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AbstractHigh-velocity bullet impact (velocity: 700 ± 15 m/s) is a significant concern during asymmetric warfare. Ballistic helmets for high-velocity bullet impact and associated biomechanical responses of the headforms are actively sought. Further, data on the performance of ballistic helmets against high-velocity impact are not available in the literature. Toward this end, we have evaluated the performance of Patka (helmet) against high-velocity bullet impact. Patka was mounted on various headforms to assess the perforation of the Patka by the bullet and to measure various biomechanical parameters such as back face deformation (BFD), head kinematics, brain simulant pressures, and neck loads. Bullet impact in front and oblique (30° right to the sagittal axis) orientations were considered. Our results suggest that the Patka was effective in mitigating the high-velocity bullet impact. Perforation of the Patka by the bullet was not observed. BFD in the witness material was negligible (<1 mm). Measured head kinematics and brain simulant pressures were comparable to the quantities typically reported during low- and medium-velocity bullet impacts. Further, most of the biomechanical responses, except angular acceleration and neck force, were below 50% injury risk for mild traumatic brain injury (mTBI). Despite these encouraging trends, the shattering of bullets into fragments was also observed. These fragments traveled a considerable distance at reasonable velocities. This work provides unique insights regarding the performance of Patka and associated biomechanical responses of the headforms against high-velocity bullet impact, with implications in the design and evaluation of futuristic ballistic helmets.TERMINOLOGIESBallistic threat level: Ballistic threat levels are defined based on the impact velocity of the bullet. Different types of bullets are proposed to achieve the desired impact velocities corresponding to different ballistic threat levels.Ballistic helmet: Ballistic helmet is a type of protective headgear designed to protect the wearer head from potentially fatal impacts during combat operations.Ballistic performance: Ballistic performance of the helmet is evaluated based on the perforation resistance capacity of the helmet and back face deformation (BFD) of the helmet.Back face deformation (BFD): Back face deformation (BFD) of the helmet refers to the localized dent or bulge in the inner layer of the helmet due to ballistic impact. BFD is measured as a depth of indent in the witness material (e.g., Roma Plastilina #1 clay) of the cruciform cavity-based headform.Headform: Headform is a model of the human head replicating a few of the anatomical and material aspects. Headforms can vary in design and complexity based on the intended applications.Patka: Patka is a protective headgear designed to provide ballistic protection to the head against high-velocity threat (impact velocity: 700 ± 15 m/s). Patka is made up of hardened (armour) steel material.Head kinematics: Head kinematics refers to the motion of the head typically measured in terms of linear acceleration and angular velocity.Neck loads: Neck loads refer to the forces and moments experienced by the neck during a static or dynamic event.Brain simulant pressure: Brain simulant pressure is a hydrodynamic pressure generated within the brain simulant.Ballistic threat level: Ballistic threat levels are defined based on the impact velocity of the bullet. Different types of bullets are proposed to achieve the desired impact velocities corresponding to different ballistic threat levels.Ballistic helmet: Ballistic helmet is a type of protective headgear designed to protect the wearer head from potentially fatal impacts during combat operations.Ballistic performance: Ballistic performance of the helmet is evaluated based on the perforation resistance capacity of the helmet and back face deformation (BFD) of the helmet.Back face deformation (BFD): Back face deformation (BFD) of the helmet refers to the localized dent or bulge in the inner layer of the helmet due to ballistic impact. BFD is measured as a depth of indent in the witness material (e.g., Roma Plastilina #1 clay) of the cruciform cavity-based headform.Headform: Headform is a model of the human head replicating a few of the anatomical and material aspects. Headforms can vary in design and complexity based on the intended applications.Patka: Patka is a protective headgear designed to provide ballistic protection to the head against high-velocity threat (impact velocity: 700 ± 15 m/s). Patka is made up of hardened (armour) steel material.Head kinematics: Head kinematics refers to the motion of the head typically measured in terms of linear acceleration and angular velocity.Neck loads: Neck loads refer to the forces and moments experienced by the neck during a static or dynamic event.Brain simulant pressure: Brain simulant pressure is a hydrodynamic pressure generated within the brain simulant. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to evaluate the angular kinematics of the hip, knee, ankle, and the linear kinematics of the barbell during the back squat (BS) at different load intensities in powerlifters and weightlifters. Methods: Seventeen athletes were recruited (n = 14 powerlifters; n = 3 weightlifters). The 1-RM of the BS of each participant was calculated and, 1-week after, each participant was asked to perform 5 trials of the BS at different load intensities (i.e., 60%, 70%, 80%, 90%, 100%) of the 1-RM. An action camera recorded the execution of each BS trial in the sagittal plane and, afterward, the videos were analyzed by measuring the range of motion (ROM) of hip, knee, and ankle for the angular kinematics, and the timing, distances, speeds, and accelerations of the barbell for the linear kinematics. Results: Regarding the angular kinematics, no significant differences were found in the parameters in the starting and ending positions among the 5 trials, while a significant decrease was found in the hip relative angle (p = 0.026) in the maximum flexion position as load intensity increased. Regarding the linear kinematics, a significant difference was found in the descent acceleration (p = 0.049) in the descent phase, while a significant difference was found in the ascent speed (p <.001) and vertical speed of ascent (p <.001) in the ascent phase, which decreased as load intensity increased. Discussion: Our findings show that the angular and linear kinematics of BS change as load intensity increases. [ABSTRACT FROM AUTHOR]

In this study, we focus on human activity recognition, particularly aiming to distinguish the activity of praying (salat) from other daily activities. To achieve this goal, we have created a new dataset named HAR-P (Human activity recognition for Praying), which includes eight different activities: walking, running, sitting, standing, walking upstairs, walking downstairs, typing with a keyboard, and praying (salat). The HAR-P dataset was collected from 50 male individuals, who wore smartwatches on their dominant wrists. We compare the activity classification performance using three state-of-the-art algorithms from the literature: Long Short-Term Memory, Convolutional Long Short-Term Memory, and Convolutional Neural Network—Long Short-Term Memory. To assess the influence of sensors, data from accelerometer, gyroscope, linear acceleration sensor, and magnetic field sensor were utilized. The impact of individual sensor data as well as combinations thereof was investigated. The highest classification accuracy within single sensor groups, reaching 95.7%, was achieved using the accelerometer data with the Convolutional Long Short-Term Memory method. Combining two sensor groups resulted in an increase in accuracy of up to 9%. The highest accuracy of 96.4% was obtained by utilizing three sensor groups together with the Convolutional Neural Network—Long Short-Term Memory method. Furthermore, the evaluation of sensor and model performance was conducted using the stratified k-fold cross-validation method with 5-folds. These findings contribute significantly to evaluating the performance of sensor combinations and different algorithms in activity classification. This study may provide an effective foundation for the automatic recognition and tracking of human activities and offer an applicable model, particularly for the recognition of religious practices such as praying. [ABSTRACT FROM AUTHOR]

