Search Results (2,076)

Background/Objectives: Frailty is common among rural Chinese older adults despite relatively high daily physical activity, a phenomenon known as the “rural frailty paradox.” Conventional moderate-to-vigorous physical activity (MVPA) metrics rely on absolute cut-points and are often highly correlated with activity volume, limiting their ability to distinguish the roles of activity volume and activity intensity distribution. We therefore applied a cut-point-free accelerometer approach using average acceleration (AvAcc) and intensity gradient (IG) to distinguish activity volume from activity intensity distribution and to examine whether activity intensity distribution, together with diet quality, could help explain the rural frailty paradox beyond total activity volume alone. Methods: In this cross-sectional analysis of the Healthy Aging and Lifestyle Enhancement study, 1203 rural older adults were included. Physical activity (PA) was objectively measured using triaxial accelerometers to derive AvAcc and the IG. Diet quality was assessed using the China Prime Diet Quality Score (CPDQS), and frailty was assessed using the Fried frailty phenotype adapted for rural Chinese older adults. Multiple linear regression, joint effect models, and restricted cubic spline analyses were conducted after adjustment for age, sex, chronic disease status, total energy intake, and related covariates. Results: In mutually adjusted models, higher IG and CPDQS were independently associated with lower frailty scores, whereas AvAcc was not. In the fully adjusted model, IG (β = −0.14, p < 0.001) and CPDQS (β = −0.10, p < 0.001) were inversely associated with frailty score, while AvAcc showed no significant association (p = 0.665). In joint analyses, compared with the low-IG/low-CPDQS group, participants with high IG/high CPDQS had the lowest frailty scores (β = −0.28, p < 0.001), followed by those with low IG/high CPDQS (β = −0.20, p = 0.002). Restricted cubic spline analyses indicated a non-linear association between IG and frailty and an approximately linear inverse association for CPDQS. Conclusions: These findings suggest that, among rural older adults, frailty may be more strongly associated with activity intensity distribution than with total activity volume alone. Together with diet quality, this may help explain the rural frailty paradox. Full article

Low back pain associated with exposure to whole-body vibration (WBV) is common among agricultural workers, and seated posture significantly affects health outcomes from WBV exposure. Current posture assessment methods rely on manual observation or body-worn sensors, which are labor-intensive and impractical for continuous monitoring. We developed a machine learning approach to classify seated posture using force sensors and accelerometers integrated into a vibration sensing seat pad for use in agricultural machinery, avoiding the need for body-worn sensors. Twenty-four participants were exposed to WBV in different upper body postures while seat pad force and acceleration data were recorded. We compared four machine learning architectures: Logistic Regression, Random Forest, Support Vector Machine, and Recurrent Neural Network with Gated Recurrent Unit (GRU). The GRU architecture substantially outperformed baseline models, achieving 89% accuracy (weighted F1 = 0.89) in classifying forward and backward leaning postures. To our knowledge, this study demonstrates the first application of machine learning to classify seated postures from seat pad force measurements during WBV exposure. Temporal modeling with an 18 s window proved essential for accurate classification, enabling non-invasive, continuous posture monitoring. Full article

Background: Wearable sensors have gained increasing popularity as an objective method for remotely monitoring infant movement in naturalistic settings. Over the first year of life, infants generate a wide range of motions, from goal-directed to spontaneous movement. These include linear movements, such as kicks, and orientation changes, such as postural transitions. Many sensor processing pipelines emphasize capturing linear movements through movement-generated acceleration while focusing less on information about orientation embedded in the gravitational part of the data. Here, we introduce a complementary gravity-referenced approach that extracts the gravitational component of accelerometer signals to estimate limb orientation, extending the reliable quantification of rich and detailed aspects of infant movement. Infant orientation has demonstrated clinical relevance, including associations with later neuromotor outcomes, and it can be used to chart infant motor development, motivating the development of objective methods to quantify orientation from sensor data. Methods: Wearable sensors (Opal APDM) were used to longitudinally evaluate infant motor activity recorded in sessions conducted at 3, 6, 9, and 12 months of age. We extracted data from a 5 min segment that has simultaneous video recordings. From these datasets, applying the gravity-referenced method, we computed pitch, roll, and yaw, angles that collectively describe limb orientation. We then quantified orientation variability using axis-specific circular standard deviations (SDs) for pitch, roll, and yaw and a multi-axis composite measure based on generalized variance. Results: Axis-specific circular SDs for pitch, roll, and yaw, as well as the composite generalized variance, increased significantly from 3 to 12 months (p ≤ 0.01 for each metric). Composite variability was strongly associated with Mullen gross motor outcomes at 9 and 12 months of age (r = 0.55, p < 0.001). Conclusions: Overall, gravity-referenced pitch, roll, and yaw provide rich orientation features that increased as infants develop more postural transitions. Furthermore, the orientation features correlated with standardized measures of infant motor function. These orientation metrics can complement traditional linear kinematic measures and improve our ability to granularly track infant motor development in the first year of life. Full article

