Measurement Of Movement Research Articles

Correlation Analysis of Vertical Ground Movement and Climate Using Sentinel-1 InSAR

Seasonal vertical ground movement (SVGM), which refers to the periodic vertical displacement of the Earth’s surface, has significant implications for infrastructure stability, agricultural productivity, and environmental sustainability. Understanding how SVGM correlates with climatic conditions—such as temperatures and drought—is essential in managing risks posed by land subsidence or uplift, particularly in regions prone to extreme weather events and climate variability. The correlation of periodic SVGM with climatic data from Earth observation was investigated in this work. The European Ground Motion Service (EGMS) vertical ground movement measurements, provided from 2018 to 2022, were compared with temperature and precipitation data from MODIS and CHIRP datasets, respectively. Measurement points (MP) from the EGMS over Italy provided a value for ground vertical movement approximately every 6 days. The precipitation and temperature datasets were processed to provide drought code (DC) maps calculated ad hoc for this study at a 1 km spatial resolution and daily temporal resolution. Seasonal patterns were analyzed to assess correlations with Spearman’s rank correlation coefficient (ρ) between this measure and the DCs from the Copernicus Emergency Management Service (DCCEMS), from MODIS + CHIRP (DC1km) and from the temperature. The results over the considered area (Italy) showed that 0.46% of all MPs (32,826 MPs out of 7,193,676 MPs) had a ρ greater than 0.7; 12,142 of these had a positive correlation, and 20,684 had a negative correlation. DC1km was the climatic factor that provided the highest number of correlated MPs, roughly giving +59% more correlated MPs than DCCEMS and +300% than the temperature data. If a ρ greater than 0.8 was considered, the number of MPs dropped by a factor of 10: from 12,142 to 1275 for positive correlations and from 20,684 to 2594 for negative correlations between the DC1km values and SVGM measurements. Correlations that lagged in time resulted in most of the correlated MPs being within a window of ±6 days (a single satellite overpass time). Because the DC and temperature are strongly co-linear, further analysis to assess which was superior in explaining the seasonality of the MPs was carried out, resulting in DC1km significantly explaining more variance in the SVGM than the temperature for the inversely correlated points rather than the directly correlated points. The spatial distribution of the correlated MPs showed that they were unevenly distributed in clusters across the Italian territory. This work will lead to further investigation both at a local scale and at a pan-European scale. An interactive WebGIS application that is open to the public is available for data consultation. This article is a revised and expanded version of a paper entitled “Detection and correlation analysis of seasonal vertical ground movement measured from SAR and drought condition” which was accepted and presented at the ISPRS Mid-Term Symposium, Belem, Brasil, 8–12 November 2024. Data are shared in a public repository for the replication of the method.

Hand Gesture Recognition Using Ultrasonic Array with Machine Learning

In the field of gesture recognition technology, accurately detecting human gestures is crucial. In this research, ultrasonic transducers were utilized for gesture recognition. Due to the wide beamwidth of ultrasonic transducers, it is difficult to effectively distinguish between multiple objects within a single beam. However, they are effective at accurately identifying individual objects. To leverage this characteristic of the ultrasonic transducer as an advantage, this research involved constructing an ultrasonic array. This array was created by arranging eight transmitting transducers in a circular formation and placing a single receiving transducer at the center. Through this, a wide beam area was formed extensively, enabling the measurement of unrestricted movement of a single hand in the X, Y, and Z axes. Hand gesture data were collected at distances of 10 cm, 30 cm, 50 cm, 70 cm, and 90 cm from the array. The collected data were trained and tested using a customized Convolutional Neural Network (CNN) model, demonstrating high accuracy on raw data, which is most suitable for immediate interaction with computers. The proposed system achieved over 98% accuracy.

