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Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein–Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky–Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein–Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs. [ABSTRACT FROM AUTHOR]

The intensity gradient is a new cutpoint-free metric that was developed to quantify physical activity (PA) measured using accelerometers. This metric was developed for use with the ENMO (Euclidean norm minus one) metric, derived from raw acceleration data, and has not been validated for use with count-based accelerometer data. In this study, we determined whether the intensity gradient could be reproduced using count-based accelerometer data. Twenty participants (aged 7–22 years) wore a GT1M, an ActiGraph (count-based), and a GT9X, ActiGraph (raw accelerations) accelerometer during both in-lab and at-home protocols. We found strong agreement between GT1M and GT9X counts during the combined in-lab activities (mean bias = 2 counts) and between minutes per day with different intensities of activity (e.g., sedentary, light, moderate, and vigorous) classified using cutpoints (mean bias < 5 min/d at all intensities). We generated bin sizes that could be used to generate IGs from the count data (mean bias = −0.15; 95% LOA [−0.65, 0.34]) compared with the original IG. Therefore, the intensity gradient could be used to analyze count data. The count-based intensity gradient metric will be valuable for re-analyzing historical datasets collected using older accelerometer models, such as the GT1M. [ABSTRACT FROM AUTHOR]

Inertial signals are the most widely used signals in human activity recognition (HAR) applications, and extensive research has been performed on developing HAR classifiers using accelerometer and gyroscope data. This study aimed to investigate the potential enhancement of HAR models through the fusion of biological signals with inertial signals. The classification of eight common low-, medium-, and high-intensity activities was assessed using machine learning (ML) algorithms, trained on accelerometer (ACC), blood volume pulse (BVP), and electrodermal activity (EDA) data obtained from a wrist-worn sensor. Two types of ML algorithms were employed: a random forest (RF) trained on features; and a pre-trained deep learning (DL) network (ResNet-18) trained on spectrogram images. Evaluation was conducted on both individual activities and more generalized activity groups, based on similar intensity. Results indicated that RF classifiers outperformed corresponding DL classifiers at both individual and grouped levels. However, the fusion of EDA and BVP signals with ACC data improved DL classifier performance compared to a baseline DL model with ACC-only data. The best performance was achieved by a classifier trained on a combination of ACC, EDA, and BVP images, yielding F1-scores of 69 and 87 for individual and grouped activity classifications, respectively. For DL models trained with additional biological signals, almost all individual activity classifications showed improvement (p-value < 0.05). In grouped activity classifications, DL model performance was enhanced for low- and medium-intensity activities. Exploring the classification of two specific activities, ascending/descending stairs and cycling, revealed significantly improved results using a DL model trained on combined ACC, BVP, and EDA spectrogram images (p-value < 0.05). [ABSTRACT FROM AUTHOR]

Background: Previous research indicates the start of primary school (4-5-year-old) as an essential period for the development of children's physical activity (PA) patterns, as from this point, the age-related decline of PA is most often observed. During this period, young children are exposed to a wider variety of environmental- and social contexts and therefore their PA is influenced by more diverse factors. However, in order to understand children's daily PA patterns and identify relevant opportunities for PA promotion, it is important to further unravel in which (social) contexts throughout the day, PA of young children takes place. Methods: We included a cross-national sample of 21 primary schools from the Startvaardig study. In total, 248 children provided valid accelerometer and global positioning (GPS) data. Geospatial analyses were conducted to quantify PA in (social) environments based on their school and home. Transport-related PA was evaluated using GPS speed-algorithms. PA was analysed at different environments, time-periods and for week- and weekend days separately. Results: Children accumulated an average of 60 min of moderate-to-vigorous PA (MVPA), both during week- and weekend days. Schools contributed to approximately half of daily MVPA during weekdays. During weekends, environments within 100 m from home were important, as well as locations outside the home-school neighbourhood. Pedestrian trips contributed to almost half of the daily MVPA. Conclusions: We identified several social contexts relevant for children's daily MVPA. Schools have the potential to significantly contribute to young children's PA patterns and are therefore encouraged to systematically evaluate and implement parts of the school-system that stimulate PA and potentially also learning processes. Pedestrian trips also have substantial contribution to daily MVPA of young children, which highlights the importance of daily active transport in school- and parental routines. [ABSTRACT FROM AUTHOR]

This study examined the criterion validity of an ecological momentary assessment (EMA)-reported physical activity and sedentary time compared with accelerometry in shift workers and non-shift workers. Australian workers (n = 102) received prompts through a mobile EMA app and wore the Actigraph accelerometer on the right hip for 7–10 days. Participants received five EMA prompts per day at 3-hour intervals on their mobile phones. EMA prompts sent to shift workers (SW-T) were tailored according to their work schedule. Non-shift workers (NSW-S) received prompts at standardised times. To assess criterion validity, the association of EMA-reported activities and the Actigraph accelerometer activity counts and number of steps were used. Participants were 36 ± 11 years and 58% were female. On occasions where participants reported physical activity, acceleration counts per minute (CPM) and steps were significantly higher (β = 1184 CPM, CI 95%: 1034, 1334; β = 20.9 steps, CI 95%: 18.2, 23.6) than each of the other EMA activities. Acceleration counts and steps were lower when sitting was reported than when no sitting was reported by EMA. Our study showed that EMA-reported physical activity and sedentary time was significantly associated with accelerometer-derived data. Therefore, EMA can be considered to assess shift workers' movement-related behaviours with accelerometers to provide rich contextual data. [ABSTRACT FROM AUTHOR]