Accurately monitoring physical activity, including stationary cycling on an exercise bike, is important in managing chronic diseases and rehabilitation after lower limb surgery. This study aimed to validate a new smartphone-based sensor application (the BeSAFE+) for activity recognition and step counting across five phone placements, using the SENS Motion^® system as a reference standard, and observed activity time as ground truth. In a laboratory-based study, 20 participants performed walking, brisk walking, running, high- and low-intensity cycling, sitting, standing, and lying activities while carrying five smartphones placed in the front and back trouser pockets, a backpack, a running armband, and a fanny pack, and wearing the activity tracker. The front pocket placement had the most accurate classification during cycling activities (89–93%) versus SENS Motion^® (96–98%). For other activities, the highest overall classification accuracy was achieved with the phone in the back pocket. Overall, the SENS Motion^® activity tracker demonstrated higher classification accuracy than most smartphone placements across all activities, except for running. Nevertheless, several smartphone placements and Application Programming Interface (API) approaches achieved activity recognition and step count estimates that were not significantly different from the SENS Motion^® activity tracker, indicating that smartphone-based activity recognition can be valid under specific conditions. Full article

Background/Objectives: Evidence suggests that modifiable lifestyle interventions improve disability in relapsing multiple sclerosis (MS); however, interactions between different factors may impact outcomes. Thus, the objective of this secondary analysis was to investigate diet-induced effects on the impact of MS and effect modification by other modifiable lifestyle factors. Methods: The physical and psychological impact of MS was assessed with the MS Impact Scale-29 (MSIS) at run-in, baseline, 12 weeks, and 24 weeks. Participants were randomized at baseline to the Swank low-saturated fat or Wahls modified Paleolithic elimination diets and instructed to maintain usual physical activity, objectively measured with an accelerometer, throughout the trial. Baseline information on sleep, physical activity, alcohol, and smoking was explored as effect modifiers. Results: Among the Swank group, MSIS-Physical scores improved from 33.8 ± 3.8 at baseline to 28.7 ± 3.6 at 12 weeks (p = 0.04) and 25.3 ± 3.5 at 24 weeks (p < 0.001). MSIS-Psychological scores also improved from 35.7 ± 3.3 at baseline to 25.6 ± 2.6 at 12 weeks (p = 0.001) and 22.8 ± 2.4 at 24 weeks (p < 0.001). Among the Wahls group, MSIS-Physical scores improved from 33.8 ± 3.1 at baseline to 21.7 ± 3.0 at 12 weeks (p < 0.001) and 19.0 ± 3.1 at 24 weeks (p < 0.001). MSIS-Psychological scores also improved from 38.4 ± 3.8 at baseline to 25.5 ± 3.8 at 12 weeks (p < 0.001) and 20.6 ± 3.6 at 24 weeks (p < 0.001). Improvements in MSIS-Physical were greater among participants who were physically inactive or drank little alcohol at baseline. Conclusions: Both diets led to favorable within-group improvements in the perceived impact of MS. People with MS who are physically inactive or drink little alcohol may benefit the most from dietary interventions. Full article