The second-moment (SM) method is a linear variant of the quasi-diffusion (QD) method for accelerating the iterative convergence of Sn source calculations. It has several significant advantages relative to the QD method, diffusion synthetic acceleration, and nonlinear diffusion acceleration. Here, we define a variant of this method for k-eigenvalue calculations that retains the advantages of the original method, and we computationally demonstrate the efficacy of the method for simple example calculations. In particular, this method has two important properties. First, it is a linear acceleration scheme requiring only the solution of a pure k-eigenvalue diffusion equation with a corrective source term as opposed to a k-eigenvalue drift-diffusion equation. Second, unconditional stability is achieved even when the diffusion equation is not discretized in a manner consistent with the Sn spatial discretization. We are unaware of any other scheme that has these properties. We also show a connection between our method and the k-eigenvalue acceleration technique of Barbu and Adams, which motivated us to develop our SM method. [ABSTRACT FROM AUTHOR]

This research unveils an improved indoor positioning method by utilizing inbuilt Inertial Measuring Unit (IMU) and pressure sensor of smartphone and enhancing accuracy in Global Positioning System (GPS)-challenged environments. Initially, Ground Truth (GT) trajectories in 3 cases were obtained by measuring different points with measuring-tape. Each GT trajectory represents actual Positional Data (PD) which is compared with the calculated PD trajectories generated from PD 1, 2, 3, 4, and 5 obtained with 5 different methods. Applying Rotational Transformation (RT), it amalgamates accelerometer and gyroscope data to derive earth linear accelerations (ELA) for PD 1. Applying a Fast Fourier Transform (FFT)-based High Pass Filter (HPF) enhances ELA, refining it into PD 2. Incorporating the magnetic field through the Kalman Filter produces PD 3. Linear Regression (LR) models on IMU and pressure sensor data generate PD 4. Through LR model training with past Positional Data 3, IMU, pressure sensor data, and Positional Data 1, the algorithm achieves superior accuracy in PD 5. Demonstrating a substantial 19.49–78.57% improvement over existing literature data, PD 5 exhibits the least average Root Mean Square Error of 0.51 m. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

There is concern that exposure to soccer headers may be associated with neurological sequelae. Training proper heading technique represents a coachable intervention that may reduce head acceleration exposure. The objective was to assess relationships between heading technique and head kinematics in female youth soccer players. Fourteen players (mean age = 14.4 years) wore instrumented mouthpieces during practices and games. Headers were reviewed by three raters to assign a technique score. Mixed models and LASSO regression evaluated associations of technique with peak linear acceleration (PLA), rotational acceleration (PRA), rotational velocity (PRV), and head impact power ratio (HIP Ratio) while adjusting for session type and ball delivery. Two hundred eighty-nine headers (n = 212 standing, n = 77 jumping) were analyzed. Technique score (p = 0.043) and the technique score – session type interaction (p = 0.004) were associated with PRA of standing headers, whereby each 10-unit increase in technique score was associated with an 8.6% decrease in PRA during games but a 5.1% increase in PRA during practices. Technique was not significantly associated with any other kinematic metrics; however, peak kinematics tended to decrease as technique score increased. LASSO regression identified back extension and shoulder/hip alignment as important predictors of peak kinematics. Additional research on heading technique and head acceleration is recommended. [ABSTRACT FROM AUTHOR]

This paper introduces an efficient and accurate interpolator for NURBS (non-uniform rational B-spline) curves, addressing the challenge of regulating feedrate under machining accuracy and dynamic constraints, particularly at sensitive corners. A recursive matrix representation and polynomial conversion are utilized to enhance the computation of NURBS curve intermediate points and derivatives. An improved adaptive planning method is presented to adjust the feedrate at sensitive corners, ensuring that chord error and dynamic constraints are met. The method integrates linear acceleration and deceleration stages to mitigate abrupt changes in acceleration and jerk. Additionally, a prediction–correction scheme-based interpolator is developed, employing an asynchronous mechanism to improve computational efficiency. The proposed method's effectiveness and correctness are validated through simulation tests and machining experiments. [ABSTRACT FROM AUTHOR]

Background: Increased fall risk caused by vestibular system impairment is a significant problem associated with aging. A vestibule is composed of linear acceleration-sensing otoliths and rotation-sensing semicircular canals. Otoliths, composed of utricle and saccule, detect linear accelerations. Otolithic organs partially play a role in falls due to aging. Aging possibly changes the morphology and functions of otoliths. However, the specific associations between aging and otolith changes remain unknown. Therefore, this study aimed to clarify these associations in mice. Methods: Young C56BL/6  N (8  week old) and old (108–117  weeks old) mice were used in a micro-computed tomography (μCT) experiment for morphological analysis and a linear acceleration experiment for functional analysis. Young C56BL/6  N (8  week old) and middle-aged (50  week old) mice were used in electron microscopy experiments for morphological analysis. Results: μCT revealed no significant differences in the otolith volume (p  =  0.11) but significant differences in the otolith density (p  =  0.001) between young and old mice. μCT and electron microscopy revealed significant differences in the structure of striola at the center of the otolith (μCT; p  =  0.029, electron microscopy; p  =  0.017). Significant differences were also observed in the amplitude of the eye movement during the vestibulo-ocular reflex induced by linear acceleration (maximum amplitude of stimulation  =  1.3G [p  =  0.014]; maximum amplitude of stimulation  =  0.7G [p  =  0.015]), indicating that the otolith function was worse in old mice than in young mice. Discussion: This study demonstrated the decline in otolith function with age caused by age-related morphological changes. Specifically, when otolith density decreased, inertial force acting on the hair cells decreased, and when the structure of striola collapsed, the function of cross-striolar inhibition decreased, thereby causing a decline in the overall otolith function. [ABSTRACT FROM AUTHOR]

Real-time hybrid test is an economical and efficient testing method to evaluate nonlinear structural performance. During the operation of real-time hybrid test, the time delay caused by the operating error of the actuator results in the worse stability and accuracy of the real-time hybrid test. To compensate for the delay of the actuator, the stability of the hybrid test system and the accuracy of the test are improved. In this paper, the advantages of the numerical integration algorithm and the linear acceleration extrapolation method are composed organically, and then a universal linear extrapolation-Newmark optimization compensation strategy is proposed to compensate for the time delay. Under the action of different seismic waves with and without the optimized compensation strategy, the self-developed DELTA-OpenSees real-time hybrid test system is used to comprehensively compare and analyze the real-time hybrid test of three-story frame structure with linear springs. The root mean square error and maximum error amplitude of the maximum error amplitude of the optimization results can reach 2.73% and 2.18%, respectively, which proves that the developed linear extrapolation-Newmark optimization compensation strategy can effectively improve the stability and accuracy of the real-time hybrid test system, and the optimization compensation strategy has good tracking performance and robustness. [ABSTRACT FROM AUTHOR]

