autonomous navigation. Traditional calibration methods often rely on artificial targets such as checkerboards or corner reflectors, which can be impractical in dynamic or large-scale environments. This study presents a fully targetless calibration framework that estimates the rigid spatial transformation between radar and camera coordinate frames by aligning their observed trajectories of a moving object. The proposed method integrates You Only Look Once version 5 (YOLOv5)-based 3D object localization for the camera stream with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) filtering for sparse and noisy radar measurements. A passive temporal synchronization technique, based on Root Mean Square Error (RMSE) minimization, corrects timestamp offsets without requiring hardware triggers. Rigid transformation parameters are computed using Kabsch and Umeyama algorithms, ensuring robust alignment even under millimeter-wave (mmWave) radar sparsity and measurement bias. The framework is experimentally validated in an indoor OptiTrack-equipped laboratory using a Skydio 2 drone as the dynamic target. Results demonstrate sub-degree rotational accuracy and decimeter-level translational error (approximately 0.12–0.27 m depending on the metric), with successful generalization to unseen motion trajectories. The findings highlight the method’s applicability for real-world autonomous systems requiring practical, markerless multi-sensor calibration. Full article