Thermosphere mass density and crosswind can be derived from accelerometer and GNSS tracking data. However, present datasets are often provided without comprehensive uncertainty specifications. We present a newly developed method that propagates measurement noise and errors in the satellite specification, thermosphere models, and radiation flux data to density observations to quantify their uncertainty. We focus specifically on density observations derived only from GNSS tracking data, which are limited in resolution along the orbit due to unavoidable smoothing. While the method can be applied to simulated and real data, making it useful for existing datasets and mission design, we demonstrated it using data from the GRACE B satellite. First, we compare the aerodynamic acceleration derived separately from the accelerometer and GNSS tracking data, highlighting the role of two significant noise sources: noise due to the differentiation of the positions and noise from the evaluation of the gravity vector at a noisy position. Averaging substantially reduces the noise in the aerodynamic acceleration as long as the differentiation noise dominates, which is the case at frequencies higher than the orbital frequency. Below, gravity vector evaluation noise becomes the dominating noise source, and consequently, averaging over longer periods leads to only marginal uncertainty reduction. Further, we investigate the uncertainty in the radiation pressure acceleration and demonstrate that averaging over one orbit substantially reduces the uncertainty in the along-track radiation pressure acceleration. We show that the uncertainty of density observations derived from the accelerometer data is about 4% of the density for data from 2003 when the GRACE B satellite was at 490 km altitude during high solar activity. In 2008, solar activity was very low, and the altitude was still 476 km, resulting in an uncertainty of 5%–20% because GNSS tracking noise and radiation pressure modeling errors play a much larger role as the aerodynamic acceleration becomes smaller. In the case of density observations derived only from GNSS tracking data, the uncertainty is about 5% in 2003 and 20%–50% in 2008 when averaging over one-third orbit. In 2008, GNSS tracking noise explains nearly all uncertainty in the density observation. Averaging over one orbit reduces the uncertainty to 4% and 5% in 2003 and 2008, respectively. [ABSTRACT FROM AUTHOR]

Introduction: Age-related declines in physical functioning have significant implications for health in later life. Physical activity (PA) volume is associated with physical function, but the importance of the pattern in which PA is accumulated is unclear. This study investigates associations between accelerometer-determined daily PA patterns, including composition and temporal distribution (burstiness) of upright and stepping events, with physical function. Methods: Data was from participants who wore an activPAL3 accelerometer as part of The Maastricht Study. Exposures included a suite of metrics describing the composition and the temporal distribution (burstiness) of upright and sedentary behaviour. Physical function outcomes included the six-minute walk test (6MWT), timed chair-stand test (TCST), grip strength (GS), and SF-36 physical functioning sub-scale (SF-36pf). Multivariable linear regression models were used to assess associations, adjusting for covariates including overall PA volume (daily step count). Results: Participants(n = 6085) had 6 or 7 days of valid data. Upright and stepping event metrics were associated with physical function outcomes, even after adjusting PA volume. Higher sedentary burstiness was associated with better function (6MWT, TCST, and SF-36pf), as was duration and step volume of stepping events (6MWT, TCST, GS, and SF-36pf), step-weighted cadence (6MWT, TCST, and SF-36pf). Number of stepping events was associated with poorer function (6MWT, GS, and SF-36pf), as was upright event burstiness (SF-36pf). Associations varied according to sex. Conclusion: Our study reveals that diverse patterns of physical activity accumulation exhibit distinct associations with various measures of physical function, irrespective of the overall volume. Subsequent investigations should employ longitudinal and experimental studies to examine how changing patterns of physical activity may affect physical function, and other health outcomes. [ABSTRACT FROM AUTHOR]

Background: Accelerometers are commonly used for the assessment of PA; however, these devices have not been validated in people with dystonia who experience movement limitations. To properly understand movement behaviors and deliver accurate exercise prescription in this population, the validity of these devices must be tested. Objective: This study aimed to validate step count and postural transitions detected by the activPAL accelerometer (AP) against direct observation (DO) during two functional assessments: the 30-s sit-to-stand (30STS) and 6-min usual-pace walk tests. Methods: A total of 11 participants with cervical dystonia (CD) (male/female n = 5/6; mean age = 61 years; BMI = 24 kg/m²) performed the 6-min usual pace walking and 30STS while wearing the activPAL. A trained observer counted steps and observed the number of sit-to-stands. Results: The average step count detected with AP and DO was 651.8 (218-758) and 654.5 (287-798) respectively. The average transitions detected were 11 (4-16) and 12 (4-17) respectively. Both methods showed good agreement and there was a statistically significant and strong correlation between the two methods, i.e., transitions (r = 0.983, p = 0.0001), and step counts (r = 0.9841, p = 0.0001). Conclusion: There is a good agreement between activPAL and direct observation for step counts and transitions between sitting and standing in people living with CD. [ABSTRACT FROM AUTHOR]