Manual wheelchair propulsion represents a repetitive and constraining task, which leads mainly to the development of joint injury in spinal cord-injured people. One of the main reasons is the load sustained by the shoulder joint during the propulsion cycle. Moreover, the load at the shoulder joint is highly correlated with the force and moment acting at the handrim level. The main objective of this study is related to the estimation of handrim reactions forces and moments during wheelchair propulsion using only a single inertial measurement unit per hand. Two approaches are proposed here: Firstly, a method of identification of a non-linear transfer function based on the Hammerstein–Wiener (HW) modeling approach was used. The latter represents a typical multi-input single output in a system engineering modeling approach. Secondly, a specific variant of recurrent neural network called BiLSTM is proposed to predict the time-series data of force and moments at the handrim level. Eleven subjects participated in this study in a linear propulsion protocol, while the forces and moments were measured by a dynamic platform. The two input signals were the linear acceleration as well the angular velocity of the wrist joint. The horizontal, vertical and sagittal moments were estimated by the two approaches. The mean average error (MAE) shows a value of 6.10 N and 4.30 N for the horizontal force for BiLSTM and HW, respectively. The results for the vertical direction show a MAE of 5.91 N and 7.59 N for BiLSTM and HW, respectively. Finally, the MAE for the sagittal moment varies from 0.96 Nm (BiLSTM) to 1.09 Nm for the HW model. The approaches seem similar with respect to the MAE and can be considered accurate knowing that the order of magnitude of the uncertainties of the dynamic platform was reported to be 2.2 N for the horizontal and vertical forces and 2.24 Nm for the sagittal moments. However, it should be noted that HW necessitates the knowledge of the average force and patterns of each subject, whereas the BiLSTM method do not involve the average patterns, which shows its superiority for time-series data prediction. The results provided in this study show the possibility of measuring dynamic forces acting at the handrim level during wheelchair manual propulsion in ecological environments. [ABSTRACT FROM AUTHOR]

In recent years, the use of mobile sensors for human activity recognition has become an intriguing research area due to the proliferation of wearable and mobile sensors. In Muslim life, the prayer (Salah) is an activity that believers are obligated to perform five times a day. In this study, a new dataset, including Salah, is presented for use in human activity recognition. Named HAR-P (Human Activity Recognition for Praying), the dataset comprises linear acceleration, acceleration, magnetic field, and gyroscope sensor data for eight activities: walking, running, typing, downstairs, upstairs, sitting, standing, and praying. Data were collected from 50 male volunteers aged 15-60 using a smartwatch for the HAR-P dataset. The classification performance of LSTM, ConvLSTM, and CNN-LSTM models was compared for the HAR-P dataset. The highest average classification accuracy of 91% was achieved with the LSTM method using linear acceleration sensor data and the ConvLSTM model using acceleration sensor data, while the lowest average accuracy of 83.6% was attained with the gyroscope sensor data and the ConvLSTM method. [ABSTRACT FROM AUTHOR]

A real-time wind velocity vector and parameters estimation and wind model identification approach using a machine learning technique is addressed in this paper. The proposed method uses only the state measurements of an aircraft and does not require control commands, air data systems, or satellite-based data. Small unmanned aerial vehicles (UAVs) can benefit from this method, since it relies solely on measurement results from the common sensors as an attitude and heading reference system. The independence of external sources of information made estimations resistant to intentional errors. This algorithm uses long short-term memory neural networks (LSTM NNs) in a two-step deep learning process involving classification and regression. A classification NN was trained with four different labeled wind models, while individual regression NNs were trained to estimate the velocity vector and parameters of each wind model. The linear acceleration, angular velocity, and Euler angle measurements were used as the inputs of trained networks. The algorithm suggests in its first step identifying the exact wind model, and in its second step estimating the wind velocity vector and parameters using a properly assigned estimation from a trained network. A nonlinear six-degree-of-freedom simulation of straightforward and level turn maneuvers of a fixed-wing UAV in the presence of different wind models served as the dataset in the learning process. Monte Carlo simulations proved the accuracy and rapidity of the proposed algorithm in identifying the wind model and estimating three-dimensional wind velocity vector and parameters. [ABSTRACT FROM AUTHOR]

Introduction Military transport can induce whole-body vibrations, and combat almost always involves high impact between lower extremities and the ground. Therefore, robust splinting technology is necessary for lower extremity fractures in these settings. Our team compared a novel one-step spray-on foam splint (FastCast) to the current military standard structured aluminum malleable (SAM) splint. Materials and Methods Ten cadaveric specimens were subjected to complete tibia/fibula osteotomy. Specimens were fitted with custom accelerometer and gyroscope sensors superior and inferior to the fracture line. Each specimen underwent fracture and splinting from a standard of care SAM splint and an experimental FastCast spray foam splint in a randomized order. Each specimen was manually transported to an ambulance and then released from a 1 meter height to simulate impact. The custom sensors recorded accelerations and rotations throughout each event. Repeated-measures Friedman tests were used to assess differences between splint method within each event and between sensors within each splint method. Results During splinting, overall summation of change and difference of change between sensors for accelerations and rotations were greater for SAM splints than FastCast across all axes (P  ≤ 0.03). During transport, the range of acceleration along the linear superior/inferior axis was greater for SAM splint than FastCast (P  = 0.02), as was the range of rotation along the transverse plane (P  < 0.01). On impact, the summation of change observed was greater for SAM splint than FastCast with respect to acceleration and rotation on the posterior/anterior and superior/inferior axes (P  ≤ 0.03), and the cumulative difference between superior and inferior sensors was greater for SAM than FastCast with respect to anterior-axis rotation (P  < 0.05). Conclusion FastCast maintains stabilization of fractured lower extremities during transport and impacts to a significantly greater extent than SAM splints. Therefore, FastCast can potentially reduce the risk of fracture complications following physical stressors associated with combat and extraction. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article analyses the use of servomotors in the control systems of industrial equipment, focusing on the alternative offered by position and speed synchronization in relation to classical mechanical mechanisms. A complete methodology is presented to determine the dynamic parameters of the adopted kinematic system using electronic motion profiles. The results obtained constitute a mathematical model of the execution chain and an analysis of the basic quantities for linear motion, supported by actual measurements of the drive parameters. The merit of the article is to show that the servomotors can significantly simplify the design of the device, make it more flexible in adaptation to different assortments, and allow integration with systems predicting the technical condition of the device. The analysis of the results revealed significant differences in the constant rotational speed of the servomotor, which do not align with previous findings. The results suggest that changing the angular working range of the assembly to the range (205°;270°) could significantly affect the generated linear acceleration, reducing the risk of stalling. The calculations and graphs conducted allowed for the accurate representation of the actual mechanical system, considering its dynamic characteristics. The key conclusion is that precise mathematical modelling is essential to ensure the stability and durability of engineering components. [ABSTRACT FROM AUTHOR]