Developing biomimetic PVA/PAA hydrogels with cellulose nanocrystals inspired by tree frog structures for superior wearable sensor functionality

This study developed a wearable sensor device featuring biomimetic structures inspired by tree frog toe pads to measure shoulder joint movements effectively. A PVA/PAA double-network hydrogel was formulated by mixing PVA and PAA in a 3:7 ratio, resulting in a Young's modulus of 7.2 kPa, closely matching human skin's mechanical properties. To enhance moisture retention, the hydrogel was supplemented with 20 mL glycerin, increasing its weight retention rate to 95 % after 28 days, a 5.6-fold improvement over glycerin-free hydrogels. Additionally, the incorporation of 0.2 wt% cellulose nanocrystals (CNC) increased the cross-linking density, reducing the swelling ratio from 124 % to 106 % and minimizing the impact of swelling cycles on mechanical properties from 22 % to 4 %. Treefrog-inspired microstructures were fabricated on the sensor surface using laser cutting and casting techniques, significantly enhancing adhesion under humid conditions, with normal, lateral, and peel-off adhesion forces improving by 500, 700, and 700 %, respectively. The sensor demonstrated accurate measurement of shoulder joint movements with an error margin of 6.3 % for polar angles (45°-135°) and 7.8 % for azimuth angles (0°-45°). The accompanying smartphone application provided real-time feedback, helping to prevent exercise injuries and improve user awareness and control of shoulder movements, making it suitable for both rehabilitation and athletic training.

Clinical Trial Design Considerations for Hospitalised Patients With Ulcerative Colitis Flares and Application to Study Hyperbaric Oxygen Therapy in the NIDDK HBOT-UC Consortium.

Patients with ulcerative colitis (UC) who are hospitalised for acute severe flares represent a high-risk orphan population. To provide guidance for clinical trial design methodology in these patients. We created a multi-centre consortium to design and conduct a clinical trial for a novel therapeutic intervention (hyperbaric oxygen therapy) in patients with UC hospitalised for moderate-severe flares. During planning, we identified and addressed specific gaps for inclusion/exclusion criteria; disease activity measures; pragmatic trial design considerations within care pathways for hospitalised patients; standardisation of care delivery; primary and secondary outcomes; and sample size and statistical analysis approaches. The Truelove-Witt criteria should not be used in isolation. Endoscopy is critical for defining eligible populations. Patient-reported outcomes should include rectal bleeding and stool frequency, with secondary measurement of urgency and nocturnal bowel movements. Trial design needs to be tailored to care pathways, with early intervention focused on replacing and/or optimising responsiveness to steroids and later interventions focused on testing novel rescue agents or strategies. The PRECIS-2 framework offers a means of tailoring to local populations. We provide standardisation of baseline testing, venous thromboprophylaxis, steroid dosing, discharge criteria and post-discharge follow-up to avoid confounding by usual care variability. Statistical considerations are provided given the small clinical trial nature of this population. We provide an outline for framework decisions made for the hyperbaric oxygen trial in patients hospitalised for UC flares. Future research should focus on the remaining gaps identified.

Diagnosis of schizophrenia by integrated saccade scores and associations with psychiatric symptoms, and functioning.

Eye movement as a neurobiological biomarker of schizophrenia. We aim to estimate diagnostic accuracy of integrated pro/antisaccade eye movement measurements to discriminate between healthy individuals and schizophrenic patients. We compared the eye movement performance of 85 healthy individuals and 116 schizophrenia-stable patients during prosaccade and antisaccade tasks. The difference eye movement measurements were accumulated by stepwise discriminant analysis to produce an integrated score. Finally, the diagnostic value of the integrated score was calculated by the receiver operating characteristic (ROC) area under the curve (AUC), and the best sensitivity and specificity were calculated based on the given cutoff values. Using discriminant analysis, an integrated score included the residual gain and latency (step) during the prosaccade test, the error rate, and the corrected error rate during the antisaccade test. We found that the integrated score could well classify schizophrenia patients and healthy individuals with an accuracy of 80.6%. In the ROC, Youden's index was 0.634 (sensitivity = 81.0%, specificity = 82.4%) and AUC was 0.871. There were significant difference patterns of correlation between the severity of psychiatric symptoms and daily functioning and diagnostic eye movement measurements. Using only 2 saccade tasks to discriminate well between schizophrenia patients and healthy controls, suggesting that abnormalities in saccade behavior is a potential biomarker and efficient diagnostic tool for identifying schizophrenia. The underlying neuropathologic mechanisms associated with abnormal saccades may provide insights into the intervention and diagnosis of schizophrenia.