Smart sensors have revolutionized the world with their capability of capturing physical parameters. The sensors provide vital data to the monitoring station in the applications of environment, disaster management, transportation, etc. A framework is built up with an Internet of Things-based approach that detects vehicle accident with the sensors and the information is stored and monitored on the Thingspeak cloud. The alert is raised whenever an accident occurs to the vehicle with the help of an accelerometer, ultrasonic sensor, and GPS. The message of the real-time coordinates is transmitted with the communication module. The monitoring station takes the necessary action by recognizing the accident occurred and providing the emergency services to the hotspot immediately. The integrated approach will be beneficial as it will detect any mishap with the vehicle and facilitate the emergency help to the passengers in the vehicle. The proposed framework will be beneficial for saving the human life in any smart city. [ABSTRACT FROM AUTHOR]

Purpose: This work aims to assess the association of sleep duration with cardiometabolic risk (adiposity, blood pressure, lipids, albuminuria and A1C) and to investigate lifestyle factors (physical activity, light exposure, caffeine consumption and sugar consumption) associated with sleep duration in children. Methods: A nationally representative sample of 3907 children ages 6–17 years enrolled in NHANES from 2011 to 2014 was included in this cross‐sectional study. Sleep duration was defined as the daily average time spent sleeping over 7 days as measured by a physical activity monitor (PAM). Participants without valid sleep data for ≥95% of the study were excluded. Regression models were adjusted for age, sex, race, body mass index (BMI) Z score, physical activity and light exposure. Results: In adjusted regression models, longer sleep duration was associated with lower systolic blood pressure index (β = −3.63 * 10−5, 95% CI −6.99 * 10−5, −2.78 * 10−6, p = 0.035) and BMI Z score (β = −0.001, 95% CI −0.001, 0.000, p = 0.002). In logistic regression models, longer sleep duration was associated with lower odds of obesity (OR = 0.998, 95% CI 0.997, 0.999, p < 0.001) and overweight status (OR = 0.998, 95% CI 0.997, 0.999, p = 0.004). Greater light exposure (β = 6.64 * 10−5, 95% CI 3.50 * 10−5, 9.69 * 10−5, p < 0.001) and physical activity (β = 0.005, 95% CI 0.004, 0.006, p < 0.001) were associated with longer sleep. Conclusion: Longer sleep duration was associated with lower blood pressure and adiposity measures in children. Improving sleep quality by increasing physical activity and light exposure in childhood may decrease the lifetime risk of cardiometabolic disease. [ABSTRACT FROM AUTHOR]

Present wearable sensors are able to measure the frequency of vital signs such as respiration rate and heartbeat rate, but unable to measure those quantitatively, e.g. respiratory volume and heartbeat strength. Meanwhile, airflow at mouth contains both the respiration and the heartbeat quantitative signals. In this study, we propose a calibration method for a wearable vital sensor attached on the chest using the airflow at the mouth for quantitative respiration monitoring. An artificial ventilator and an experimental animal were introduced as test benches prior to human clinical experiment. As a proof of concept, a micro‐electro mechanical systems (MEMS) airflow sensor and a wearable accelerometer were implemented to both the test benches. The velocity output of the piston motion measured by the wearable sensor conformed with the airflow rate output, and the obtained results indicated that the wearable sensor can monitor not only the frequency of vital signs but also quantitative magnitudes such as the respiratory volume. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper reports a MEMS accelerometer based on bulk SiC. The fabrication and packaging scheme were designed, an efficient signal processing circuit was built, and the output performance of the sensor was tested. The test results show that the resistivity of the piezoresistive material is 194.40 Ω μm, the sensor's output sensitivity is 0.21 mV/g, the linearity is 99.8%, and the resonance frequency is 1636 Hz. Finally, the accelerometer was applied to the two ground vibration tests of aero‐engine, and the vibration signal of the turbine components and the test bed was successfully extracted. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Automatic detection of foot lift is one of the most important events of Functional Electrical Stimulation (FES). The FES system is used for the correction of Foot Drop (FD). FD is a condition where a person is unable to lift their foot from the ground due to complications that may arise after a stroke or spinal cord injury. It is crucial to accurately detect the patient’s foot lift event when correcting FD through FES as the pulse should only be applied when the person lifts their foot. The FES system applies the electrical pulse based on the input of the foot-lift detection sensor. A conventional FES system employs a sensor that is affixed on the heel to detect the lifting of the foot, but the connecting cables make the patient uncomfortable. To address this problem, IMU (Inertial Measurement Unit)-based sensors have been used, but they have some disadvantages, such as false triggering, low accuracy, and calibration. In this paper, we have presented an algorithm for detecting foot-lift events with high accuracy using a single IMU sensor through the application of deep learning techniques. We have recorded data from 10 healthy people and 10 foot drop patients. We have implemented Artificial Neural Network (ANN), K-Nearest Neighbour (KNN), and Convolutional Neural Network (CNN) models on these data and compared the results of these three models. The proposed algorithm aims to improve the precision of stimulation in the FES system. [ABSTRACT FROM AUTHOR]