Localization and navigation techniques have become fundamental for modern lives, while achieving accurate results indoors still remains a significant challenge. The widespread adoption of smart devices, and especially smartphones, has increased the need for accurate and robust pedestrian dead reckoning systems that operate in infrastructure-less environments. Pedestrian dead reckoning’s primary challenge is maintaining accuracy despite varying smartphone placements (attitudes) and the noisy, low-cost inertial measurements units. In this work, a comprehensive pedestrian dead reckoning framework is presented that integrates advanced step counting and heading estimation techniques. For step detection and counting, we propose a robust step counting algorithm that utilizes the optimum fusion of the raw IMU readings, i.e., accelerometer, linear accelerometer, gyroscope, and magnetometer readings, each broken down into three degrees of freedom for different body placements and walking speeds. Furthermore, to address the critical issue of heading estimation, we propose the heading estimation axis mapping (HEAT-MAP) algorithm, which dynamically adjusts the sensor axes in response to the smartphone’s orientation, ensuring a consistent coordinate frame and reducing heading drift. Moreover, to eliminate cumulative pedestrian dead reckoning errors, the system incorporates an adaptive weighted fusion mechanism with Wi-Fi fingerprinting. Experimental results demonstrate that this integrated system significantly improves the overall trajectory accuracy, providing a high-precision, attitude-unconstrained solution for real-time indoor pedestrian navigation. Full article

Anti-friction bearings are fundamental components of rotating machines. In bearing condition monitoring, fault detection is a primary task, as any undetected faults could result in catastrophic failures and downtime losses. To ensure effective and reliable fault detection, the use of appropriate sensors and measurement technologies is essential. This paper presents a comparative study on the applications of four sensor types in bearing condition monitoring. These four sensor types are vibration accelerometer, encoder, acoustic emission (AE) sensor and motor current probe. Their effectiveness and practicability in bearing fault detection are evaluted. Data simultaneously measured from these four sensor types on a split roller bearing within an experimental rig are used for the analysis. Different factors such as machine operating speeds, bearing fault sizes and their location are considered during the experiments to understand the effectiveness and fault detectability of different sensors on a common bearing. Both the accelerometer and the AE sensor are observed to effectively detect all bearing faults from small to extended sizes and from low to high operating speeds. However, the other two sensors, the encoder and motor current probe, have been found to be sensitive only to relatively larger fault sizes and higher operating speeds. The study presents valuable insights into their advantages and limitations through a systematic comparison of roller bearing fault detection. The study provides a basis for sensor selection in bearing condition monitoring and fault detection to enhance the reliability of industrial maintenance activities. Full article

The stable and accurate output of the inertial measurement unit array (IMU) of a micro-electro-mechanical system (MEMS) is the key to ensuring the data fusion of the MEMS IMU array. However, due to the large number of MEMS IMUs contained in the MEMS IMU array, it is susceptible to interference and has difficulty avoiding failures. The output of the MEMS IMU contains noise, outliers, and other related errors, which can seriously lead to low fault detection and isolation accuracy in the MEMS IMU. In this study, a new method of fault detection and isolation based on multivariate variational mode decomposition (MVMD), a whale optimization algorithm (WOA), and a generalized likelihood test (GLT) is proposed, which is called WOA-MVMD-GLT. Firstly, a multi-index fitness function WOA is proposed to optimize the parameters of MVMD. Secondly, MVMD is used to extract the features of the MEMS IMU’s signals. Finally, a GLT is used to construct a fault detection function and a fault isolation function to detect and isolate the faults of gyroscopes and accelerometers. The experimental results show that the method proposed in this paper can significantly reduce the false alarm rate and false isolation rate. Full article

Wearable fall detection systems face a fundamental challenge: while gyroscope data provide valuable orientation cues, naively combining raw gyroscope and accelerometer signals can degrade performance due to noise contamination. To overcome this challenge, we present a dual-stream transformer architecture that incorporates (i) Kalman-based sensor fusion to convert noisy gyroscope angular velocities into stable orientation estimates (roll, pitch, yaw), maintaining an internal state of body pose, and (ii) processing accelerometer and orientation streams in separate encoder pathways before fusion to prevent cross-modal interference. Our architecture further integrates Squeeze-and-Excitation channel attention and Temporal Attention Pooling to focus on fall-critical temporal patterns. Evaluated on the SmartFallMM dataset using 21-fold leave-one-subject-out cross-validation, the dual-stream Kalman transformer achieves 91.10% F1, outperforming single-stream Kalman transformers (89.80% F1) by 1.30% and single-stream baseline transformers (88.96% F1) by 2.14%. We further evaluate the model in real time using a watch-based SmartFall App on five participants, maintaining an average F1 score of 83% and an accuracy of 90%. These results indicate robust performance in both offline and real-world deployment settings, establishing a new state-of-the-art for inertial-measurement-unit-based fall detection on commodity smartwatch devices. Full article

