Search Results (3,922)

High-sensitivity accelerometers are essential for spacecraft micro-vibration monitoring. This study proposes a procedure to facilitate precise on-ground calibration of such accelerometers with a limited operational range by rotating to multiple positions with its input axis mounted along the horizontal tilt axis of a two-axis indexing device. Each single-axis accelerometer unit of a self-developed tri-axial nano-g accelerometer was respectively tested with its various reference axes along the rotation axis for identifying the parameters of their model equations including higher-order terms. The minute tilt axis deviation of the test equipment from the horizontal plane and the accelerometer’s higher-order response to gravity during calibration are corrected for application in the microgravity environment. Errors of accelerometer biases and scale factors are satisfactorily improved, respectively, to ±2% and ±0.01 mg, by at least one order of magnitude. Parameters of all three units of the accelerometer are unified into one coordinate frame defined by the accelerometer mounting surface. Acceleration measured by our accelerometer shows consistency with the other collocated one in a space mission. Full article

Background/Objectives: International physical activity (PA) questionnaires require a reliability and validity assessment in many countries to understand cross-cultural differences accurately. The current study examined the reliability and validity of the Japanese version of the WHO Health Behaviour in School-aged Children PA (HBSC-J) survey in adolescent students in Japan. Methods: The participants were 215 Japanese high school students. The HBSC-J was administered twice to measure reliability. The PA in the last week evaluated using the HBSC-J was compared with the PA evaluated using a triaxial accelerometer to measure the concurrent validity. Results: The intraclass correlation coefficients (ICCs) for reliability were 0.74 for the number of days, with 60 min/day or more of moderate-to-vigorous PA (MVPA). For the days with an MVPA of 60 min/day or more, ICCs were lower for girls (0.63 [0.49–0.74]) than boys (0.82 [0.75–0.87]). Positive correlations were observed between the accelerometry MVPA and the number of days, with at least 60 min/day of MVPA (r = 0.44). Conclusions: The HBSC-J questionnaire should be acceptable for evaluating MVPA in Japanese adolescents, with a reasonable reliability and validity. Full article

This study investigated differences in energy-expenditure (EE) modeling between badminton players of varying competitive levels during aerobic training. It evaluated the impact of sensor quantity and sample size on prediction model accuracy and generalizability, providing evidence for personalized training-load monitoring. Fifty badminton players (25 elite, 25 enthusiasts) performed treadmill running, cycling, rope skipping, and stair walking. Data were collected using accelerometers (waist, wrists, ankles), a heart rate monitor, and indirect calorimetry (criterion EE). Multiple machine learning models (Linear Regression, Bayesian Ridge Regression, Random Forest, Gradient Boosting) were employed to develop EE prediction models. Performance was assessed using R², mean absolute percentage error (MAPE), and root mean square error (RMSE), with further evaluation via the Triple-E framework (Effectiveness, Efficiency, Extension). Elite athletes demonstrated stable, coordinated movement patterns, achieving the best values for R² and the smallest errors using minimal core sensors (typically dominant side). Enthusiasts required multi-site sensors to compensate for greater execution variability. Increasing sensors beyond three yielded no performance gains; optimal configurations involved 2–3 core accelerometers combined with heart rate data. Expanding sample size significantly enhanced model stability and generalizability (e.g., running task R² increased from 0.49 (N = 20) to 0.95 (N = 40)). Triple-E evaluation indicated that strategic sensor minimization coupled with sufficient sample size maximized predictive performance while reducing computational cost and deployment burden. Competitive level significantly influences EE modeling requirements. Elite athletes are suited to a “low-sensor, small-sample” scenario, whereas enthusiasts necessitate a “multi-sensor, large-sample” strategy. Full article

Background/Objectives: Declining physical activity (PA) and cardiorespiratory fitness (CRF) in children are global public health concerns, particularly in populations experiencing urbanization and economic transition. This study investigated the effects of a school-based intervention on PA, CRF, and cardiovascular disease (CVD) risk factors in children aged 6–12 years from marginalized communities in Gqeberha, South Africa. Methods: A cluster randomized controlled trial was conducted in four schools, with participants randomly assigned to one of the following four arms: (i) PA and multi-micronutrient supplementation (MMNS); (ii) PA and placebo; (iii) MMNS; or (iv) placebo (control). A total of 1151 children were assessed at baseline (T1), 1003 at post-intervention (T2), and 549 at follow-up (T3). PA was measured using accelerometers. Secondary outcomes included CRF (20 m shuttle-run) and CVD risk factors (i.e., anthropometry, blood pressure, glycated hemoglobin [HbA1c], and lipid profile). Mixed linear models adjusted for baseline characteristics were used. Results: None of the interventions significantly improved daily PA. From T1 to T2, the MMNS arm significantly increased CRF, while PA + MMNS reduced HbA1c. However, MMNS alone increased triglycerides, and PA + placebo increased low-density lipoprotein (LDL). From post-intervention (T2) to follow-up (T3), the MMNS arms significantly reduced blood pressure. Yet, the PA + MMNS arm increased body fat percentage and decreased high-density lipoprotein (HDL). Conclusions: While MMNS showed promise for improving fitness and blood pressure and PA + MMNS reduced HbA1c, adverse metabolic changes emerged. The results should be interpreted with caution due to the short intervention span and COVID-19 disruptions during the second year of the intervention. Full article

