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Background: A mid-fidelity simulation mannequin, equipped with an instrumented cervical and lumbar spine, was developed to investigate best practices and train healthcare professionals in applying spinal motion restrictions (SMRs) during the early mobilization and transfer of accident victims with suspected spine injury. The study objectives are to (1) examine accuracy of the cervical and lumbar motions measured with the mannequin; and (2) confirm that the speed of motion has no bearing on this accuracy. Methods: Accuracy was evaluated by concurrently comparing the orientation data obtained with the mannequin with that from an optoelectronic system. The mannequin’s head and pelvis were moved in all anatomical planes of motion at different speeds. Results: Accuracy, assessed by root-mean-square error, varied between 0.7° and 1.5° in all anatomical planes of motion. Bland–Altman analysis revealed a bias ranging from −0.7° to 0.6°, with the absolute limit of agreement remaining below 3.5°. The minimal detectable change varied between 1.3° and 2.6°. Motion speed demonstrated no impact on accuracy. Conclusions: The results of this validation study confirm the mannequin’s potential to provide accurate measurements of cervical and lumbar motion during simulation scenarios for training and research on the application of SMR.

In baseball, the swing speed and swing angle of the bat just before ball impact are important to increase the speed and horizontal distance of a batted ball. This study investigated the accuracies and error trends of four commercially available bat sensors to measure these parameters. The hitting motions of seven healthy participants were measured simultaneously using the bat sensors and an optical motion capture system, and the swing speeds and swing angles were compared. The swing speed was measured with high accuracy, as indicated by the high intraclass correlation coefficient (ICC) between the bat sensor and the motion capture system measurements (mean ICC = 0.78). However, the ICC for the swing angle was lower (mean ICC = 0.58) than that of the swing speed for all but one bat sensor, indicating low accuracy. Moreover, in the high swing speed range, the accuracy of the swing speed tended to decrease for three bat sensors, but the trend of the swing angle was different among bat sensors. Significant systematic biases or proportional errors were found for all bat sensors, indicating the possibility of error correction. The sensor used in this study can help to evaluate the differences between players with different competition levels and hitting motions. Coaches need to be cautious in taking measurements of players with high swing speeds and in assessing slight changes within an individual.

Motion sickness is characterized by nausea and vomiting among a constellation of symptoms. Symptom severity is dynamic and distressing. Most validated motion sickness scales are time-intensive and effortful, with alternative scales having uncertain performance or non-specific measures. A validated instrument allowing for facile, rapid assessment of core motion sickness symptom severity would therefore be valuable. We assessed the performance of the Motion Sickness Severity Scale (MSSS), a six-item questionnaire designed to measure real-time motion sickness symptoms. MSSS construct validity was assessed as a secondary analysis of data from 63 healthy participants without antiemetic treatment in a clinical trial (Unique Identifier = NCT03772340) conducted to evaluate the safety and efficacy of Tradipitant-a novel neurokinin-1 receptor antagonist-in the treatment of motion sickness. Clinical outcome assessments included the MSSS, the Patient Global Impression of Severity (PGI-S), and the Motion Sickness Assessment Questionnaire (MSAQ). The performance of the MSSS through Pearson correlation coefficients, within-group analysis of variance, empirical cumulative distribution functions, and Kolmogorov-Smirnov tests. The MSSS correlated very highly with the PGI-S (r = 0.93, p-value<0.0001) and highly with the MSAQ (r = 0.83, p-value<0.0001). Mean MSSS scores between increasing PGI-S severity levels increased significantly in all four increments (None-to-Mild: p-value = 0.006, Mild-to-Moderate: p-value<0.0001, Moderate-to-Severe: p-value = 0.006, Severe-to-Very-Severe: p-value = 0.002). There were statistically significant differences in MSSS score distributions stratified by PGI-S severity level, with higher MSSS scores associated with higher PGI-S severity levels and lower MSSS scores associated with lower PGI-S severity levels. The MSSS is a valid instrument for the assessment of the core motion sickness symptoms and is reflective of global disease severity. Implementation of the MSSS and comparable simplified, short questionnaires in motion sickness research will provide rapid and accurate measures of disease severity. These measures will enable further elucidation of motion sickness as an illness and inform the development and evaluation of motion sickness therapies.

