Assessing hip proprioceptive accuracy in passive, active, and weighted flexion: Implications of joint position reproduction for clinical rehabilitation.

Comparative accuracy of registration pins versus titanium screws for registering dynamic guidance dental implant placement on vascularized bone flap reconstruction: A cohort study.

ExacTrac Dynamic (EXTD) is an advanced surface-guided radiotherapy system that integrates thermal imaging with optical surface tracking. However, the effect of surface temperature on EXTD positional uncertainty has not been sufficiently characterized. This study aimed to fundamentally characterize the impact of surface temperature changes on EXTD positional errors along individual axes. Simultaneous monitoring was performed using EXTD and an external thermal camera on geometric and thoracic phantoms during surface cooling. Tracking areas were defined for each phantom. Linear regression was used to assess the relationship between surface temperature and positional errors, including the overall translational error (ΔDTrans) and translational and rotational errors along each axis. ΔDTrans increased as surface temperature decreased, with greater temperature dependence in smaller tracking areas. The maximum regression slopes of ΔDTrans were -0.867mm/°C and -0.984mm/°C in the geometric and thoracic phantoms, respectively. In the geometric phantom, consistent positive correlations were observed in the vertical and pitch directions, suggesting systematic temperature-dependent behavior. In the thoracic phantom, the longitudinal direction showed weak negative correlations, whereas the roll direction showed the greatest temperature dependence, indicating stronger effects of surface geometry. For rotational errors, the standard errors decreased as the tracking area increased. Overall, the influence of surface temperature varied according to axis. No significant differences were found in the temperature changes at which tracking was lost among the tracking areas. Accurate EXTD monitoring requires an understanding of these characteristics and the definition of appropriate tracking areas.

Neck disability in chronic neck pain: Associations with temporomandibular disorders, sleep quality, and cervical sensorimotor function.

To compare the effects of various physical therapy interventions on proprioception in individuals with chronic ankle instability (CAI) and to provide an evidence-based medical basis for choosing the best physical therapy. Six electronic databases were systematically searched from the time the database was created until March 1, 2025. Randomized controlled trials evaluating the efficacy of physical therapy for joint position sense were included. A total of 24 randomized controlled trials, including 949 individuals with CAI, were included. Seven physical therapy methods were used. Network meta-analysis revealed that balance training [standardized mean difference (SMD)=0.66, 95% CI: 0.26-1.05], strength training (SMD=0.97, 95% CI: 0.53-1.41), balance combined with strength training (SMD=0.94, 95% CI: 0.50-1.37), cognitive motor training (SMD=0.77, 95% CI: 0.08-1.45), electroacupuncture (SMD=0.84, 95% CI: 0.03-1.65), and whole-body vibration training (SMD=1.00, 95% CI: 0.45-1.55) significantly reduced ankle joint position error. Although whole-body vibration training had the highest ranking (75.4%), it was not significantly better than other therapies. With the exception of electrophysical agents, all 6 physical therapy treatments significantly improved ankle proprioception and yielded similar results. However, interventions should be chosen carefully, as the certainty of evidence is very low.

This paper develops a guidance control law based on a parametric guiding vector field (GVF) and integrates it with a state-of-the-art acceleration and attitude control architecture for tailsitters. The resulting framework enables a direct comparison between traditional trajectory-tracking guidance and GVF-based path-following guidance using a realistic tailsitter model operating under windy conditions. Through extensive simulations, it is shown that for agile flight scenarios with wind and small initial position error, both guidance strategies achieve comparable tracking performance, indicating that the additional complexity introduced by the GVF formulation is not always justified. However, the GVF-based approach exhibits an advantage when initial deviation from the path is present, yielding smooth and well-behaved convergence toward the desired path. Two additional contributions support this evaluation. First, a modification of the parametric GVF is proposed that guarantees exponential stability of the tracking error dynamics for a single-integrator system. Second, the differential flatness transform of a tailsitter vehicle is extended to account for explicit knowledge of the wind velocity vector.