Objective: We assessed changes in social skill development, problem behaviors, and academic competencies with an acute implementation of an activity-zoned playground (AZP) during recess. Methods: Third- and fifth-grade students (N = 116) from 2 midwestern elementary institutions were selected to wear an accelerometer at recess and have their teacher complete a pre- and post-intervention assessment of the Social Skills Improvement System (SSIS). We measured physical activity (PA) using Actigraph-GT3X accelerometers for 4 continuous weeks: one week of baseline, 2 weeks of activity zone intervention, one week of post-intervention with activity zone removal. Results: The AZP intervention was effective at increasing MVPA with decreases in sedentary activity during recess for third-grade students whereas fifth-grade students showed no significant change in PA (ps < .001). Furthermore, third-grade children improved their social skill scores by 3% (p = .003), but no change in social skill behavior was noted for fifth-grade students (p = .73); we detected no differences in problem behaviors or academic competence (ps > .05). Conclusion: Activity-zoned playgrounds can improve PA engagement and can lead to improvements in social skill development without worsening problem behaviors, especially in younger students. [ABSTRACT FROM AUTHOR]

Commercially available wearable activity monitors can promote physical activity behaviour. Clinical trials typically quantify physical activity with research grade activity monitors prior to testing interventions utilising commercially available wearable activity monitors aimed at increasing step count. Therefore, it is important to test the agreement of these two types of activity monitors. Observational. Thirty adults (20–65 years, n = 19 females) were provided a Fitbit Charge 4©. To determine reliability using an intraclass correlation coefficient, two, one-minute bouts of treadmill walking were performed at a self-selected pace. Subsequently, participants wore both an ActiGraph wGT3X-BT and the Fitbit for seven days. To determine agreement, statistical equivalence and the mean absolute percentage error were calculated and represented graphically with a Bland–Altman plot. Ordinary least products regression was performed to identify fixed or proportional bias. The Fitbit showed 'good' step count reliability on the treadmill (intraclass correlation coefficient = 0.75, 95 % CI = 0.53–0.87, p < 0.001). In free-living however, it overestimated step count when compared to the ActiGraph wGT3X-BT (mean absolute percentage error = 26.02 % ± 14.63). Measurements did not fall within the ± 10 % equivalence region and proportional bias was apparent (slope 95 % CI = 1.09–1.35). The Fitbit Charge 4© is reliable when measuring step count on a treadmill. However, there is an overestimation of daily steps in free-living environments which may falsely indicate compliance with physical activity recommendations. [ABSTRACT FROM AUTHOR]

Objective: To assess the impact of the COVID-19 pandemic associated governmental restrictions on physical activity and sedentary behavior of school-going adolescents in India and its effect on nutrition and health status. Methods: This was a before-after natural experiment study that recorded paired data of 449 (206 boys) school-going adolescents. COVID-19 related governmental measures (March 24, 2020 till February 2021) were taken as the natural experiment. The change in proportion of adolescents who met the recommended amount of physical activity guidelines and change in sedentary and dietary behaviors and body mass index (BMI) were compared. Results: The proportion of adolescents performing adequate physical activity decreased from 33.9% to 30.7% (OR 1.2,95% CI 0.9,1.6) during the pandemic. Fruit intake increased by 8.1% during the pandemic while junk food intake decreased by 17% during the pandemic. Mean (SD) BMI z-scores increased from −0.7)1.4) to −0.5)1.3) (P < 0.001). Conclusion: While there was a small decrease in the proportion of physically active adolescents during the pandemic, a shift towards healthier dietary habits was seen. [ABSTRACT FROM AUTHOR]

A top-down design methodology and implementation of a time domain sensor is presented in this paper. The acceleration resolution of the time domain sensor is equal to the time-measurement accuracy divided by the sensor sensitivity. Combined with the sensitivity formula, the acceleration resolution is proportional to the vibration amplitude, the time-measurement accuracy, and the third power of the resonant frequency. According to the available time-measurement accuracy and the desired acceleration resolution, the parameters including the vibration amplitude and the resonant frequency were theoretically calculated. The geometrical configuration of the time domain sensor device was designed based on the calculated parameters. Then, the designed device was fabricated based on a standard silicon-on-insulator process and a matched interface circuit was developed for the fabricated device. Experimental results demonstrated that the design methodology is effective and feasible. Moreover, the implemented sensor works well. In addition, the acceleration resolution can be tuned by adjusting the time-measurement accuracy and the vibration amplitude. All the reported results of this work can be expanded to other time domain inertial sensors, e.g., a gyroscope or tilt sensor. [ABSTRACT FROM AUTHOR]