The growing focus on health management and smart technology advancements have propelled the use of wearable sensors in healthcare and human body motion analysis, particularly in preventing work‐related upper limb musculoskeletal disorders (MSDs) through guided exercises. However, most available wearable medical sensors are rigid, bulky, and incapable of in situ recognition of the comprehensive motion of human body. Here, a conformal self‐powered inertial displacement sensor (CSIDS) with geometric optimization for in situ noninvasive inertial data acquisition is proposed. Leveraging template‐assisted processing and COMSOL simulation, the geometric configurations of tribo‐layer materials, specifically focusing on the curvature of semicylindrical protrusions is systematically altered. This enhancement significantly improves the detection accuracy of joint range of motion. The features of shoulder joint bending angles and linear accelerations of the humerus are accurately captured using a deep learning model based on multilayer perceptron (MLP) networks, resulting in an exceptional recognition accuracy of 99.38% and 99.58%. Compared to traditional TENG wearable sensors that can only identify single metrics, CSIDS achieves a more comprehensive health assessment through inertial data detection. This system provides early warning for shoulder joint diseases, prevents MSDs, and extends to smart wearables for comprehensive joint health and ergonomic monitoring. [ABSTRACT FROM AUTHOR]

This work reports a comprehensive multiphysics modeling and optimization of a MEMS-based piezoelectric inertial measurement unit (IMU) sensor using a single proof mass. The studied miniaturized sensor consists of a stick-shaped cylindrical proof mass with two concentric rings connected between them with joints where the stress is concentrated. The advantage of introducing this new piezoelectric IMU concept is the concurrent detection of all six degrees of freedom (DOFs), i.e., 3-axis linear accelerations and angular velocities, using a single seismic mass and only four electrodes. The piezoelectric IMU structure optimization was carried out using finite element analysis (FEA) in order to obtain the optimal dimensions and generate the maximum stress in the piezoelectric layer. In fact, the mechanical (displacement and stress) and electrical (potential and sensitivity) performance of the IMU structure was determined when 3-axis linear accelerations and 3-axis angular velocities were applied. The simulation results showed a sensitivity of 1 mV/g and 6.8 mV/g, respectively, for linear accelerations along the x-/y- and z-axes. Along these same axes, the angular velocity sensitivity was approximately 0.39 µV/rad/s and 1.7 mV/rad/s. Moreover, the simulation results claim that the proposed IMU has good performance in terms of cross-axis sensitivity of less than 0.88% for all six DOFs, which is due to its perfectly symmetric circular structure. Finally, a simple block diagram is presented to separate the outputs related to linear acceleration from those related to angular velocity from the four sensing electrodes. [ABSTRACT FROM AUTHOR]

Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them—(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using "sensorized objects" that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

The present study aimed to observe and analyze the ocular movements induced by Coriolis stimulation (eccentric pitch while rotating: PWR) that induces Coriolis forces on the vestibular apparatus of healthy human individuals. A total of 31 healthy subjects participated in the study. Eccentric PWR was performed on 27 subjects, by pitching the participants' heads forward and backward at an angle of 30° each on an axis parallel and 7 cm below inter-aural axis, at a frequency of 0.5 Hz while on a chair rotating at a constant angular velocity of 97.2°/s on the earth-vertical axis. Ocular movements during stimulation were recorded using three-dimensional video-oculography. As a subsidiary analysis, 0.5 Hz head roll tilt was used as another stimulus that also induced torsional ocular movements. The forces induced on the vestibular apparatus, and phases of ocular torsion against the stimulus were calculated from the observed data. In the Coriolis stimulation during rightward yaw rotation, a rightward ocular torsion of 4.8° on average, was observed when the head pitched forward, and the direction of ocular torsion reversed when the head pitched backward. During leftward yaw rotation, these relationships were reversed with an average amplitude of 4.7° The phase of ocular torsion preceded that of Coriolis force by 0.2 s during rightward rotation and 0.14 s during leftward rotation. There were no significant differences in amplitude or phase between the directions of rotation. The phase lead of 0.5 Hz roll-tilt was significantly smaller than that of Coriolis stimulation (p < 0.01). Coriolis stimulation induced a specific pattern of ocular torsion, where its direction and phase suggested that the mechanism likely involved both the otolith and semicircular canals. Further studies may provide a clue to the magnitude of the otolith and semicircular canal contributions. [ABSTRACT FROM AUTHOR]

The present work is carried out in order to analyze the data for more than 15 000 coronal mass ejections (CMEs) during solar cycle 24, spanning the period of 2009–2017. We investigated, the properties of two categories of CMEs, narrow (W ≤ 20°) and normal (W > 20°), including angular width, linear speed, acceleration and their location. Based on statistical analysis, it is found the following. (1) 45% of the CMEs found in the angular range of W ∼ 10° and 30° with peak at 15°. (2) 70% of the narrow and 60% normal CMEs speed lies in the range of 150–400 km/s. The occurrence rate of both categories of CMEs declines sharply at linear speeds > 400 km/s and 0.1% narrow while 1.95% are of normal category, having the speeds above than 1000 km/s. (3) The 99% of narrow and 82% of normal CMEs are biased towards deceleration whereas small portion of normal CMEs do move with positive acceleration. We observed a low correlation between linear speed and acceleration –0.13 and –0.24 for narrow and normal CMEs respectively. (4) The latitudinal distribution of almost all narrow and normal CMEs were observed from equatorial regions during solar minimum, while during solar maximum, the distribution becomes wider and appears at all latitudes for both catagories. Despite of the fact that, solar cycle 24 is a weaker one in terms of geoeffectivity, but we observe a greater number of CMEs than solar cycle 23 throughout the solar maximum. [ABSTRACT FROM AUTHOR]

Featured Application: The machine learning model predicts Minimum Foot Clearance heights to prevent tripping falls. Integrated into exoskeletons or other assistive devices, it offers real-time interventions for vulnerable populations, enhancing safety with quick and accurate foot clearance adjustments. Tripping is the largest cause of falls, and low swing foot ground clearance during the mid-swing phase, particularly at the critical gait event known as Minimum Foot Clearance (MFC), is the major risk factor for tripping-related falls. Intervention strategies to increase MFC height can be effective if applied in real-time based on feed-forward prediction. The current study investigated the capability of machine learning models to classify the MFC into various categories using toe-off kinematics data. Specifically, three MFC sub-categories (less than 1.5 cm, between 1.5 and 2.0 cm, and higher than 2.0 cm) were predicted to apply machine learning approaches. A total of 18,490 swing phase gait cycles' data were extracted from six healthy young adults, each walking for 5 min at a constant speed of 4 km/h on a motorized treadmill. K-Nearest Neighbor (KNN), Random Forest, and XGBoost were utilized for prediction based on the data from toe-off for five consecutive frames (0.025 s duration). Foot kinematics data were obtained from an inertial measurement unit attached to the mid-foot, recording tri-axial linear accelerations and angular velocities of the local coordinate. KNN, Random Forest, and XGBoost achieved 84%, 86%, and 75% accuracy, respectively, in classifying MFC into the three sub-categories with run times of 0.39 s, 13.98 s, and 170.98 s, respectively. The KNN-based model was found to be more effective if incorporated into an active exoskeleton as the intelligent system to control MFC based on the preceding gait event, i.e., toe-off, due to its quicker computation time. The machine learning-based prediction model shows promise for the prediction of critical MFC data, indicating higher tripping risk. [ABSTRACT FROM AUTHOR]