Background: Physical activity, sedentary behaviour, and sleep are interdependent components of the 24 h movement profile that may influence appetite control. While acute exercise can alter appetite perceptions and food reward, less is known about how reallocating time between daily behaviours affects appetite outcomes under free-living conditions. Methods: We applied isotemporal-substitution modelling in a cross-sectional study of 130 young, healthy, active adults. Accelerometer-derived estimates of sedentary time, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sleep were analysed in relation to energy intake (food diaries, laboratory meals), subjective appetite perceptions, appetite-related hormones (acylated ghrelin, PYY, leptin), and psychological traits, including food reward (Leeds Food Preference Questionnaire, LFPQ), food cravings (Control of Eating Questionnaire, CoEQ), and eating behaviour traits (Three-Factor Eating Questionnaire, TFEQ). Results: Reallocating 30 min/day of sedentary time to MVPA was associated with higher energy intake in free-living (+113 kcal/day, 95% CI: 34–192) and laboratory settings (+120 kcal/day, 95% CI: 55–185), along with greater postprandial hunger and prospective food consumption, reduced fullness, elevated fasting acylated ghrelin, and lower postprandial PYY. No associations were observed for reallocations to LPA or sleep. Furthermore, sedentary time reallocations were unrelated to leptin or psychological eating traits assessed by the LFPQ, CoEQ, or TFEQ. Conclusions: In this population, reallocating sedentary time to MVPA was linked to physiological and behavioural compensation consistent with elevated energy demands, whereas reallocating to LPA or sleep showed no associations. Trait-level eating behaviours were unaffected, suggesting MVPA influences appetite primarily through acute physiological rather than enduring cognitive or hedonic pathways. Full article

Inertial Measurement Units (IMUs), which embed several sensors (accelerometers, gyroscopes, magnetometers) are employed by musicians and performers to control sound, music, or lighting on stage. In particular, wireless IMU systems in the performing arts require particular attention due to strict requirements regarding streaming sample rate, latency, power consumption, and programmability. This article presents a review of systems developed in this context at IRCAM as well as in other laboratories and companies, highlighting specificities in terms of sensing, communication, performance, digital processing, and usage. Although basic IMUs are now widely integrated into IoT systems and smartphones, the availability of complete commercial wireless systems that meet the constraints of the performing arts remains limited. For this reason, a review of systems used in performing Arts provides exemplary use cases that may also be relevant to other applications. Full article

Micro-electromechanical system (MEMS) sensors are widely used in various navigation applications because of their cost-effectiveness, low power consumption, and compact size. However, their performance is often degraded by temperature hysteresis, which arises from internal temperature gradients. This paper presents a calibration method that corrects temperature hysteresis without requiring any additional hardware or modifications to the existing MEMS sensor design. By analyzing the correlation between the external temperature change rate and hysteresis errors, a mathematical calibration model is derived. The method is experimentally validated on MEMS accelerometers, with results showing an up to 63% reduction in hysteresis errors. We further evaluate bias repeatability, scale factor repeatability, nonlinearity, and Allan variance to assess the broader impacts of the calibration. Although minor trade-offs in noise characteristics are observed, the overall hysteresis performance is substantially improved. The proposed approach offers a practical and efficient solution for enhancing MEMS sensor accuracy in dynamic thermal environments. Full article

An accelerometer mounting technique has large implications on the frequency range and accuracy of the measurement, with stiffness and the mass relative to the monitored structure as the primary concerns. The International Organization for Standardization (ISO) gives an extensive list in 5348:2021, detailing mounting methods, and provides recommendations for testing mounts that are not specifically defined. In the nuclear industry on the laboratory scale, there is a need for vibration measurements for predictive maintenance and process monitoring that are nondestructive and capable of working in high-temperature environments. Commercial adhesive products with easy application and removal were tested as nondestructive methods, while quick mounts to a commonly used aluminum frame were tested as nondestructive and have potential applicability in high-temperature environments. The sinusoidal excitation method was used, measuring frequencies from 50 Hz to 10 kHz in one-third octave band intervals, utilizing three accelerometers and comparing the results to those obtained with the stud-mounting method. Using the lowest ±3 dB threshold across each accelerometer, foam dots and poster strips were not successful, and foam tapes were accurate up to 2000 Hz, hose clamps and zip ties up to 800 Hz, and a custom 3D printed mount up to 1000 Hz. Knowing the limitations of each mounting technique allows for accurate measurements within the appropriate range. Full article