BackgroundDespite the role attributed to cervical proprioception in balance control, the impact of cervical dysfunction on patients with dizziness remains largely elusive.ObjectiveTo investigate the effects of cervical abnormalities on the quality of life and balance control of patients with unilateral peripheral vestibulopathy.MethodsThis was an observational cross-sectional study. Sixty-one patients with dizziness of peripheral origin were enrolled. They filled out clinical questionnaires, the Dizziness Handicap Inventory (DHI), and the Neck Disability Index (NDI). Twenty-four individuals without vestibular or neck complaints were used as controls. The participants underwent the Cervical Joint Position Error Test (CJPT) and a static posturography.ResultsPatients with dizziness showed worse CJPT results (p = 0.0003). During conditions 1 and 3 of posturography, patients with cervical symptoms performed particularly worse compared to those without them. Also, their mean DHI was higher (p = 0.014). DHI and the CJPT results correlated in the case group (r = 0.32; p = 0.012). In the subgroup with cervical complaints, the NDI and DHI scores showed a moderate correlation (r = 0.540; p = 0.0001).ConclusionCervical proprioception correlated with the DHI scores in patients with dizziness. Patients with vestibular disorders with cervical complaints showed poorer results in balance control. Among them, the scores of self-perceived handicap of cervical complaints and dizziness correlated.

Tooth autotransplantation requires recipient socket preparation that matches donor root morphology while preserving surrounding bone. We developed an autonomous multi-axis robotic system that executes nonlinear, surface-conforming milling trajectories to create a geometry-matched socket and compared it with a static tooth-supported guide in forty 3D-printed mandibular models representing single-rooted and double-rooted anatomies. Recipient sockets were planned by offsetting the donor root surface by 0.5 mm and eliminating insertion axis undercuts. The robot executed the planned milling path with a depth-stop Lindemann bur, whereas the guide workflow used guided pilot drilling followed by freehand refinement. Robot assistance reduced deep positional errors and improved agreement between planned and prepared socket geometry, with the most pronounced benefit in double-rooted models, while overall preparation time was comparable between approaches. These findings support further clinical validation to confirm that autonomous robotic, surface-conforming osteotomy can improve full-depth geometric fidelity and reduce unnecessary bone removal in technique-sensitive autotransplantation procedures.

Determining the structure and following the structural evolution of molecules undergoing chemical reactions is one of the key goals of ultrafast molecular physics and chemistry. Recently, Coulomb explosion imaging has emerged as a promising technique for imaging the evolving structure of individual molecules in the gas phase. However, its practical application to structure determination is hampered by the lack of suitable algorithms for directly retrieving the molecular structure from the measured fragment-ion momentum data. Here, we propose a scheme to solve the underlying inverse problem by employing neural networks to infer the initial atomic positions from the final ion momenta on an event-by-event basis. Using this scheme, we retrieve the structure of several polyhalomethane isomers from simulated Coulomb explosion imaging data with an average per-atom position error of ∼0.1 atomic units, i.e., to within 5% of the typical bond lengths. This development paves the way for an automated structure retrieval from Coulomb explosion data one molecule at a time, making it ideally suitable for analyzing pump-probe experiments where several products are formed that need to be distinguished.

Improving accuracy of pterygoid plate resection using real-time navigated piezoelectric surgery and a three-dimensionally printed guide.

In this paper, a new solution of inverse kinematics was derived and programmed as an algorithm. The algorithm was coded and embedded in a MATLAB simulation program to manipulate a 5-DOF Humanoid Robotic Arm (HRA) and assess its reaching accuracy. The algorithm was designed as follows: joint frames were modelled based on the Denavit-Hartenberg (D-H) concept. Configuration for each joint frame was designed using the proposed D-H parameters. Forward kinematic (FK) equations were derived using the designed frames configuration. The inverse kinematic problem was solved (derived) using an analytical method to find five joint angles for the desired location and orientation. IK equations were derived from the FK transformation matrix for the given location and orientation to revers back the joint angles. The Graphical User Interface (GUI) was designed using MATLAB to simulate the proposed FK/IK algorithm. A group of desired locations was handled using the GUI to show the resultant pose of the HRA. The accuracy of the proposed IK was assessed by means of positional error. The lowest and highest average positional errors achieved were (0.026 and 0.79) cm, respectively.

Conventional musculoskeletal spine models often rely on deterministic kinematic constraints to estimate spinopelvic kinematics and kinetics, which can produce unrealistic intervertebral motions and loading patterns. Incorporating intervertebral stiffness in kinematic evaluations would improve physiological accuracy, particularly during lifting tasks where spinal loading is critical. We developed a stiffness-dependent kinematics estimation workflow that integrates nonlinear intervertebral stiffness within an optimal control formulation, enabling spinal motion to emerge from mechanical behaviour rather than prescribed kinematic constraints in full body musculoskeletal models. Eleven healthy participants performed trunk flexion, lateral bending, and axial rotation tasks, with and without lifting a load. Predicted kinematics and compressive forces were compared against a conventional constraints-driven approach. A single-subject dataset with biplanar radiography provided ground-truth validation of vertebral motions. The stiffness-dependent method generated smoother, more physiologically plausible motion distributions and consistently reduced lumbar compressive loading. Vertebral orientation and position errors were lower than with the constraints-driven approach, and compressive forces aligned with literature ranges. Nonlinear stiffness modelling yields more evenly distributed spinal kinematics and reduced lumbar loading, providing a physiologically grounded framework for spine biomechanics and injury prevention research.