This work describes a mathematical model for handwriting devices without a specific reference surface (SRS). The research was carried out on two hypotheses: the first considers possible circular segments that could be made during execution for the reconstruction of the trace, and the second is the combination of lines and circles. The proposed system has no flat reference surface, since the sensor is inside the pencil that describes the trace, not on the surface as in tablets or cell phones. An inertial sensor was used for the measurements, in this case, a commercial Micro-Electro Mechanical sensor of linear acceleration. The tracking device is an IMU sensor and a processing card that allows inertial measurements of the pen during on-the-fly tracing. It is essential to highlight that the system has a non-inertial reference frame. Comparing the two proposed models shows that it is possible to construct shapes from curved lines and that the patterns obtained are similar to what is recognized; this method provides an alternative to quaternion calculus for poorly specified orientation problems. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Exact Sciences is the property of Master Editora and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accelerometers are mainly used to measure the non-conservative forces at the center of mass of gravity satellites and are the core payloads of gravity satellites. All kinds of disturbances in the satellite platform and the environment will affect the quality of the accelerometer data. This paper focuses on the quality assessment of accelerometer data from the GRACE-FO satellites. Based on the ACC1A data, we focus on the analysis of accelerometer data anomalies caused by various types of disturbances in the satellite platform and environment, including thruster spikes, peaks, twangs, and magnetic torque disturbances. The data characteristics and data accuracy of the accelerometer in different operational states and satellite observation modes are analyzed using accelerometer observation data from different time periods. Finally, the data consistency of the accelerometer is analyzed using the accelerometer transplantation method. The results show that the amplitude spectral density of three-axis linear acceleration is better than the specified accuracy (above 10−1 Hz) in the accelerometer's nominal status. The results are helpful for understanding the characteristics and data accuracy of GRACE-FO accelerometer observations. [ABSTRACT FROM AUTHOR]

INTRODUCTION: To detect early cognitive impairment in community‐dwelling older adults, this study explored the viability of artificial intelligence (AI)‐assisted linear acceleration and angular velocity analysis during walking. METHODS: This cross‐sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini‐Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides. RESULTS: The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets. DISCUSSION: AI‐enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention. Highlights: Artificial intelligence (AI)‐enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community‐dwelling cohort.AI‐assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia. [ABSTRACT FROM AUTHOR]

Copyright of Ido Movement for Culture. Journal of Martial Arts Anthropology is the property of Idokan Poland Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article in the Physics Teacher journal describes an experiment involving a spool pulled across a horizontal surface to study friction forces. The friction force on the spool increases with the pull force, and a smaller friction force occurs when the ribbon is pulled from the top of the spool. The experiment explores the direction of motion of the spool based on the pulling force and friction force, providing insights into theoretical calculations and practical applications. The study highlights the relationship between the friction force, pulling force, and the point of application of the pulling force on the spool, offering valuable insights into the physics of motion and friction. [Extracted from the article]

The article discusses how the animated movie Lightyear introduces the concept of time dilation in a science fiction context, which can be used to spark discussions in physics classrooms. It highlights the potential benefits of using popular culture, like movies, to engage students in learning about physics. The analysis of time dilation in Lightyear is presented, along with suggestions for in-class discussions to help students understand the difference between scientific concepts and science fiction. The article concludes by emphasizing the importance of discussing scientific accuracy in movies and exploring other related topics like time travel and parallel timelines. [Extracted from the article]

This paper presents a collision-free transferring strategy for space manipulator in the static environment. The entire trajectory is planned based on acceleration to improve the smoothness. Firstly, the improved bidirectional alternating search strategy and the variable-step search strategy are applied to A* algorithm to find a collision-free linear acceleration trajectory of the captured target in Cartesian space effectively and rapidly. Then, the collision between the accessories carried on the target and the space robot is avoided by adjusting the attitude of the target in the aforementioned trajectory. Tracking-differentiator (TD) is adopted to achieve the smooth transition between the adjacent expected attitudes of the target. In addition, considering the posture of the manipulator during the task, an acceleration potential field is established to guide the arm-angle within the safe range all the time. Finally, the performance of the trajectory planning strategy is verified by simulation. [ABSTRACT FROM AUTHOR]

Headguard use is appropriate during some combat sports activities where the risks of injury to the face and ears are elevated. Headguards are highly effective in reducing the incidence of facial lacerations in studies of amateur boxers and are just as effective in other striking sports. They should be used in scenarios – especially sparring prior to competitions – where avoidance of laceration and subsequent exposure to potential blood-borne pathogens is important. Headguards are appropriate where avoidance of auricular injury is deemed important; limited data show a marked reduction in incidence of auricular injury in wrestlers wearing headguards. Headguards should not be relied upon to reduce the risk of concussion or other traumatic brain injury. They have not been shown to prevent these types of injuries in combat sports or other sports, and human studies on the effect of headguards on concussive injury are lacking. While biomechanical studies suggest they reduce linear and rotational acceleration of the cranium, changes in athlete behavior to more risk-taking when wearing headguards may offset any risk reduction. In the absence of high-quality studies on headguard use, the Association of Ringside Physicians recommends that further research be conducted to clarify the role of headguards in all combat sports, at all ages of participation. Furthermore, in the absence of data on gender differences, policies should be standardized for men and women. [ABSTRACT FROM AUTHOR]

Introduction: Spatial orientation refers to the perception of relative location and self-motion in space. The accurate formation of spatial orientation is essential for animals to survive and interact safely with their environment. The formation of spatial orientation involves the integration of sensory inputs from the vestibular, visual, and proprioceptive systems. Vestibular organs function as specialized head motion sensors, providing information regarding angular velocity and linear acceleration via the semicircular canals and otoliths, respectively. However, because forces arising from the linear acceleration (translation) and inclination relative to the gravitational axis (tilt) are equivalent, they are indistinguishable by accelerometers, including otoliths. This is commonly referred to as the tilt - translation ambiguity, which can occasionally lead to the misinterpretation of translation as a tilt. The major theoretical frameworks addressing this issue have proposed that the interpretation of tilt versus translation may be contingent on an animal's previous experiences of motion. However, empirical confirmation of this hypothesis is lacking. Methods: In this study, we conducted a behavioral experiment using goldfish to investigate how an animal's motion experience influences its interpretation of tilt vs. translation. We examined a reflexive eye movement called the vestibuloocular reflex (VOR), which compensatory-rotates the eyes in response to head motion and is known to reflect an animal's three-dimensional head motion estimate. Results: We demonstrated that the VORs of naïve goldfish do not differentiate between translation and tilt at 0.5 Hz. However, following prolonged visualtranslation training, which provided appropriate visual stimulation in conjunction with translational head motion, the VORs were capable of distinguishing between the two types of head motion within 3 h. These results were replicated using the Kalman filter model of spatial orientation, which incorporated the variable variance of process noise corresponding to the accumulated motion experience. Discussion: Based on these experimental and computational findings, we discuss the neural mechanism underlying the resolution of tilt-translation ambiguity within a context analogous to, yet distinct from, previous cross-axis VOR adaptations. [ABSTRACT FROM AUTHOR]