Effective upper-extremity rehabilitation requires intensive and precise movement training, yet conventional therapies lack accurate motion tracking. Robotic exoskeletons address this limitation but are often hindered by ergonomic misalignment and limited adaptability. The AssistOn-Arm, a novel self-aligning exoskeleton, integrates ergonomic design and back-drivable actuation to enhance comfort and facilitate natural user interaction. This study aimed to assess the usability and ergonomics of the device in healthy participants and to conduct a pilot clinical evaluation in individuals with upper-extremity impairments. Thirty healthy participants and twelve patients with shoulder impairments performed predefined tasks under participant-active and device-active conditions. Kinematic data captured concurrently with AssistOn-Arm and Xsens MVN demonstrated strong agreement between conditions. Quantitative analysis revealed no significant differences (p > 0.05) in flexion, elevation, abduction–adduction, and external rotation, indicating reliable alignment with natural joint axes. Significant differences (p < 0.05) were observed only in sagittal hyperextension and internal rotation, reflecting device mechanical constraints. The study confirms the clinical feasibility of AssistOn-Arm as a sensor-driven, self-aligning exoskeleton that bridges engineering innovation and precision rehabilitation, paving the way for its integration into clinical practice.

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.

The industrial societies face difficulty applying traditional work-related musculoskeletal disorder (WMSD) risk assessment methods in practical applications due to in-situ task dynamics, complex data processing, and the need of ergonomics professionals. This study aims to develop and validate a wearable inertial sensors-based automated system for assessing WMSD risks in the workspace conveniently, in order to enhance workspace safety and improve workers’ health. Both postural ergonomic analysis (RULA/REBA) and two-dimensional static biomechanical analysis were automatized as two toolboxes in the proposed system to provide comprehensive WMSD risk assessment based on the kinematic data acquired from wearable inertial sensors. The effectiveness of the developed system was validated through a follow-up experiment among 20 young subjects when performing representative tasks in the heavy industry. The RULA/REBA scores derived from our system achieved high consistency with experts’ ratings (intraclass correlation coefficient ≥0.83, classification accuracy >88%), and good agreement was also found between low-back compression force from the developed system and the reference system (mean intersystem coefficient of multiple correlation >0.89 and relative error <9.5%). These findings suggested that the wearable inertial sensors-based automated system could be effectively used for WMSD risk assessment of workers when performing tasks in the workspace.

Hand-intensive work is strongly associated with work-related musculoskeletal disorders (WMSDs) of the hand/wrist and other upper body regions across diverse occupations, including office work, manufacturing, services, and healthcare. Addressing the prevalence of WMSDs requires reliable and practical exposure measurements. Traditional methods like electrogoniometry and optical motion capture, while reliable, are expensive and impractical for field use. In contrast, small inertial measurement units (IMUs) may provide a cost-effective, time-efficient, and user-friendly alternative for measuring hand/wrist posture during real work. This study compared six orientation algorithms for estimating wrist angles with an electrogoniometer, the current gold standard in field settings. Six participants performed five simulated hand-intensive work tasks (involving considerable wrist velocity and/or hand force) and one standardised hand movement. Three multiplicative Kalman filter algorithms with different smoothers and constraints showed the highest agreement with the goniometer. These algorithms exhibited median correlation coefficients of 0.75–0.78 for flexion/extension and 0.64 for radial/ulnar deviation across the six subjects and five tasks. They also ranked in the top three for the lowest mean absolute differences from the goniometer at the 10th, 50th, and 90th percentiles of wrist flexion/extension (9.3°, 2.9°, and 7.4°, respectively). Although the results of this study are not fully acceptable for practical field use, especially for some work tasks, they indicate that IMU-based wrist angle estimation may be useful in occupational risk assessments after further improvements.