Background/Objectives: Tacrolimus is the most used immunosuppressive agent in solid organ transplantation due to its efficacy in preventing acute rejection, but it has a narrow therapeutic range, and overexposure often leads to toxicities, including neurological side effects like tremors. Tremor affects up to 54% of renal transplant patients under tacrolimus. Extended-release tacrolimus (LCPT) has demonstrated efficacy in reducing tremor severity, as evidenced by studies employing quality of life (QoL) questionnaires, the Fahn–Tolosa–Marin (FTM) scale, and Accelerometer devices. The objectives of this study were to evaluate the benefits of the conversion to LCPT formulation in kidney transplant recipients experiencing tremors on prolonged-release tacrolimus (PR-TAC) treatment and to validate the DyCare device, a wearable wireless sensor for tremors. Results: The DyCare device measured tremor frequencies of 8.74 ± 0.11 Hz and 1.36 ± 0.08° and 17.38 ± 1.16°, as root mean square (RMSx100 for accelerometer and Gyroscope, respectively) in PR-TAC patients. After switching ten patients to LCPT, tremor severity significantly decreased, as confirmed by DyCare and the QoL in the Essential Tremor Questionnaire (QUEST). Additionally, LCPT allowed a 34% reduction in tacrolimus dosage while maintaining therapeutic trough concentrations. Immunological and pharmacodynamic biomarkers (p-miR-210-3p, p-IL10, p-IL12p70, p-IFNγ uCXCL10, NFAT-regulated gene expression) confirmed stable immunosuppression post-conversion. Conclusions: The conversion to the LCPT formulation significantly reduced tremors in kidney transplant recipients without altering their immunological status, as confirmed through a panel of immunologic and pharmacodynamic biomarkers. The DyCare device enables a precise quantification of tremors in transplant recipients, allowing physicians to optimize treatment strategies. Full article

Fetal movement monitoring (FMM) is crucial for assessing fetal well-being, traditionally relying on clinical assessments or maternal perception, each with inherent limitations. This study presents a novel lightweight deep learning framework for real-time FMM on edge devices. Data were collected from 120 participants using a wearable device equipped with an inertial measurement unit, which captured both accelerometer and gyroscope data, coupled with a rigorous two-stage labeling protocol integrating maternal perception and ultrasound validation. We addressed class imbalance using virtual-rotation-based augmentation and adaptive clustering-based undersampling. The data were transformed into spectrograms using the Short-Time Fourier Transform, serving as input for deep learning models. To ensure model efficiency suitable for resource-constrained microcontrollers, we employed knowledge distillation, transferring knowledge from larger, high-performing teacher models to compact student architectures. Post-training integer quantization further optimized the models, reducing the memory footprint by 74.8%. The final optimized model achieved a sensitivity (SEN) of 90.05%, a precision (PRE) of 87.29%, and an F1-score (F1) of 88.64%. Practical energy assessments showed continuous operation capability for approximately 25 h on a single battery charge. Our approach offers a practical framework adaptable to other medical monitoring tasks on edge devices, paving the way for improved prenatal care, especially in resource-limited settings. Full article

This study aims to assess the effect of vegetation angle and density on hydrostatic pressure and acceleration of a downstream house model experimentally. The vegetation cylinders were positioned at angles 30°, 45°, 60° and 90° with respect to the flow and two densities of vegetation conditions, i.e., sparse (G/d = 2.13) and intermediate (G/d = 1.09), where G is the spacing between the model vegetation elements in the cross-stream di-rection and d is the vegetation diameter. The streamwise acceleration of the house model was measured by an X2-2 accelerometer that was located downstream from the vegetation patches. Results show that the perpendicular orientation of the vegetation patch (90°) most effectively reduces hydrodynamic loads, with intermediate density (I90) achieving the highest reductions, i.e., 22.1% for acceleration and 7.4% for pressure impacts. Even sparse vegetation (S90) provided substantial protection, reducing acceleration by 21.9% and pressure by 5.8%. These findings highlight the importance of integrating vegetation density and orientation into flood management designs to enhance both their performance and reliability under varying hydraulic conditions. Full article

The increasing concern for road safety has driven the development of advanced driver behavior analysis systems. This study presents a comprehensive review of various techniques to detect unsafe driving behaviors, with a particular emphasis on using smartphone sensors. By leveraging data from accelerometers, gyroscopes, and GPS, these methods allow for the detection of aggressive driving patterns, which may result from factors such as driver distraction or drowsiness. Modern sensor technology plays a crucial role in real-time monitoring and has significant potential to enhance vehicle safety systems. A Long Short-Term Memory (LSTM) network combined with a Conv1D layer was trained to analyze driving patterns using a sliding window technique. As technology continues evolving, its application in driver behavior analysis holds great promise for reducing traffic accidents and improving driving habits. Furthermore, the ability to gather and analyze large amounts of data from drivers in various conditions opens new opportunities for more personalized and adaptive safety solutions. This research offers insights into the future direction of driver monitoring systems and the growing impact of mobile and sensor-based solutions in transportation safety. Full article