Capturing human locomotion in nearly any environment or context is becoming increasingly feasible with wearable sensors, giving access to commonly encountered walking conditions. While important in expanding our understanding of locomotor biomechanics, these more variable environments present challenges to identify changes in data due to person-level factors among the varying environment-level factors. Our study examined foot-specific biomechanics while walking on terrain commonly encountered with the goal of understanding the extent to which these variables change due to terrain. We recruited healthy adults to walk at self-selected speeds on stairs, flat ground, and both shallow and steep sloped terrain. A pair of inertial measurement units were embedded in both shoes to capture foot biomechanics while walking. Foot orientation was calculated using a strapdown procedure and foot trajectory was determined by double integrating the linear acceleration. Stance time, swing time, cadence, sagittal and frontal orientations, stride length and width were extracted as discrete variables. These data were compared within-participant and across terrain conditions. The physical constraints of the stairs resulted in shorter stride lengths, less time spent in swing, toe-first foot contact, and higher variability during stair ascent specifically (p<0.05). Stride lengths increased when ascending compared to descending slopes, and the sagittal foot angle at initial contact was greatest in the steep slope descent condition (p<0.05). No differences were found between conditions for horizontal foot angle in midstance (p≥0.067). Our results show that walking on slopes creates differential changes in foot biomechanics depending on whether one is descending or ascending, and stairs require different biomechanics and gait timing than slopes or flat ground. This may be an important factor to consider when making comparisons of real-world walking bouts, as greater proportions of one terrain feature in a data set could create bias in the outcomes. Classifying terrain in unsupervised walking datasets would be helpful to avoid comparing metrics from different walking terrain scenarios. [ABSTRACT FROM AUTHOR]

Stroke can impair mobility, with deficits more pronounced while simultaneously performing multiple activities. In this study, common clinical tests were instrumented with wearable motion sensors to study motor–cognitive interference effects in stroke survivors (SS). A total of 21 SS and 20 healthy controls performed the Timed Up and Go (TUG), Sit-to-Stand (STS), balance, and 10-Meter Walk (10MWT) tests under single and dual-task (counting backward) conditions. Calculated measures included total time and gait measures for TUG, STS, and 10MWT. Balance tests for both open and closed eyes conditions were assessed using sway, measured using the linear acceleration of the thorax, pelvis, and thighs. SS exhibited poorer performance with slower TUG (16.15 s vs. 13.34 s, single-task p < 0.001), greater sway in the eyes open balance test (0.1 m/s2 vs. 0.08 m/s2, p = 0.035), and slower 10MWT (12.94 s vs. 10.98 s p = 0.01) compared to the controls. Dual tasking increased the TUG time (~14%, p < 0.001), balance thorax sway (~64%, p < 0.001), and 10MWT time (~17%, p < 0.001) in the SS group. Interaction effects were minimal, suggesting similar dual-task costs. The findings demonstrate exaggerated mobility deficits in SS during dual-task clinical testing. Dual-task assessments may be more effective in revealing impairments. Integrating cognitive challenges into evaluation can optimize the identification of fall risks and personalize interventions targeting identified cognitive–motor limitations post stroke. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to measure deformations in geomaterials using the recently developed 'Smart Pebble' sensors. Smart Pebbles were included in triaxial test specimens of unbound aggregates stabilized with geogrids. The sensors are equipped with an aggregate particle/position tracking algorithm that can manage uncertainty arising due to signal noise and random walk effects. Two Smart Pebbles were placed in each test specimen, one at specimen's mid-height, where a geogrid was installed in the mechanically stabilized specimen, and one towards the top of the specimen. Even with simple raw data processing, the trends on linear vertical acceleration indicated the ability of Smart Pebbles to assess the geomaterial configuration and applied stress states. Employing a Kalman filter-based algorithm, the Smart Pebble position coordinates were tracked during testing. The specimen's resilient deformations were simultaneously recorded. bender element shear wave transducer pairs were also installed on the specimens to further validate the Smart Pebble small-strain responses. The results indicate a close agreement between the BE sensors and Smart Pebbles estimates towards local stiffness enhancement quantification in the geogrid specimen. The study findings confirm the viability of using the Smart Pebbles in describing the resilient behavior of an aggregate material under repeated loading. [ABSTRACT FROM AUTHOR]

Full‐body avatar presence is important for immersive social and environmental interactions in digital reality. However, current devices only provide three six degrees of freedom (DOF) poses from the headset and two controllers (i.e. three‐point trackers). Because it is a highly under‐constrained problem, inferring full‐body pose from these inputs is challenging, especially when supporting the full range of body proportions and use cases represented by the general population. In this paper, we propose a deep learning framework, DivaTrack, which outperforms existing methods when applied to diverse body sizes and activities. We augment the sparse three‐point inputs with linear accelerations from Inertial Measurement Units (IMU) to improve foot contact prediction. We then condition the otherwise ambiguous lower‐body pose with the predictions of foot contact and upper‐body pose in a two‐stage model. We further stabilize the inferred full‐body pose in a wide range of configurations by learning to blend predictions that are computed in two reference frames, each of which is designed for different types of motions. We demonstrate the effectiveness of our design on a large dataset that captures 22 subjects performing challenging locomotion for three‐point tracking, including lunges, hula‐hooping, and sitting. As shown in a live demo using the Meta VR headset and Xsens IMUs, our method runs in real‐time while accurately tracking a user's motion when they perform a diverse set of movements. [ABSTRACT FROM AUTHOR]

A meshfree orthotropic laminated shell model for geometrically nonlinear static and dynamic analysis based on the reproducing kernel particle method (RKPM) and the Mindlin-Reissner shell theory is proposed for the first time, suitable for dealing with the finite deformation of composite shell structures of arbitrary geometry in engineering. The Mindlin-Reissner shell theory is adopted to describe the geometric configuration of the laminated shells via a single-layer particle. Then, the Galerkin weak form governing equations are established explicitly and discretized using RKPM and linear transformation in the thickness direction. Furthermore, a laminate stacking sequence that can eliminate the coupling terms of linear and angular acceleration is proposed for simplification. For considering the orthotropic materials, the stress–strain transformation between local and material coordinates is introduced. A stiffened laminated shell model is also presented for simulating complex composited structures. The accuracy and convergence of the proposed laminated shell model are verified through several static and dynamic benchmarks, demonstrating the ability of the presented model to solve the nonlinear response of composite structures. [ABSTRACT FROM AUTHOR]