The issue of the transport of goods is well-known, yet, in practice, there are often cases of damaged shipments due to improper storage and inappropriately chosen transport technology. Many cases are due to ignorance of the basic characteristics of the cargo and, consequently, its transport characteristics. Vehicle dynamics is crucial to the design of proper cargo securing; therefore, this article provides the values of longitudinal and lateral acceleration of a 16.5 m semi-trailer vehicle combination for test routes of length of 10,827 km on highways and other roads in Slovakia, Austria, and Germany from the monitoring of goods. The horizontal acceleration of 0.2 g is considered as the minimum stability of the load unit that should withstand transport. A load unit with a stability from 0.2 g to 0.3 g could be considered as the weakest load unit. The test results show that even the weakest load units such as these can be damaged in transports, as semi-trailer vehicle combinations still reach longitudinal ax1000 and lateral ay1000 accelerations between 0.2 g and 0.3 g relatively frequently. Acceleration events higher than 0.3 g occur very rarely, at 1.4 event/1000 km for roads, but only 0.1 event/1000 km for highways from our test transports. We have demonstrated through our research that it is necessary for the load units to have a minimum stability of 0.2 g. We can conclude that load units with a stability of less than 0.2 g are completely unacceptable for transport without additional securing because we obtained 70.3 acceleration events per 1000 km in the interval from 0.1 g to 0.2 g on highways but 1148.1 events per 1000 km on other roads. There is a big difference between the number of acceleration events per 1000 km on roads and highways for all acceleration intervals, which means that there is a substantially lower probability of damaging the weak load units on highways than on other roads. [ABSTRACT FROM AUTHOR]

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Background: Sedentary behavior (SB) is a recognized risk factor for many chronic diseases. ActiGraph and activPAL are two commonly used wearable accelerometers in SB research. The former measures body movement and the latter measures body posture. The goal of the current study is to quantify the pattern and variation of movement (by ActiGraph activity counts) during activPAL-identified sitting events, and examine associations between patterns and health-related outcomes, such as systolic and diastolic blood pressure (SBP and DBP). Methods: The current study included 314 overweight postmenopausal women, who were instructed to wear an activPAL (at thigh) and ActiGraph (at waist) simultaneously for 24 hours a day for a week under free-living conditions. ActiGraph and activPAL data were processed to obtain minute-level time-series outputs. Multilevel functional principal component analysis (MFPCA) was applied to minute-level ActiGraph activity counts within activPAL-identified sitting bouts to investigate variation in movement while sitting across subjects and days. The multilevel approach accounted for the nesting of days within subjects. Results: At least 90% of the overall variation of activity counts was explained by two subject-level principal components (PC) and six day-level PCs, hence dramatically reducing the dimensions from the original minute-level scale. The first subject-level PC captured patterns of fluctuation in movement during sitting, whereas the second subject-level PC delineated variation in movement during different lengths of sitting bouts: shorter (< 30 minutes), medium (30 -39 minutes) or longer (> 39 minute). The first subject-level PC scores showed positive association with DBP (standardized β ^ : 2.041, standard error: 0.607, adjusted p = 0.007), which implied that lower activity counts (during sitting) were associated with higher DBP. Conclusion: In this work we implemented MFPCA to identify variation in movement patterns during sitting bouts, and showed that these patterns were associated with cardiovascular health. Unlike existing methods, MFPCA does not require pre-specified cut-points to define activity intensity, and thus offers a novel powerful statistical tool to elucidate variation in SB patterns and health. Trial registration: ClinicalTrials.gov NCT03473145; Registered 22 March 2018; https://clinicaltrials.gov/ct2/show/NCT03473145; International Registered Report Identifier (IRRID): DERR1-10.2196/28684 [ABSTRACT FROM AUTHOR]

Satellites in Earth's orbit are exposed to Earth radiation, consisting of reflected solar and emitted thermal radiation, thereby exerting a non‐conservative force that causes acceleration and affects the orbits. Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) mission aiming to retrieve the Earth's gravity potential is critically dependent on accounting for this force and all other non‐gravitational forces. There are both diurnal and seasonal variations in the Earth's radiation pressure, of which the seasonal variability can be represented by climatology. Nevertheless, the daily variations in the Earth's radiation pressure, due to the transient changes in the weather; for example, clouds and their properties, are not accounted for in the orbit perturbations studies. We show here that the top‐of‐atmosphere radiation fluxes computed with a numerical weather prediction (NWP) model explain most of the measured short‐term variations in the radial acceleration of the GRACE‐FO satellite. Our physics‐based modeling corrects a hitherto unexplained lack of power spectral density in the measured accelerations. For example, we can accurately model the accelerations associated with a tropical storm in the Indian Ocean in December 2020, which would not be possible when using climatological data. Our results demonstrate that using a global numerical weather prediction model significantly improves the simulation of non‐gravitational effects in the satellites' orbits. In the 7‐day data set, OpenIFS‐simulated acceleration exhibited higher accuracy than climatological‐data‐simulated acceleration (2.5 compared to 2.6 nms−2) and an improved precision (2.6 compared to 3.0 nms−2). This advancement contributes to a more precise orbit determination across various applications in Earth sciences. Plain Language Summary: Perturbing forces affect the satellites in Earth's orbit. A gravity field retrieval satellite mission is sensitive to the impacts that have a non‐gravitational origin, such as Earth radiation pressure. Thus, modeling this source of perturbation is crucial to have an accurate gravity field time series. In this article, we demonstrate the effectiveness of using a physics‐based numerical weather model to simulate Earth's radiation pressure. Our analysis supports the idea that this approach can improve the accuracy of non‐gravitational force estimation in various satellite missions. Key Points: We computed the top‐of‐atmosphere radiation fluxes from a weather prediction model named OpenIFSThese fluxes were used to quantify the measured short‐term variations in the radial acceleration of the Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO) satelliteWe proved that storms that OpenIFS can predict, exert high perturbation in GRACE‐FO orbit [ABSTRACT FROM AUTHOR]