Multisensory behavioral research is increasingly aiming to move beyond traditional laboratories and into real-world settings. Smartphones offer a promising platform for this purpose, but their use in psychophysical experiments requires rigorous validation of their ability to precisely present multisensory stimuli and record reaction times (RTs). To date, no study has systematically assessed the feasibility of conducting RT-based multisensory paradigms on smartphones. In this study, we developed a reproducible validation method to quantify smartphones’ temporal precision in synchronized auditory–tactile stimulus delivery and RT logging. Applying this method to five Android devices, we identified two with sufficient precision. We also introduced a technique to enhance RT measurement by combining touchscreen and accelerometer data, effectively doubling the measure resolution—from 8.33 ms (limited by a 120 Hz refresh rate) to 4 ms. Using a top-performing device identified through our validation, we conducted an audio–tactile RT experiment with 20 healthy participants. Looming sounds were presented through headphones during a tactile detection task. Results showed that looming sounds reduced tactile RTs by 20–25 ms compared to static sounds, replicating a well-established multisensory effect linked to peripersonal space. These findings present a robust method for validating smartphones for cognitive research and demonstrate that high-precision audio–tactile paradigms can be reliably implemented on mobile devices. This work lays the groundwork for rigorous, scalable, and ecologically valid multisensory behavioral studies in naturalistic environments, expanding participant reach and enhancing the relevance of multisensory research. Full article

Linear velocity is commonly used to estimate 1-repetition maximum (1RM) from a load–velocity profile (LVP), as well as prescribe training intensity. However, no study has assessed angular velocity, which may be more representative of joint motion. The purpose of this study was to compare the prediction of 1RM from linear velocity (1RM_linear) and angular velocity (1RM_angular) LVPs in men and women. Fourteen recreationally trained college-aged subjects (7 males, 7 females) completed 1RM testing on day 1, then a randomized submaximal (30–90% 1RM) squat protocol on day 2. Linear velocity was measured with a linear position transducer, while angular velocity was recorded using an accelerometer affixed to the thigh. 1RM_angular was not significantly different from actual 1RM (p = 0.951), with a trivial effect size (d = 0.02), and nearly perfect correlation with actual 1RM (r = 0.984). 1RM_linear had a near perfect correlation with actual 1RM (r = 0.991) but was significantly different than actual 1RM (p < 0.001) with a large effect size (d = 1.56). Additionally, 1RM_angular had a significantly (p = 0.020) lower absolute error (6.7 ± 5.3 kg) than 1RM_linear (12.9 ± 8.2 kg). Regardless of prediction method, males (12.9 ± 8.2 kg) had a greater absolute error in 1RM prediction than females (6.7 ± 5.2 kg). During submaximal loads, a significant load × gender interaction was observed for linear velocity (p < 0.001), with men showing faster velocities at 30% (p = 0.009) and 40% (p = 0.044) 1RM, with no significant interaction (p = 0.304) of main effect of gender (p = 0.116). Angular velocity may provide strength and conditioning coaches a more accurate 1RM prediction during submaximal sets of back squat than using linear velocity; however, neither meet all criteria to be considered highly valid. Lastly, the gender differences in linear velocity at submaximal exercises suggest gender-specific considerations in velocity-based training particularly at lighter loads. Full article

Early train detection is vital for ensuring the safety of railway personnel, particularly in remote locations where fixed signaling infrastructure is unavailable. Unlike many existing solutions that rely on high-power, high-cost sensors and compute platforms, this work presents a lightweight, low-cost, and portable framework designed to run entirely on resource-constrained microcontrollers with just kilobytes of Random Access Memory (RAM). The proposed system uses vibration data from low-cost accelerometers and employs a simple yet effective Linear Regression (LR) model for almost real-time prediction of train arrival times. To ensure feasibility on low-end hardware, a parallel-processing framework is introduced, enabling continuous data collection, Machine Learning (ML) inference, and wireless communication with strict timing and energy constraints. The decision-making process, including data preprocessing and ML prediction, completes in under 10 ms, and alerts are transmitted via LoRa, enabling kilometer-range communication. Field tests on active railway lines confirm that the system detects approaching trains 15 s in advance with no false negatives and a small number of explainable false positives. Power characterization demonstrates that the system can operate for more than 6 days on a 10 Ah battery, with potential for months of operation using wake-on-vibration modes. Full article