Micro-acceleration generation during ultra-low-frequency micro-vibration calibration is a sensitive issue. There are issues of traditional pendulum tables being unable to change the pendulum radius and direction to produce micro-accelerations of different magnitudes, and the line shakers having a low signal-to-noise ratio when the vibration amplitude is the same as that of the pendulum tables. Therefore, a novel ultra-low-frequency micro-vibration calibration method is proposed to solve the above issues based on virtual pendulum motion trajectories of the Stewart platform. The micro-accelerations of 10-5 to 10-3 m/s2 can be generated by the trajectories with the radius of up to 12 m, the displacement amplitudes of up to 11.636 mm and the frequencies between 0.01 and 0.1 Hz. In the virtual pendulum motion, the maximum acceleration can be 2481 times greater than the acceleration of linear motion at the same frequency and displacement amplitude. In a comparison experiment with the current rotating platform, the maximum relative deviation of sensitivity amplitude calibration for pendulum motion around the x- and y-axis based on the Stewart platform are 0.411% and 0.295% respectively. The above results demonstrate the validity and reliability of this kind of method. [ABSTRACT FROM AUTHOR]

Analytical probability density function of acceleration random response for cubic nonlinear product package has seldomly been investigated due to the difficulty of solving nonlinear random problem and less attention paid to the acceleration response. In this paper, the analytical solution for the probability density function of linear package acceleration response under random vibration is obtained, and on this basis, the equivalent linearization method is introduced to further explore the approximate analytical solution of acceleration response probability density function for cubic nonlinear product package under random vibration. Some suggestions on packaging optimal design are given by analyzing the sensitivity of acceleration random response to the external excitation and system parameters, and the first-passage failure probability for the acceleration response of cubic nonlinear package under random vibration is analyzed. The results show that the equivalent linearization method can precisely predict the acceleration response probability density function for the cubic nonlinear package under certain conditions, and the applicable range of the method is given by the equivalent stiffness formula. Nonlinear characteristic parameter γ of the cubic package activates acceleration response a completely different trend from the quasi-linear system. Properly increasing the system stiffness and decreasing the damping ratio can effectively reduce the acceleration response strength of the product package. As the vibration time increases and the product fragility decreases, the first-passage failure probability of the product package increases. The analysis provides theoretical foundation and guidance for packaging optimization design. [ABSTRACT FROM AUTHOR]

The vehicle's windshield plays an important role in the safety and comfort of drivers and passengers, so the windshield is solicited to many loads, such as the deformation of the vehicle body, the wind force, the crash between vehicles and vehicles, and a pedestrian. The primary goal of this study is to examine the mechanical and structural design of laminated glass for windshields in the scenario of an adult dummy head impact. The head impactor is represented by a spherical form covered in a rubber hull. The pedestrian head's Finite Element Method (FEM) is created per the requirements of Global Technical Regulations (GTRs). It weighs approximately 4 kg and strikes a windshield at a speed of 11.11 mm/ms. Furthermore, the windshield model is designed with two layers of glass with a layer of Polyvinyl Butyral (PVB). Our study's windshield Finite Element model is modeled with shell elements. The mathematical models of the PVB are described in a systematic manner. The model accurately represents the windshield's behavior, with critical fracture stress occurring in the impacted zone and maximum linear acceleration of the dummy head. The obtained results showed good agreement in energy absorption and maximum stress due to the impact. [ABSTRACT FROM AUTHOR]

The article provides a discussion on the results of the authors’ original studies of a power transmission system of a mining scraper conveyor coupled with an innovative highly flexible clutch, conducted in operating conditions. The research consisted in establishing the static characteristics of the highly flexible clutch in question, determining the torsional vibrations of the said highly flexible clutch and the vibrations of the transmission housings at a test rig, verifying if the coupling between the innovative flexible clutch and a typical scraper conveyor drive unit was correct, and testing durability of individual components of the highly flexible clutch. Following the aforementioned tests and based on the static characteristics of the highly flexible clutch examined, one can distinguish three phases of its operation: initial, main, and final – all differing in terms of flexibility. Furthermore, upon increasing the flexibility of the metal clutch, a significant decline in the root mean square (RMS) values of linear vibration accelerations was observed compared to the blocked condition of the clutch. It was further noticed that, as the torsional vibrations of the clutch shaft were increasing, the linear vibrations measured at the transmission bearing housings were decreasing significantly. Based on the tests conducted in operating conditions, it was found that the durability of the flexibilising system (bolt and nut) was sufficient and that there were no thermal effects associated with the motion of the system components. [ABSTRACT FROM AUTHOR]

A detailed investigation of walking spaces is essential for ensuring the safety of mobility-impaired pedestrians because the conditions of walking spaces affect the daily lives. Seoul Metropolitan Government is exploring barrier-free policies and the Seoul universal design guidelines define the structure of pedestrian spaces. However, these guidelines apply to only a few representative pedestrian-friendly areas or public institutions, causing mobility-impaired people to experience restrictions in other areas. This study propose possibility to investigate condition of pedestrian space using smartphones and strollers with a crowdsourcing model. A smartphone was attached to the cup holder of a stroller to acquire data during its drive through a residential complex. While the stroller was driving, acceleration, rotation, location, and photo data were obtained using a free app installed on a smartphone. As a result of experiment, the acquired data showed different variations in linear acceleration depending on the roughness of the surface. When there was a slope, there was a change in the vertical angular. Therefore, possibility was determined to investigate the flatness of the walking space using a smart device and a four-wheeled vehicle. Additionally, a crowdsourcing model was proposed to survey the entire city in a short period of time. This model includes providing highly feasible rewards to increase citizen participation in surveys. [ABSTRACT FROM AUTHOR]

* Problem Statement: Soccer players expose themselves to repetitive head impacts (RHI) by purposefully heading the ball - an act that has been suggested to adversely affect brain structure and function and potentially contribute to the long-term development of neurodegenerative disorders. A deeper understanding of the head's kinematic response to these RHI is crucial to assess the actual risk resulting from routine soccer heading. To that aim, we investigated the influence of a comprehensive set of heading-related factors on the resulting linear and rotational acceleration of the head, as obtained by wearable sensors in a field study. * Methods: Across 26 matches, 19 semi-professional female soccer players (23.0±3.7 years) were equipped with wearable head impact sensors that registered peak linear acceleration (PLA) and peak rotational acceleration (PRA) of on-field head impact events. Actual headers were confirmed and further allocated to the following categories using video analyses: Scenario, Distance, Player Movement, Ball Reflection, Impact Location, Duel, Jump. Linear mixed models were used to assess the relationships between these factors and PLA as well as PRA. * Results: Average PLA and PRA of the head due to heading was 29.6 (±18.1) g and 6195.6 (±4448.1) rad/s², respectively. Analyses revealed a statistically significant influence of three factors on both PLA (R2=0.34) and PRA (R2=0.37). Next to subject-related factors (p<0.001; proportional variance: 12.6% [PLA] and 19.0% [PRA]), especially longer in-air distances of the ball prior to heading (p<0.001; proportional variance: 24.6% [PLA] and 19.2% [PRA]) as well as a greater extent of ball reflection due to a header (p<0.001; proportional variance: 9.2% [PLA] and 6.9% [PRA]) were significantly associated with increases in head impact kinematics. * Conclusion: Our findings demonstrate that the ball's travelling distance and the extent of ball reflection due to a header directly affect the head's kinematic response to purposeful headers in female players. Along with these factors, future studies should focus on the direct assessment of inter-individual differences in heading technique and anthropometric variables to increase the current understanding of the potential risk resulting from RHI due to soccer heading. [ABSTRACT FROM AUTHOR]