Movement Outcomes Acquired via Markerless Motion Capture Systems Compared with Marker-Based Systems for Adult Patient Populations: A Scoping Review

Mobile motion capture is a promising technology for assessing physical movement; markerless motion capture systems (MLSs) offer great potential in rehabilitation settings, given their accessibility compared to marker-based motion capture systems (MBSs). This review explores the current literature on rehabilitation, for direct comparison of movement-related outcomes captured by MLSs to MBSs and for application of MLSs in movement measurements. Following a scoping review methodology, nine databases were searched (May to August 2023). Eligible articles had to present at least one estimate of the mean difference between a measure of a physical movement assessed by MLS and by MBS. Sixteen studies met the selection criteria and were included. For comparison of MLSs with MBSs, measures of mean joint range of motion (ROM) displacement were found to be similar, while peak joint angle outcomes were significantly different. Upper body movement outcomes were found to be comparable, while lower body movement outcomes were very different. Overall, nearly two-thirds of measurements identified statistical differences between MLS and MBS outcomes. Regarding application, no studies assessed the technology with patient populations. Further MLS-specific research with consideration of patient populations (e.g., intentional error testing, testing in less-than-ideal settings) would be beneficial for utilization of motion capture in rehabilitation contexts.

Usefulness of driver’s eye movement measurement to detect potential risks under combined conditions of taking second-generation antihistamines and calling tasks

BackgroundConcerns persist regarding the potential reduction in driving performance due to taking second-generation antihistamines or performing hands-free calling. Previous studies have indicated a potential risk to driving performance under an emergency event when these two factors are combined, whereas a non-emergency event was operated effectively. Currently, there is a lack of a discriminative index capable of detecting the potential risks of driving performance impairment. This study aims to investigate the relationship between driving performance and eye movements under combined conditions of taking second-generation antihistamines and a calling task, and to assess the usefulness of eye movement measurements as a discriminative index for detecting potential risks of driving performance impairment.MethodsParticipants engaged in a simulated driving task, which included a calling task, both under taking or not taking second-generation antihistamines. Driving performance and eye movements were monitored during both emergency and non-emergency events, assessing their correlation between driving performance and eye movements. The study further evaluated the usefulness of eye movement as a discriminative index for potential driving impairment risk through receiver operating characteristic (ROC) analysis.ResultsIn the case of a non-emergency event, no correlation was observed between driving performance and eye movement under the combined conditions. Conversely, a correlation was observed during an emergency event. The ROC analysis, conducted to assess the discriminative index capability of eye movements in detecting the potential risk of driving performance impairment, demonstrated a high discriminative power, with an area under the curve of 0.833.ConclusionsThe findings of this study show the correlation between driving performance and eye movements under the concurrent influence of second-generation antihistamines and a calling task, suggesting the usefulness of eye movement measurement as a discriminant index for detecting potential risks of driving performance impairment.

Fully automated method for three-dimensional segmentation and fine classification of mixed dentition in cone-beam computed tomography using deep learning

ObjectiveTo establish a high-precision, automated model using deep learning for the fine classification and three-dimensional (3D) segmentation of mixed dentition in cone-beam computed tomography (CBCT) images. MethodsA high-precision, automated deep learning model was built based on modified nnU-Net and U-Net networks and was used to classify and segment mixed dentition. It was trained on a series of 336 CBCT scans and tested using 120 mixed dentition CBCT scans from three centers and 143 permanent dentition CBCT scans from a public dataset. The diagnostic performance of the model was assessed and compared with those of two observers with different seniority levels. ResultsThe model achieved accurate classification and segmentation of specific tooth positions in the internal and external mixed dentition datasets (Dice similarity coefficient: 0.964 vs. 0.951; Jaccard coefficient: 0.931 vs. 0.921; precision: 0.963 vs. 0.945; recall: 0.945 vs. 0.941; F-1 score: 0.954 vs. 0.943). These indices consistently exceeded 0.9 across multiple conditions, including fillings, malocclusion, and supernumerary tooth, with an average symmetric surface distance of 0.091 ± 0.029 mm. For permanent dentition, the Dice similarity and Jaccard coefficients exceeded 0.90, the average symmetric surface distance was 0.190 ± 0.092 mm, and precision and recall exceeded 0.94. With the aid of the model, the performance of junior dentists in mixed dentition classification and segmentation improved significantly; in contrast, there was no significant improvement in the performance of senior dentists. The speed of segmentation conducted by the dentists increased by 20.9–22.8 times. ConclusionThe artificial intelligence model has strong clinical applicability, robustness, and generalizability for mixed and permanent dentition. Clinical SignificanceThe precise classification and 3D segmentation of mixed dentition in dentofacial deformities, supernumerary teeth, and metal artifacts present challenges. This study developed a deep learning approach to analyze CBCT scans, enhancing diagnostic accuracy and efficacy. It facilitates detailed measurements of tooth morphology and movement as well as informed orthodontic planning and orthotic design. Additionally, this method supports dental education by assisting doctors in explaining CBCT images to the families of pediatric patients.