Shoe-based wearable sensor systems are a growing research area in health monitoring, disease diagnosis, rehabilitation, and sports training. These systems—equipped with one or more sensors, either of the same or different types—capture information related to foot movement or pressure maps beneath the foot. This captured information offers an overview of the subject's overall movement, known as the human gait. Beyond sensing, these systems also provide a platform for hosting ambient energy harvesters. They hold the potential to harvest energy from foot movements and operate related low-power devices sustainably. This article proposes two types of strategies (Strategy 1 and Strategy 2) for an energy-autonomous shoe-based system. Strategy 1 uses an accelerometer as a sensor for gait acquisition, which reflects the classical choice. Strategy 2 uses a piezoelectric element for the same, which opens up a new perspective in its implementation. In both strategies, the piezoelectric elements are used to harvest energy from foot activities and operate the system. The article presents a fair comparison between both strategies in terms of power consumption, accuracy, and the extent to which piezoelectric energy harvesters can contribute to overall power management. Moreover, Strategy 2, which uses piezoelectric elements for simultaneous sensing and energy harvesting, is a power-optimized method for an energy-autonomous shoe system. [ABSTRACT FROM AUTHOR]

Background: Gait speed is often used to estimate the walking ability in daily life in people after stroke. While measuring gait with inertial measurement units (IMUs) during clinical assessment yields additional information, it remains unclear if this information can improve the estimation of the walking ability in daily life beyond gait speed. Objective: We evaluated the additive value of IMU-based gait features over a simple gait-speed measurement in the estimation of walking ability in people after stroke. Methods: Longitudinal data during clinical stroke rehabilitation were collected. The assessment consisted of two parts and was administered every three weeks. In the first part, participants walked for two minutes (2MWT) on a fourteen-meter path with three IMUs attached to low back and feet, from which multiple gait features, including gait speed, were calculated. The dimensionality of the corresponding gait features was reduced with a principal component analysis. In the second part, gait was measured for two consecutive days using one ankle-mounted IMU. Next, three measures of walking ability in daily life were calculated, including the number of steps per day, and the average and maximal gait speed. A gait-speed-only Linear Mixed Model was used to estimate the association between gait speed and each of the three measures of walking ability. Next, the principal components (PC), derived from the 2MWT, were added to the gait-speed-only model to evaluate if they were confounders or effect modifiers. Results: Eighty-one participants were measured during rehabilitation, resulting in 198 2MWTs and 135 corresponding walking-performance measurements. 106 Gait features were reduced to nine PCs with 85.1% explained variance. The linear mixed models demonstrated that gait speed was weakly associated with the average and maximum gait speed in daily life and moderately associated with the number of steps per day. The PCs did not considerably improve the outcomes in comparison to the gait speed only models. Conclusions: Gait in people after stroke assessed in a clinical setting with IMUs differs from their walking ability in daily life. More research is needed to determine whether these discrepancies also occur in non-laboratory settings, and to identify additional non-gait factors that influence walking ability in daily life. [ABSTRACT FROM AUTHOR]

The switched reluctance motor (SRM) is an electric motor that can function effectively in challenging operating conditions thanks to its sturdy construction and resilience to external factors. Despite somewhat weaker parameters in terms of energy and power density compared to other types of electric motors, the SRM is recommended for applications such as the military, mining, industry, and other locations where the reliability of vehicle drive is essential. Therefore, monitoring the motor's operating state and identifying the fault's condition while it is still in the beginning phase is crucial. The paper presents SRM diagnostic methods and the authors' research on the test stand. The examined faults were dynamic eccentricity and imbalance. Experiments were performed for various rotational speeds and loads. The analysis of the results consisted of the interpretation of the current and acceleration spectra acquired from proper sensors. The spectra bands are compared in terms of their amplitudes and frequency values. These results show the nonlinear characteristics of the motor's operation, and interpretation of these results allows for estimating the impact of a fault parameter on a motor's performance. [ABSTRACT FROM AUTHOR]

Greater impulse is a key performance indicator of success in short track speed skating. The main objective of this study was to develop a method to measure skating strokes using a single IMU. Eight elite or world-class speed skaters had one IMU placed against their skin on the lower back, and a camera setup was positioned to capture the test. A maximal speed trial was then executed by each participant, and the data were analysed to estimate agreement between the camera and IMU estimates of skate stroke events. Inter-evaluator reliability was assessed on a dataset of 22 athletes performing speed trials as well. The algorithm detected 100% of the strokes identified on the video capture system with a root mean square error of 0.06s. Bland-Altman analysis showed a bias of 0.03s between the two methods, which corresponds to the frame rate of the camera. The inter-evaluator reliability yielded an intra-class correlation of 1.00 (ICC3,1) from a dataset of 7089 strokes. This study provides an example of on-ice evaluation of speed skating strokes using a single IMU. This equipment is less expensive than that employed by previous authors and can be implemented in training situations with low invasiveness. [ABSTRACT FROM AUTHOR]