Highly detailed topographic surveying at minimal cost and effort has always been one of the developing areas of scientific interest. Image-based remote sensing solutions using unmanned aerial systems (UAS) and structure from motion (SfM) with multi-view stereo (MVS) photogrammetry are the latest automation and advancement in surveying engineering that provides high-resolution topographic data. Although recent developments have led to the extensive use of UAS–SfM in mapping applications, the only concern that remains is the UAS-based survey accuracy; is this method accurate enough to be used in surveying and mapping applications as an alternative to conventional methods? Evaluation of accuracy and validation of products before they can be applied to a real-world problem is a prerequisite for any emerging technology. Recently, there has been a proliferation of accuracy assessment and validation studies of UAS–SfM-based surveying. However, quantitative validation studies are slightly different, and the accuracy of each study is significantly different from another. The true limits of this technique can only be revealed by assembling a large dataset from previous individual studies. This study was motivated by the lack of such quantitative analysis. This study gives an overview of UAS and SfM, discusses the major factors that influence the accuracy, and presents a synthesis of the recent validation studies conducted quantitative assessments of UAS–SfM-derived digital elevation datasets, and thereby demonstrates the accuracy and limitation of UAS–SfM -based topographic surveying.

Correct use of multi-temporal Interferometric Synthetic Aperture Radar (InSAR) datasets to complement geodetic surveying for geo-hazard applications requires rigorous assessment of their precision and accuracy. Published inter-comparisons are mostly limited to ground displacement estimates obtained from different algorithms belonging to the same family of InSAR approaches, either Persistent Scatterer Interferometry (PSI) or Small BAseline Subset (SBAS); and accuracy assessments are mainly focused on vertical displacements or based on few Global Navigation Satellite System (GNSS) or geodetic leveling points. To fill this demonstration gap, two years of Sentinel-1 SAR ascending and descending mode data are processed with both PSI and SBAS consolidated algorithms to extract vertical and horizontal displacement velocity datasets, whose accuracy is then assessed against a wealth of contextual geodetic data. These include permanent GNSS records, static GNSS benchmark repositioning, and geodetic leveling monitoring data that the National Institute of Statistics, Geography, and Informatics (INEGI) of Mexico collected in 2014−2016 in the Aguascalientes Valley, where structurally-controlled land subsidence exhibits fast vertical rates (up to −150 mm/year) and a non-negligible east-west component (up to ±30 mm/year). Despite the temporal constraint of the data selected, the PSI-SBAS inter-comparison reveals standard deviation of 6 mm/year and 4 mm/year for the vertical and east-west rate differences, respectively, thus reassuring about the similarity between the two types of InSAR outputs. Accuracy assessment shows that the standard deviations in vertical velocity differences are 9−10 mm/year against GNSS benchmarks, and 8 mm/year against leveling data. Relative errors are below 20% for any locations subsiding faster than −15 mm/year. Differences in east-west velocity estimates against GNSS are on average −0.1 mm/year for PSI and +0.2 mm/year for SBAS, with standard deviations of 8 mm/year. When discrepancies are found between InSAR and geodetic data, these mostly occur at benchmarks located in proximity to the main normal faults, thus falling within the same SBAS ground pixel or closer to the same PSI target, regardless of whether they are in the footwall or hanging wall of the fault. Establishing new benchmarks at higher distances from the fault traces or exploiting higher resolution SAR scenes and/or InSAR datasets may improve the detection of the benchmarks and thus consolidate the statistics of the InSAR accuracy assessments.

3D motion analysis (3DMA) can help identify patients at increased risk of ACL re-injury, but traditional marker-based systems have limited clinical accessibility. OpenCap, a novel, low-cost, markerless system, aims to enhance accessibility to 3DMA. This study evaluated the concurrent validity of a modified OpenCap version using a 2-DOF knee model for kinematics, while kinetics and ground reaction forces were derived using the native 1-DOF model, compared to a marker-based system. Twenty-four healthy participants performed 240 drop jumps, with data simultaneously captured by both systems. Root mean square error (RMSE), mean absolute error (MAE), maximum error, Pearson correlation, Bland-Altman plots, and statistical parametric mapping (SPM) were used to analyze inter-system differences. RMSE exceeded 6° for frontal-plane knee kinematics with strong waveform correlations ( r  > 0.90). Transverse-plane hip moments showed normalized MAE < 1% with weak to strong negative correlations. Sagittal-plane knee moments had normalized MAE of 5.6% and strong correlations ( r  > 0.90). Vertical GRFs showed normalized MAE > 6% and strong correlations ( r  > 0.90). SPM identified significant differences across most ground contact phases, and Bland-Altman analyses showed wide agreement limits for knee moment asymmetry at initial contact. OpenCap currently cannot be recommended for ACL re-injury risk assessment but demonstrated potential for increasing 3DMA accessibility.