Self-powered human movement measurement with a unidirectional ratchet driven wearable energy harvester

A large amount of energy is generated during human movement which is mostly not effectively utilized. To collect human movement energy and utilize it rationally, a wearable piezoelectric-electromagnetic energy harvester (WPEEH) driven by a unidirectional ratchet is proposed, which adopts the principle of up-conversion to convert mechanical energy into electrical energy. The electromagnetic power generator unit is used to obtain energy to power the sensing circuit, while piezoelectric generator unit can be used for both energy generation and self-powered sensing. A spring push rod and a ratchet structure are used to convert the swinging of the human limb into the unidirectional rotation of the device to enhance its energy harvesting efficiency. The theoretical model of the WPEEH is established, and the experimental test platform is built. The results show that the WPEEH is able to generate a maximum power of 17.2 mW at an oscillation frequency of 1.5 Hz. In the actual wearable test, the proposed energy harvester is able to supply the harvested energy to the low-power electronic devices through capacitors, and it enables to light up 42 LEDs as well as to drive the digital thermo-hygrometer to work continuously and stably. Apart from having excellent properties of a high output power and low drive frequency, the proposed energy harvester also exhibits a high energy density (7.1 × 10–5 W/cm3). What’s more, the speed of human movement can be predicted and calculated for different individuals by testing the results of the swing frequency with a maximum error of 2.71 %. The investigation proves that the proposed WPEEH can measure human movement and has great potential in the field of power supply for low-power electronic devices.

Single unit electrophysiology recordings and computational modeling can predict octopus arm movement.

The octopus simplified nervous system holds the potential to reveal principles of motor circuits and improve brain-machine interface devices through computational modeling with machine learning and statistical analysis. Here, an array of carbon electrodes providing single-unit electrophysiology recordings were implanted into the octopus anterior nerve cord. The number of spikes and arm movements in response to stimulation at different locations along the arm were recorded. We observed that the number of spikes occurring within the first 100ms after stimulation were predictive of the resultant movement response. Computational models showed that temporal electrophysiological features could be used to predict whether an arm movement occurred with 88.64% confidence, and if it was a lateral arm movement or a grasping motion with 75.45% confidence. Both supervised and unsupervised methods were applied to gain streaming measurements of octopus arm movements and how their motor circuitry produces rich movement types in real time. Deep learning models and unsupervised dimension reduction identified a consistent set of features that could be used to distinguish different types of arm movements. These models generated predictions for how to evoke a particular, complex movement in an orchestrated sequence for an individual motor circuit.

Enhancing facial nerve regeneration with scaffold-free conduits engineered using dental pulp stem cells and their endogenous, aligned extracellular matrix.