Ensuring the crashworthiness of heavy-duty truck cabs is essential for reducing occupant fatalities and improving passive safety in commercial vehicles. Regulatory frameworks such as UNECE Regulation No. 29 (R29) define structural integrity requirements through full-scale destructive impact tests, which are costly and limit iterative design. In this study, an integrated experimental–numerical methodology is presented for the impact assessment of a real Iveco Eurocargo 120E18 truck cab reconstructed using high-resolution 3D scanning. The scanned geometry was used to generate a dimensionally accurate CAD model of the load-bearing cab structure, which was analysed using explicit finite element simulations in ANSYS Academic Mechanical and CFD Teaching package under impact conditions compliant with UNECE R29 and implemented according to the Swedish regulation VVFS 2003:29. In parallel, a full-scale physical pendulum impact test was performed on the same cab using a cylindrical impactor with a diameter of 580 mm, a length of 1800 mm, and a mass of approximately 1000 kg, impacting the upper region of the A-pillar. The experimental setup was instrumented using high-speed optical measurements and an accelerometer to capture impact kinematics and structural response. The numerical predictions showed good agreement with experimental results in terms of acceleration–time histories, absorbed energy evolution, and structural deformation, with differences generally below 6%. Critical regions susceptible to local buckling and plastic collapse were consistently identified in both approaches, while preservation of the driver survival space was confirmed. The results demonstrate that scan-based finite element models, when properly calibrated and validated, can reliably reproduce certification-level impact behaviour. The proposed workflow provides a robust and cost-effective framework for regulatory pre-validation, structural optimisation, and digitalisation of crashworthiness assessment for heavy-duty truck cabs. Full article

The European Union’s regulatory mandate requirements for vehicular components include the integrity of sealing performance, mitigating leaks from fuel tanks and transmission systems in order to guard against environmental pollution. Non-compliance can result in significant costs for the OEM and their supplier base. The majority of the reported research regarding leakage from radial lip seals focuses on static analysis of leakage under a given set of laboratory conditions. However, in practice, seal conjunctions are often subjected to significant excitations due to vehicular vibration. In the current study, the case of a front-wheel drive vehicle, equipped with three-axle accelerometers and subjected to a comprehensive road test, is used as the basis for the development of a realistic representative test rig. The test rig is developed using bespoke components from the vehicle under investigation to assess the impact of the encountered natural frequencies on sealing performance in controlled laboratory conditions, when the system is subjected to controlled excitation. Experiments are conducted to evaluate leakage at the transmission interface, focusing specifically on the sealing system’s performance. The influence of driveshaft manufacturing processes using corundum grinding and subsequent surface topography upon leakage performance are also considered. Identified modal response frequencies are imposed upon the test rig using a shaker, whilst the seal leakage is measured. The importance of shaft roughness characteristics, such as topographical skewness upon seal leakage rate under various resonant conditions, are ascertained. The results indicate potentially significant leakage rates under excitation conditions, with a non-optimised shaft roughness profile. Full article

Pedestrian inertial navigation is a pivotal technology for achieving seamless indoor and outdoor positioning. Traditional methods based on the Extended Kalman Filter (EKF) suffer from cumulative errors induced by inertial measurement unit (IMU) noise, which severely degrade the accuracy of pedestrian trajectory estimation over long durations. To address this critical limitation, a post-processing trajectory optimization approach for pedestrian inertial navigation based on the Birkhoff pseudospectral method is proposed in this paper. Leveraging the initial attitude and position estimates derived from the Zero-Velocity Update (ZUPT) technique and the EKF framework, the proposed method first parameterizes continuous-time acceleration measurements by adopting Chebyshev nodes as collocation points, and then formulates and solves the trajectory optimization problem via a Birkhoff pseudospectral framework, which effectively suppresses noise interference from the IMU accelerometer. Simulation experiments validate the superior noise suppression capability of the proposed algorithm. Furthermore, physical experiments conducted with a foot-mounted IMU demonstrate that the final position error is reduced by approximately 90% in comparison with the traditional EKF-based method. Full article