The Canalith Repositioning Maneuver (CRM) is the gold-standard treatment for Benign Paroxysmal Positional Vertigo (BPPV), but its success relies heavily on strict angular precision. Clinical estimation of head angles introduces substantial execution variability, leading to maneuver failure. Extended Reality (XR) and wearable sensors offer a promising solution by providing real-time biomechanical guidance. This scoping review systematically maps the emerging evidence on XR technologies for sensor-guided precision control during CRMs. Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, Cochrane CENTRAL, and IEEE Xplore for studies published between January 2010 and December 2025. Eligible studies evaluated Head-Mounted Displays (HMDs) or comparable wearable devices integrated with motion-tracking technologies for CRM execution. Data were synthesized using a convergent narrative approach. Six studies met the inclusion criteria, comprising clinical feasibility trials (n = 3) and computational simulation studies (n = 3). Hardware predominantly included smartphone-based Virtual Reality (VR) and custom head-mounted Inertial Measurement Units (IMUs) utilizing visual or auditory feedback. Clinical evidence demonstrated that sensor-guided feedback significantly minimized angular positional errors compared to standard unguided maneuvers. Concurrently, advanced 3D simulations successfully mapped inner ear biomechanics and validated optimized maneuver pathways. XR and motion-tracking technologies effectively standardize CRM angular precision. By reducing execution variability, these systems can ensure consistent, expert-level care among clinicians and ultimately democratize execution to enable safe, highly effective self-treatment for patients at home. Future research must prioritize large-scale comparative effectiveness trials and evaluate Mixed Reality (MR) systems to mitigate visual occlusion and fall risks.

To develop a deep learning model capable of automatically detecting common positioning errors in panoramic radiographs and to establish an error-based objective ten-point image quality scoring system aligned with expert evaluation. A total of 4,390 panoramic radiographs were retrospectively annotated for seven clinically relevant positioning errors (including chin position, head rotation/tilt, bite position, tongue position, cervical spine positioning, and anatomical coverage) associated with diagnostically meaningful geometric distortion or anatomical superimposition by two experienced oral radiologists. A multi-label deep learning model based on a modified residual network was trained to detect all errors simultaneously. An independent dataset of 510 radiographs was scored for subjective image quality on a ten-point scale. Multiple linear regression was used to estimate the independent contribution of each positioning error to subjective image quality and to derive an objective scoring system established by converting regression-derived weights of significant errors into standardized point deductions. High annotation and scoring reliability were achieved (Kappa: 0.879-0.953; ICC: 0.838). The model achieved high performance across all error types, with accuracy ranging from 88.40% to 96.00% and area under the curve values between 98.17 and 99.37. Several positioning errors were associated with lower subjective image quality scores (p < 0.001). The resulting error-weighted objective score showed moderate correlation with expert ratings (r = 0.673, p < 0.001). The proposed system enables accurate detection of positioning errors and provides a structured and interpretable, error-based surrogate measure of perceived panoramic image quality. This study presents an error-specific, data-driven framework for objective assessment of panoramic image acquisition quality, enhancing the interpretability and standardization of image quality evaluation.

PhenoRob-P: An autonomous robotic system for high-throughput phenotyping of potted plants

A study on the effects of intrafractional changes in bladder volume on positional accuracy and target dosimetry in online adaptive radiotherapy for cervical cancer.