Objective. Engineered nerve conduits must simultaneously enhance axon regeneration and orient axon extension to effectively restore function of severely injured peripheral nerves. The dental pulp contains a population of stem/progenitor cells that endogenously express neurotrophic factors (NTFs), growth factors known to induce axon repair. We have previously generated scaffold-free dental pulp stem/progenitor cell (DPSC) sheets comprising an aligned extracellular matrix (ECM). Through the intrinsic NTF expression of DPSCs and the topography of the aligned ECM, these sheets both induce and guide axon regeneration. Here, the capacity of bioactive conduits generated using these aligned DPSC sheets to restore function in critical-sized nerve injuries in rodents was evaluated.Approach. Scaffold-free nerve conduits were formed by culturing DPSCs on a substrate with aligned microgrooves, inducing the cells to align and deposit an aligned ECM. The sheets were then detached from the substrate and assembled into scaffold-free cylindrical tissues.Main results. In vitroanalyses confirmed that scaffold-free DPSC conduits maintained an aligned ECM and had uniformly distributed NTF expression. Implanting the aligned DPSC conduits across critical-sized defects in the buccal branch of rat facial nerves resulted in the regeneration of a fascicular nerve-like structure and myelinated axon extension across the injury site. Furthermore, compound muscle action potential and stimulated whisker movement measurements revealed that the DPSC conduit treatment promoted similar functional recovery compared to the clinical standard of care, autografts. Significance. This study demonstrates that scaffold-free aligned DPSC conduits supply trophic and guidance cues, key design elements needed to successfully promote and orient axon regeneration. Consequently, these conduits restore function in nerve injuries to similar levels as autograft treatments. These conduits offer a novel bioactive approach to nerve repair capable of improving clinical outcomes and patient quality of life.

393 The role of on-animal sensors in understanding feed intake, behavior, and performance of grazing livestock

Abstract Intake by grazing animals is difficult to reliably measure in the field, particularly in more diverse grazing systems where pastures vary greatly across time and space in resource availability, which in turn leads to greater variation in animal time budgets and intake patterns. Advances in remote livestock monitoring (e.g., on-animal sensors, walk -over-weighing), and advanced analytics such as machine learning algorithms can contribute to the development of robust, adaptable tools for better understanding of animal performance and behavior in grazing systems. Many existing tools for predicting livestock production require estimates of feed intake, and while estimates based on forage sward characteristics have been developed and used for temperate, relatively homogenous pastures, they are less well defined for more diverse rangelands. These diverse pastures are more difficult to characterize due to their greater variation in quantity and quality; consequently, animal selectivity is greater in more variable pastures. However, such feedback loops between pasture diversity and animal behavior may not yet be adequately represented in biophysical models. On-animal sensors such as accelerometers and GPS devices can be used to develop new ways of assessing relative intake by providing accurate estimates of animal location and behavior profiles, such as the use of machine learning algorithms to detect how much time is spent grazing, walking, ruminating, or resting. Measurements of animal behavior could then be combined with other animal-based methods such as walk-over-weighing and fecal NIR to improve estimates of intake and production for a wide range of extensive grazing environments. Sensor-derived measurements of animal movement could also be used to improve estimates of the energetic costs of grazing, thereby improving estimates of energy available for growth. As an example, data on recent activity and paddock use from on-animal sensors could be entered into a model to estimate current feeding requirements, then combined with weather and pasture quality forecasts. The ensuing baseline could then be used to explore the potential variability of these projections and generate a range for possible expected performance over the next month or season. This approach could be repeated over time, and animal performance and behavior measures used to update model estimates and improve forward projections. These methods could also be used in conjunction with estimates of animal energy requirements to provide real-time prediction of relative feed intake, which would provide a step-change in modelling livestock production in extensive grazing systems. For these approaches to be successful, additional calibration data are needed across a wide variety of environments and species, and the tools must be cost-effective to implement. In summary, on-animal measurements provide an adaptable, data-driven approach that could be integrated with existing tools to improve our understanding of feed intake, behavior, and performance of grazing animals.