This study quantified head impact exposures for Canadian university football players over their varsity career. Participants included 63 players from one team that participated in a minimum of 3 seasons between 2013 and 2018. A total of 127,192 head impacts were recorded from 258 practices and 65 games. The mean (SD) number of career impacts across all positions was 2023.1 (1296.4), with an average of 37.1 (20.3) impacts per game and 7.4 (4.4) impacts per practice. The number of head impacts that players experienced during their careers increased proportionally to the number of athletic exposures (P < .001, r = .57) Linebackers and defensive and offensive linemen experienced significantly more head impacts than defensive backs, quarterbacks, and wide receivers (P ≤ .014). Seniority did not significantly affect the number of head impacts a player experienced. Mean linear acceleration increased with years o f seniority within defensive backs and offensive linemen (P ≤ .01). Rotational velocity increased with years of seniority within defensive backs, defensive and offensive linemen. running backs, and wide receivers (P < .05). These data characterize career metrics of head impact exposure for Canadian university football players and provide insights to reduce head impacts through rule modifications and contact regulations. [ABSTRACT FROM AUTHOR]

Occupational ergonomics aims to optimize the work environment and to enhance both productivity and worker well-being. Work-related exposure assessment, such as lifting loads, is a crucial aspect of this discipline, as it involves the evaluation of physical stressors and their impact on workers' health and safety, in order to prevent the development of musculoskeletal pathologies. In this study, we explore the feasibility of machine learning (ML) algorithms, fed with time- and frequency-domain features extracted from inertial signals (linear acceleration and angular velocity), to automatically and accurately discriminate safe and unsafe postures during weight lifting tasks. The signals were acquired by means of one inertial measurement unit (IMU) placed on the sternums of 15 subjects, and subsequently segmented to extract several time- and frequency-domain features. A supervised dataset, including the extracted features, was used to feed several ML models and to assess their prediction power. Interesting results in terms of evaluation metrics for a binary safe/unsafe posture classification were obtained with the logistic regression algorithm, which outperformed the others, with accuracy and area under the receiver operating characteristic curve values of up to 96% and 99%, respectively. This result indicates the feasibility of the proposed methodology—based on a single inertial sensor and artificial intelligence—to discriminate safe/unsafe postures associated with load lifting activities. Future investigation in a wider study population and using additional lifting scenarios could confirm the potentiality of the proposed methodology, supporting its applicability in the occupational ergonomics field. [ABSTRACT FROM AUTHOR]

Head impacts in bicycle accidents are typically oblique to the impact surface and transmit both normal and tangential forces to the head, causing linear and rotational head kinematics, respectively. Traditional expanded polystyrene (EPS) foam bicycle helmets are effective at preventing many head injuries, especially skull fractures and severe traumatic brain injuries (TBIs) (primarily from normal contact forces). However, the incidence of concussion from collisions (primarily from rotational head motion) remains high, indicating need for enhanced protection. An elastomeric honeycomb helmet design is proposed herein as an alternative to EPS foam to improve TBI protection and be potentially reusable for multiple impacts, and tested using a twin-wire drop tower. Small-scale normal and oblique impact tests showed honeycomb had lower oblique strength than EPS foam, beneficial for diffuse TBI protection by permitting greater shear deformation and had the potential to be reusable. Honeycomb helmets were developed based on the geometry of an existing EPS foam helmet, prototypes were three-dimensional-printed with thermoplastic polyurethane and full-scale flat and oblique drop tests were performed. In flat impacts, honeycomb helmets resulted in a 34% higher peak linear acceleration and 7% lower head injury criteria (HIC15) than EPS foam helmets. In oblique tests, honeycomb helmets resulted in a 30% lower HIC15 and 40% lower peak rotational acceleration compared to EPS foam helmets. This new helmet design has the potential to reduce the risk of TBI in a bicycle accident, and as such, reduce its social and economic burden. Also, the honeycomb design showed potential to be effective for repetitive impact events without the need for replacement, offering benefits to consumers. [ABSTRACT FROM AUTHOR]

Study Design. This is a cross-sectional study. Objective. To evaluate the effectiveness of a novel finger Kinematic Parameter-Based Tool in the grip and release (G&R) test for assessing degenerative cervical myelopathy (DCM). Summary of Background Data. The development and progression of DCM symptoms are gradual and obscure. Although previous studies have objectively evaluated hand movements specific to myelopathy using the G&R test, virtual reality, or wearable sensors, these methods have limitations, such as limited discrimination or inconvenience for simple screening. Consequently, there is a need to develop effective screening methods. Materials and Methods. Totally, 297 asymptomatic volunteers and 258 DCM patients were enrolled. This system comprises a wearable acceleration/gyro sensor. The acceleration/gyro sensor was placed on the little finger of the participants to perform 40 cycles of full-range G&R as quickly as possible. The collected data were then transformed into kinematic parameters using sensor-based software and R studio software (version: RStudio 2022.07.2 +576, Boston, USA). Gender, age, and body mass index (BMI) subgroups (classified as BMI <18.5--below normal weight; 18.5≤ BMI <25--normal weight group; BMI ≥25--overweight group) were matched as predictor variables, and 201 pairs were matched. Nonparametric analysis using the Mann-Whitney U test was used for diagnosing the differences between the two groups, and Kruskal-Wallis's test followed by the Mann-Whitney U test was used for analyzing the differences among three different age groups (<40, 41--60, and > 60 yr group). The cut-off value of 10s G&R cycles and a combined parameter were determined using receiver operating characteristics curve analysis, area under the curve, and Youden index. Results. The authors found that little finger kinematic parameters were significantly lower in DCM patients than in asymptomatic participants. The optimal diagnostic indicator appeared to be the average of the top 10 linear accelerations with an area under the curve of 0.923. Conclusion. The Finger Kinematic Test System is an objective, practical, and quantitative utility that appears to have the capacity to diagnose and evaluate the severity of DCM. [ABSTRACT FROM AUTHOR]

Accurate orientation and position estimation are critical elements in optimizing real-time object tracking performance when leveraging smartphone sensors such as accelerometers and gyroscopes. The primary challenges encountered in smartphone-based object tracking are attributed to the GPS signal, canyon effect, and orientation errors, accumulation error in sensor. To address these limitations, a novel approach is proposed wherein a smartphone application is developed based on IMU Multi -sensor fusion using Kalman filter and Rotation vector. The proposed approach integrates Kalman filtering to fuse sensor data and leverages the rotation vector for precise orientation estimation. Additionally, geohash filtering is employed to efficiently proficiency in quantifying intricate spatial interdependencies and display track paths on maps within the application. A detailed mathematical analysis and thorough comparison with existing algorithms in the field proves the dexterity of the proposed object tracking scheme. The comprehensive evaluation showcases the algorithm's capability and advancement compared to state-of-the-art approaches. [ABSTRACT FROM AUTHOR]