Cough is a common symptom of multiple respiratory diseases, such as asthma and chronic obstructive pulmonary disorder. Various research works targeted cough detection as a means for continuous monitoring of these respiratory health conditions. This has been mainly achieved using sophisticated machine learning or deep learning algorithms fed with audio recordings. In this work, we explore the use of an alternative detection method, since audio can generate privacy and security concerns related to the use of always-on microphones. This study proposes the use of a non-contact tri-axial accelerometer for motion detection to differentiate between cough and noncough events/movements. A total of 43 time-domain features were extracted from the acquired tri-axial accelerometry signals. These features were evaluated and ranked for their importance using six methods with adjustable conditions, resulting in a total of 11 feature rankings. The ranking methods included model-based feature importance algorithms, first principal component, leave-one-out, permutation, and recursive features elimination (RFE). The ranking results were further used in the feature selection of the top 10, 20, and 30 for use in cough detection. A total of 68 classification models using a simple logistic regression classifier are reported, using two approaches for data splitting: subject-record-split and leave-one-subject-out (LOSO). The best-performing model out of the 34 using subject-record-split obtained an accuracy of 92.20%, sensitivity of 90.87%, specificity of 93.52%, and F1 score of 92.09% using only 20 features selected by the RFE method. The bestperforming model out of the 34 using LOSO obtained an accuracy of 89.57%, sensitivity of 85.71%, specificity of 93.43%, and F1 score of 88.72% using only 10 features selected by the RFE method. These results demonstrate the ability for future implementation of a motion-based wearable cough detector. [ABSTRACT FROM AUTHOR]

Kumite is a karate sparring competition in which two players face off and perform offensive and defensive techniques. Depending on the players, there may be preliminary actions (hereinafter referred to as "pre-actions"), such as pulling the arms or legs, lowering the shoulders, etc., just before a technique is performed. Since the presence of a pre-action allows the opponent to know the timing of the technique, it is important to reduce pre-actions in order to improve the kumite. However, it is difficult for beginners and intermediate players to accurately identify their pre-actions and to improve them through practice. Therefore, this study aims to construct a practice support system that enables beginners and intermediate players to understand their pre-actions. In this paper, we focus on the forefist punch, one of kumite's punching techniques. We propose a method to estimate the presence or absence of a pre-action based on the similarity between the acceleration data of an arbitrary forefist punch and a previously prepared dataset consisting of acceleration data of the forefist punch without a pre-action. We found that the proposed method can estimate the presence or absence of a pre-action in an arbitrary forefist punch with an accuracy of 86%. We also developed KARATECH as a system to support the practice of reducing pre-actions using the proposed method. KARATECH shows the presence or absence of pre-actions through videos and graphs. The evaluation results confirmed that the group using KARATECH had a lower pre-action rate. [ABSTRACT FROM AUTHOR]

Hip-worn accelerometers are commonly used to assess habitual physical activity, but their accuracy in precisely measuring sedentary behavior (SB) is generally considered low. The angle for postural estimation (APE) method has shown promising accuracy in SB measurement. This method relies on the constant nature of Earth's gravity and the assumption that walking posture is typically upright. This study investigated how cardiorespiratory fitness (CRF) and body mass index (BMI) are related to APE output. A total of 3475 participants with adequate accelerometer wear time were categorized into three groups according to CRF or BMI. Participants in low CRF and high BMI groups spent more time in reclining and lying postures (APE ≥ 30°) and less time in sitting and standing postures (APE < 30°) than the other groups. Furthermore, the strongest partial Spearman correlation with CRF (r = 0.284) and BMI (r = −0.320) was observed for APE values typical for standing. The findings underscore the utility of the APE method in studying associations between SB and health outcomes. Importantly, this study emphasizes the necessity of reserving the term "sedentary behavior" for studies wherein the classification of SB is based on both intensity and posture. [ABSTRACT FROM AUTHOR]

This study purposed to identify the better accelerometer location for monitoring badminton sports training load by investigating the correlation between internal (HR, training impulse [TRIMP]) training load and external (acceleration, player load [PL]) training loads measured from 5 different body locations. Twelve college-level badminton athletes wore a HR transmitter belt and five accelerometers fixed on both hands, legs, and lower back while performing continuous three-minute training of four badminton skills including backhand serve (BS), net shot (NS), footwork training (FT), and jump smash (JS). Results showed PL at the five locations were significantly related to TRIMP (r = 0.570–0.843, p < 0.05), in which PL at lower back has highest Pearson (r = 0.843) and partial (r = 0.366) correlated to TRIMP (p < 0.05). Base on the stepwise multiple regression, PL at lower back and racket hand explained 88% of the variance of TRIMP (R2 = 0.879). In conclusion, lower back is an idea location for accelerometer to monitor overall external training load in badminton if considering one accelerometer location, while combining PL at the lower back and racket hand can predict 88% of the variation of the internal training load. [ABSTRACT FROM AUTHOR]

This study examined the concordance between accelerometry-measured and self-reported physical activity (PA) and sedentary time in adults with autism. Twenty-four participants wore an ActiGraph GT3X + accelerometer for seven consecutive days and completed the International Physical Activity Questionnaire-Short Form (IPAQ-SF) on the last day of their study participation. Bland-Altman plots assessed the magnitude of agreement between the two measures. Nearly 80% of the participants accumulated the recommended ≥ 150 min of moderate to vigorous PA (MVPA)/week, but were also sedentary for over nine hours/day according to accelerometry data. Findings showed that adults with autism tended to overreport MVPA (b = 1.606, p < 0.01) and underreport sedentary time (b = 1.161, p = 0.03) via the IPAQ-SF, as compared to objective measurements. [ABSTRACT FROM AUTHOR]