BLUE SABINO: Development of a BiLateral Upper-Limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output

Arm and hand function play a critical role in the successful completion of everyday tasks. Lost function due to neurological impairment impacts millions of lives worldwide. Despite improvements in the ability to assess and rehabilitate arm deficits, knowledge about underlying sources of impairment and related sequela remains limited. The comprehensive assessment of function requires the measurement of both biomechanics and neuromuscular contributors to performance during the completion of tasks that often use multiple joints and span three-dimensional workspaces. To our knowledge, the complexity of movement and diversity of measures required are beyond the capabilities of existing assessment systems. To bridge current gaps in assessment capability, a new exoskeleton instrument is developed with comprehensive bilateral assessment in mind. The development of the BiLateral Upper-limb Exoskeleton for Simultaneous Assessment of Biomechanical and Neuromuscular Output (BLUE SABINO) expands on prior iterations toward full-arm assessment during reach-and-grasp tasks through the development of a dual-arm and dual-hand system, with 9 active degrees of freedom per arm and 12 degrees of freedom (six active, six passive) per hand. Joints are powered by electric motors driven by a real-time control system with input from force and force/torque sensors located at all attachment points between the user and exoskeleton. Biosignals from electromyography and electroencephalography can be simultaneously measured to provide insight into neurological performance during unimanual or bimanual tasks involving arm reach and grasp. Design trade-offs achieve near-human performance in exoskeleton speed and strength, with positional measurement at the wrist having an error of less than 2 mm and supporting a range of motion approximately equivalent to the 50th-percentile human. The system adjustability in seat height, shoulder width, arm length, and orthosis width accommodate subjects from approximately the 5th-percentile female to the 95th-percentile male. Integration between precision actuation, human–robot-interaction force-torque sensing, and biosignal acquisition systems successfully provide the simultaneous measurement of human movement and neurological function. The bilateral design enables use with left- or right-side impairments as well as intra-subject performance comparisons. With the resulting instrument, the authors plan to investigate underlying neural and physiological correlates of arm function, impairment, learning, and recovery.

Mediastinal Shift Index: A Novel Postnatal Measurement of Mediastinal Movement that Predicts Survival in Neonates With Congenital Diaphragmatic Hernia on Extracorporeal Membrane Oxygenation

PurposeMediastinal position varies in neonates with congenital diaphragmatic hernia (CDH), reflecting contralateral shift due to mass effect. We aimed to create and validate a postnatal measurement of mediastinal positioning using chest radiographs in neonates with CDH who require extracorporeal membrane oxygenation. MethodsChart review identified neonates with CDH who required veno-arterial extracorporeal membrane oxygenation between 2017 and 2022. Mediastinal shift index (MSI) is the ratio of the distance between the venous cannula tip and the contralateral chest wall divided by the total width of the contralateral hemithorax. Three raters completed MSI measurements at designated timepoints: after cannulation, post- CDH repair, and immediately before decannulation. Intraclass correlation coefficients (ICC) assessed inter-rater agreement. Initial MSI and observed/expected lung head ratio (O/E LHR) were correlated and compared between survivors and non-survivors. Receiver operative characteristic (ROC) curve analysis evaluated the ability of MSI and O/E LHR to predict survival. Results38 neonates were included. MSI demonstrated excellent agreement (ICC>0.98) amongst raters. Initial MSI and O/E LHR had a moderate positive correlation (Spearman correlation = 0.47, p = 0.014). Initial MSI differed significantly between survivors and non-survivors (0.52 vs. 0.33, p = 0.035) as did O/E LHR (0.36 vs. 0.26, p = 0.036). ROC analysis revealed initial MSI >0.35 was predictive of survival with 73% sensitivity and 70% specificity. ConclusionMediastinal shift index is reliable and predicted survival with a higher specificity than O/E LHR. Future studies will elucidate the role of trending MSI over a patient's course to inform interventions to optimize mediastinal position. Level of Evidence3.

Results in Engineering Experimental investigation of drag loss and plate separation behavior of wet clutches under external forces

Understanding the drag loss behavior of wet clutches is crucial for the development of low-loss clutch systems. For non-horizontal installation positions and non-stationary applications, external forces caused by the inclination or vehicle dynamics act on the plates. It has yet to be investigated how external forces influence the plate separation process and distribution and, subsequently, the drag loss behavior. Therefore, this study aimed to fundamentally investigate wet clutches' drag loss and plate separation behavior under the effect of external forces. The investigations were conducted in three steps. First, fundamental knowledge of the plate separation and drag loss behavior was gained. The effects of relevant design and operating parameters on the drag loss behavior were determined then. Separation springs were finally tested to avoid the influence of external forces. The drag loss and plate separation behavior were investigated on a drag torque test rig based on different clutch systems. The external forces were applied by inclining the test rig. The plate separation behavior was determined through image-based measurement of the plate movements. It was found that the plates separate under external forces and that the drag torque curve shows characteristics similar to wet clutches operated without external forces applied. The utilization of the set total clearance depends on the inclination angle. Hence, increasing the set total clearance does not generally imply lower drag losses, but increasing the inclination angle leads to higher drag losses. It was confirmed that the drag loss behavior is not influenced by external forces when using spacing elements. The findings of this study extend the existing body of literature and may support the development process of brake and clutch systems.