Background: The relationship of objectively measured levels of physical activity (PA) to quality of life (QoL) in young adults with intellectual disabilities (IDs) needs to be further researched. This study compares PA levels and self-perceived QoL in young adults with ID compared to those with no intellectual disability and examines whether higher levels of PA are related to better self-perceived QoL in the domains of physical, psychological, social and environmental well-being. Methods: A hundred young adults participated (GID: n = 50; GNID: n = 50). Demographic data were collected through questionnaires, and PA levels were measured using ActiGraph GT3X-BT accelerometers over a 7-day period. QoL was assessed using the World Health Organization Quality of Life short questionnaire (WHOQOL-BREF). An independent samples t-test was used to examine differences between groups (GID and GNID), and correlations between PA variables and QoL variables were calculated intra-group. The statistical significance was set at p ≤ 0.05. Results: The GID scored significantly lower in social (p = 0.001, d = 0.67), environmental (p = 0.007, d = 0.56) and total QoL (p = 0.015, d = 0.51) domains, and showed lower light PA (p = 0.042, d = 0.45). No significant PA–QoL correlations were found in the GID, while vigorous PA correlated positively with physical QoL in the GNID (rho = 0.35; p = 0.028). Conclusions: Self-perceived QoL values, as well as PA levels, are lower in young people with ID, with significant differences observed in the social and environmental domains, and in light PA. No associations were found between PA and QoL variables in the group of young people with IDs. Vigorous PA was significantly associated with the physical domain of QoL in the GNID. Full article

Rotational modulation improves strapdown inertial navigation system (SINS) by periodically reorienting the inertial measurement unit (IMU) to convert slowly varying sensor errors into manageable, cancelable components. However, existing dual-axis schemes may accumulate large total rotation angles and introduce delayed error balancing, which results in non-negligible residual attitude errors and degrades real-time navigation accuracy. To overcome these limitations, we propose an odd-symmetric dual-axis rotation strategy that jointly optimizes the rotation order and dwell positions to maximize error cancellation on each axis and across axes while constraining cumulative rotation. Based on this principle, we design a 64-position rotation scheme and derive its IMU error modulation/suppression characteristics, including gyroscope drift, accelerometer bias, scale-factor errors, and misalignment (installation) errors, and we quantify their effects on attitude and velocity. Simulations show that the proposed scheme reduces position and velocity errors by more than $60\%$ compared to a 16-position scheme, and decreases longitude error, east-velocity error, and yaw error by more than $30\%$ relative to a 32-position scheme. Experiments further validate consistent improvements in position, velocity, and attitude accuracy, demonstrating the effectiveness of the proposed rotational design for dual-axis SINS. Full article

When bridge towers are subjected to strong winds, they exhibit significant displacements. This displacement change can serve as an important indicator for structural performance warning. The displacement and acceleration collected in real time by the intelligent bridge monitoring system are disturbed by various noises, resulting in missed alarms in the monitoring system and causing huge economic losses. This study employs the fractional-order Butterworth lowpass filter method, eliminating the maximum value method, triple standard deviation method, etc. for preprocessing abnormal monitoring data characterized by missing values and outlier points. A fractional-order multi-rate Kalman fusion is proposed to process and model the correlation of structural displacement and acceleration data, and the simulated data and measured data are analyzed and verified respectively. Spectral analysis confirmed that by effectively fusing the low-frequency GPS signal with the high-frequency accelerometer signal, the fractional-order multi-rate Kalman fusion displacement measurement has a relatively high accuracy. Displacements obtained by the fractional-order multi-rate Kalman fusion method are adopted for correlation modeling, and residuals generated from this fractional-order fusion modeling are used for structural performance warning testing. The effectiveness of this structural performance warning is quantitatively validated through statistical assessment of warning accuracy. Full article