Chronic neck pain is increasingly common among digital device users due to prolonged static postures. Evidence is limited regarding interventions targeting both neuromuscular control and proprioception. Cervical Proprioceptive Neuromuscular Facilitation (PNF) with Hold-Relax is hypothesized to enhance neuromuscular control and proprioception, improving muscle relaxation and reducing joint position errors (JPE) through sensory-motor coordination. This study evaluated the effectiveness of six weeks of dynamic cervical PNF training on Neck Disability Index (NDI) in men with VDT syndrome. In this prospective, randomized controlled trial, 30 participants aged 18-25years with VDT syndrome were randomly assigned to an experimental group (n = 15) or a control group (n = 15). The primary outcome, functional disability, was measured using the NDI. Secondary outcomes included cervical Range of Motion (ROM), Flexion-Relaxation Ratio (FRR), proprioception assessed via the JPE test, and pain measured using the Visual Analog Scale (VAS). Assessments were conducted before and after the six-week intervention using MyoMotion sensors, electromyography (EMG), and a laser-equipped helmet. Data were analyzed using the Kolmogorov-Smirnov test and independent t-tests. Baseline demographics and outcomes did not differ significantly between groups. After six weeks, the experimental group demonstrated significant improvements in NDI (P < 0.05), ROM (P < 0.05), FRR (P < 0.05), and JPE (P < 0.05), along with a significant reduction in pain (P < 0.05) compared with controls. Six weeks of dynamic cervical PNF training significantly improved NDI, cervical mobility, neuromuscular control, proprioception, and reduced pain in young adults with VDT-related neck disorders. These findings provide novel evidence supporting the use of integrated PNF and proprioceptive exercises as a safe and non-invasive rehabilitation strategy, as well as a foundation for personalized therapeutic programs for digital device users.Trial registration: This study was retrospectively registered at https://irct.behdasht.gov.ir/ with the registration number IRCT20240521061862N2 on 2024/09/26.

This work focuses on control of a rigid satellite in the presence of turbulence and uncertainties. It applies actuators with first-order wheel dynamics. The fractional- and integer-order controllers have been tested under identical conditions to reach a proper comparison of the results. The satellite moment of inertia, actuator perfect, and the external disturbances are all considered as uncertain. Numerical analysis is performed using Euler's method, while FO integrals and derivatives are obtained by the Grünwald-Letnikov definition. The PSO method is employed to optimise the coefficients of both controllers during the process. The goal is to minimise the absolute average of the position error in the maneuvers. Performance criteria are analyzed and evaluated, including overshoot and settling times, while uncertainty is present. The findings demonstrate that the controllers with additional degrees of freedom, therefore, provide a much better pointing accuracy (especially in the case of uncertainty) than the IO ones.

Comment on "Symptom and Handicap Severity in Subacute Dizzy Patients: The Role of Gaze Position Error in Video Head Impulses".

HighlightsWhat are the main findings?Optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI) under static conditions with 485 ms settling time under rapid light transitions; peripheral subsystem draw is confirmed at 1.19 W ± 0.02 W (n = 60, 95% CI), validating the ≤1.2 W target.The dual-modality virtual pheromone system (WS2812B optical + MQ-135 ethanol chemical fallback) delivers robust stigmergic swarm coordination under 50/60 Hz flicker, rapid light transitions, and reflective glare.What are the implications of the main findings?Quantisation-aware MobileNetV3-Small achieves 3.2× inference speedup at 15 FPS within 1.8 W; OPTICS behavioural clustering provides automatic scout/worker role differentiation without hard-coded thresholds, both executing fully onboard the NVIDIA Jetson Orin Nano.FormicaBot is the first physical swarm platform to simultaneously demonstrate dual-modality pheromone stigmergy, onboard edge-AI inference, and GPS-denied localization within a ≤1.2 W peripheral power envelope, enabling six-hour autonomous missions on affordable ground robots.Experimental validation delivers five quantified outcomes. First, optical pheromone detection achieves 88.7% ± 0.6% accuracy (n = 150, 95% CI), and the dual-modality combined channel achieves 86.1% ± 0.9% (n = 200), with robustness confirmed under 50/60 Hz flicker interference, rapid 200–1200 lux light transitions (485 ms settling), and reflective glare spots. Second, the MQ-135 chemical channel calibration holds R2 ≥ 0.999 across temperatures of 15–35 °C and humidity of 30–90%, with maximum voltage drift of 0.093 V at the highest temperature. Third, 3.2× CNN inference speedup through 8-bit quantisation runs at 15 FPS within 1.8 W. Fourth, peripheral subsystems draw a measured mean of 1.19 W ± 0.02 W (n = 60, 95% CI); the complete per-robot system, including the Jetson Orin Nano compute rail, draws 6.15 W ± 0.09 W, enabling six-hour missions from the 55.08 Wh battery. Fifth, localisation across ten trials yields the mean position error 0.074 m and RMSE 0.081 m with 97.5% map coverage; physical multi-robot tests with 5–8 robots confirm map convergence times of 120–210 steps with collision rates below 0.042 per robot per step. To the best of our knowledge, no prior physical swarm platform has simultaneously demonstrated this combination of capabilities under comparable constraints.