Are Anthropometric Measures, Range of Motion, or Movement Control Tests Associated with Lumbopelvic Flexion during Barbell Back Squats?

Resistance training with the barbell back squat (BBS) exercise is practiced in sports, recreation, and rehabilitation. Although extensively debated, it is commonly believed and recommended that maintaining a neutral lumbopelvic alignment during BBS is an important technical aspect that might reduce the risk of injury. There is limited knowledge of how objectively measurable factors affect the extent to which the lumbopelvic region moves into flexion during a BBS. The aim of the study was to investigate the association among anthropometric measurements, range of motion in the hips and ankle joints, lumbopelvic movement control tests, and flexion of the lumbopelvic region during execution of the BBS. Observational, cross sectional. Eighteen experienced powerlifters and Olympic weightlifters were included and measurements of lumbopelvic movements were collected with inertial measurement units during BBS performed at 70 % of 1RM. Examination of anthropometric properties, range of motion in the hip and ankle joints, and lumbopelvic movement control tests were collected as independent variables. Linear regression analysis was used to investigate which independent variables were associated with lumbopelvic flexion during a BBS. The linear regression showed that a higher range of motion in ankle dorsiflexion could statistically significantly explain an increased amplitude of lumbopelvic flexion during the BBS. Anthropometrics, range of motion of the hips, and performance in lumbopelvic movement control tests did not show any statistically significant associations. The results suggest that strength and conditioning professionals and clinicians who instruct and assess lifting technique in the BBS and/or use the BBS to assess performance or as an intervention should recognize that a higher range of motion in the ankle joints might affect lumbopelvic flexion during the BBS. In practice, the value of an individual assessment of lifting technique focusing on the goal of the movement should be emphasized. 3.

Pupillometry and autonomic nervous system responses to cognitive load and false feedback: an unsupervised machine learning approach.

Pupil dilation is controlled both by sympathetic and parasympathetic nervous system branches. We hypothesized that the dynamic of pupil size changes under cognitive load with additional false feedback can predict individual behavior along with heart rate variability (HRV) patterns and eye movements reflecting specific adaptability to cognitive stress. To test this, we employed an unsupervised machine learning approach to recognize groups of individuals distinguished by pupil dilation dynamics and then compared their autonomic nervous system (ANS) responses along with time, performance, and self-esteem indicators in cognitive tasks. Cohort of 70 participants were exposed to tasks with increasing cognitive load and deception, with measurements of pupillary dynamics, HRV, eye movements, and cognitive performance and behavioral data. Utilizing machine learning k-means clustering algorithm, pupillometry data were segmented to distinct responses to increasing cognitive load and deceit. Further analysis compared clusters, focusing on how physiological (HRV, eye movements) and cognitive metrics (time, mistakes, self-esteem) varied across two clusters of different pupillary response patterns, investigating the relationship between pupil dynamics and autonomic reactions. Cluster analysis of pupillometry data identified two distinct groups with statistically significant varying physiological and behavioral responses. Cluster 0 showed elevated HRV, alongside larger initial pupil sizes. Cluster 1 participants presented lower HRV but demonstrated increased and pronounced oculomotor activity. Behavioral differences included reporting more errors and lower self-esteem in Cluster 0, and faster response times with more precise reactions to deception demonstrated by Cluster 1. Lifestyle variations such as smoking habits and differences in Epworth Sleepiness Scale scores were significant between the clusters. The differentiation in pupillary dynamics and related metrics between the clusters underlines the complex interplay between autonomic regulation, cognitive load, and behavioral responses to cognitive load and deceptive feedback. These findings underscore the potential of pupillometry combined with machine learning in identifying individual differences in stress resilience and cognitive performance. Our research on pupillary dynamics and ANS patterns can lead to the development of remote diagnostic tools for real-time cognitive stress monitoring and performance optimization, applicable in clinical, educational, and occupational settings.

Lévy Flight and the Interpersonal Distance of a Pedestrian in a Crowd

A simple self-experiment allows for the measurement of individual pedestrian movements, aiming to understand the deviations from intended directions seen in heterogeneous human crowds. The method involves pedestrian self-observations using the GPS sensor of a smartphone, providing a unique perspective on individual behavior within a crowd and offering a means to evaluate average pedestrian speed. The study is focused on individual mobility in the context of a heterogeneous crowd rather than the behavior of a crowd composed of similar types of people. With this study, an important contribution to the understanding of interpersonal distances in heterogeneous crowds is made.

Implementation of a digital distress detection system in palliative care: qualitative data on perspectives of a multiprofessional palliative care team

BackgroundDigital health technologies such as sensor systems are intended to support healthcare staff in providing adequate patient care. In the Department of Palliative Medicine (University Medical Center Freiburg), we developed and implemented a noninvasive, bed-based sensor system in a pilot study. The aim was to detect distress in patients who were no longer able to express themselves by monitoring heart and respiratory rates, vocalizations, and movement measurements. The sensor system was intended to supplement standard care, which generally cannot guarantee constant monitoring. As there is a lack of data on how healthcare professionals experience such a techno-digital innovation, the aim of this study was to explore how the multiprofessional palliative care team who piloted the sensor system perceived its potential benefits and limitations, and how they experienced the broader context of healthcare technology and research in palliative care.MethodsWe conducted a qualitative interview study with 20 members of the palliative care team and analyzed the recorded, verbatim transcribed interviews using qualitative content analysis.ResultsThe sensor system was described as easy to use and as helpful support for patients, care staff, and relatives, especially against the backdrop of demographic change. However, it could not replace human interpretation of stress and subsequent treatment decisions: this remained the expertise of the nursing staff. A potential reduction in personnel was expected to be a risk of a digital monitoring system. The special conditions of research and digital health technologies in an end-of-life context also became clear. Specifically, healthcare staff were open to health technologies if they benefited the patient and were compatible with professional nursing and/or palliative care attitudes. Additionally, a patient-protective attitude and possible interprofessional differences in priorities and the resulting challenges for the team became apparent.ConclusionsA potential digital solution for distress monitoring was considered useful by palliative care practitioners. However, interprofessional differences and compatibility with existing palliative care practices need to be considered before implementing such a system. To increase user acceptability, the perspectives of healthcare professionals should be included in the implementation of technological innovations in palliative care.

Validation of the Kinematic Assessment Protocol Used in the Technology-Supported Neurorehabilitation System, Rehabilitation Technologies for Hand and Arm (R3THA™), in Children and Teenagers with Cerebral Palsy.

This study evaluates the R3THA™ assessment protocol (R3THA-AP™), a technology-supported testing module for personalized rehabilitation in children with cerebral palsy (CP). It focuses on the reliability and validity of the R3THA-AP in assessing hand and arm function, by comparing kinematic assessments with standard clinical assessments. Conducted during a 4-week summer camp, the study assessed the functional and impairment levels of children with CP aged 3-18. The findings suggest that R3THA is more reliable for children aged 8 and older, indicating that age significantly influences the protocol's effectiveness. The results also showed that the R3THA-AP's kinematic measurements of hand and wrist movements are positively correlated with the Box and Blocks Test Index (BBTI), reflecting hand function and dexterity. Additionally, the R3THA-AP's accuracy metrics for hand and wrist activities align with the Melbourne Assessment 2's Range of Motion (MA2-ROM) scores, suggesting a meaningful relationship between R3THA-AP data and clinical assessments of motor skills. However, no significant correlations were observed between the R3THA-AP and MA2's accuracy and dexterity measurements, indicating areas for further research. These findings validate the R3THA-AP's utility in assessing motor abilities in CP patients, supporting its integration into clinical practice.