The "can do, do do" framework combines measures of poor and normal physical capacity (PC, measured by a 6 min walking test, can do/can't do) and physical activity (PA, measured by accelerometer, do do/don't do) into four domains and is able to categorize patient subgroups with distinct clinical characteristics, including fall and fracture risk factors. This study aims to explore the association between domain categorization and prospective fall, fracture, and mortality outcomes. This 6-year prospective study included patients visiting a Fracture Liaison Service with a recent fracture. Outcomes were first fall (at 3 years of follow-up, measured by fall diaries), first subsequent fracture, and mortality (at 6 years). Cumulative incidences of all three outcomes were calculated. The association between domain categorization and time to the three outcomes was assessed by uni- and multivariate Cox proportional hazard analysis with the "can do, do do" group as reference. The physical performance of 400 patients with a recent fracture was assessed (mean age: 64 years; 70.8% female), of whom 61.5%, 20.3%, and 4.9% sustained a first fall, sustained a subsequent fracture, or had died. Domain categorization using the "can do, do do" framework was not associated with time to first fall, subsequent fracture, or mortality in the multivariate Cox regression analysis for all groups. "Can't do, don't do" group: hazard ratio [HR] for first fall: 0.75 (95% confidence interval [CI]: 0.45–1.23), first fracture HR: 0.58 (95% CI: 0.24–1.41), and mortality HR: 1.19 (95% CI: 0.54–6.95). Categorizing patients into a two-dimensional framework seems inadequate to study complex, multifactorial outcomes. A personalized approach based on known fall and fracture risk factors might be preferable. [ABSTRACT FROM AUTHOR]

To improve the GNSS receiver's accuracy, continuity, and stability in measuring the height of agricultural implements, this study proposed a variable-parameter Kalman filter (VPKF) algorithm based on GNSS and accelerometer to estimate the height of the implements optimally. The VPKF was verified, and its accuracy was evaluated by parallel rail platform and field tests. From the parallel rail test results, when the GNSS receiver was in real-time kinematic (RTK) positioning and the time delay of differential correction data (TDDCD) was less than or equal to 4 s, the root mean square error (RMSE) of the VPKF estimation was 9.82 mm. The RMSE of the GNSS measurement was 18.85 mm. When the GNSS receiver lost differential correction data within 28 s, the absolute error of VPKF was less than 30 mm, and the RMSE was 16.93 mm. The field test results showed that when the GNSS receiver was in RTK positioning and the TDDCD was less than or equal to 4 s, the RMSE of VPKF estimation was 13.43 mm, and the GNSS measurement was 14.56 mm. When the GNSS receiver lost differential correction data within 28 s, the RMSE of the VPKF estimate was 15.22 mm. These results show that VPKF can optimally estimate implement height with better accuracy. Overall, the VPKF can obtain a more accurate, continuous, and stable height of the implement, and increase the application scenarios of the GNSS receiver to measure the implement height. [ABSTRACT FROM AUTHOR]

Evidence for the effectiveness of physical activity (PA) in the treatment of depression prevails for outpatients with mild and moderate symptom levels. For inpatient treatment of severe depression, evidence-based effectiveness exists only for structured and supervised group PA interventions. The Step Away from Depression (SAD) study investigated the effectiveness of an individual pedometer intervention (PI) combined with an activity diary added to inpatient treatment as usual (TAU). In this multicenter randomized controlled trial, 192 patients were randomized to TAU or TAU plus PI. The two primary outcomes at discharge were depression—blindly rated with the Montgomery–Åsberg Depression Rating Scale (MADRS)—and average number of daily steps measured by accelerometers. Secondary outcomes were self-rated depression and PA, anxiety, remission and response rates. Multivariate analysis of variance (MANOVA) revealed no significant difference between both groups for depression and daily steps. Mean MADRS scores at baseline were 29.5 (SD = 8.3) for PI + TAU and 28.8 (SD = 8.1) for TAU and 16.4 (SD = 10.3) and 17.2 (SD = 9.9) at discharge, respectively. Daily steps rose from 6285 (SD = 2321) for PI + TAU and 6182 (SD = 2290) for TAU to 7248 (SD = 2939) and 7325 (SD = 3357). No differences emerged between groups in secondary outcomes. For severely depressed inpatients, a PI without supervision or further psychological interventions is not effective. Monitoring, social reinforcement and motivational strategies should be incorporated in PA interventions for this population to reach effectiveness. [ABSTRACT FROM AUTHOR]

Objective This study aimed to investigate the intensity of the physical work of workers with typical tasks in railway tunnel construction in order to provide support for scientifically monitoring workers' health. Methods From July to August 2022, 24 workers with different tasks(3 electric welders, 7 erectors, 4 waterproofers, and 10 steel bar workers)were recruited in tunnel construction site. Their physical activities were recorded for 24-hours for 3 - 5 consecutive days using the energy consumption meter on the dominant wrist. The intensity of physical work was estimated based on counts per minute, energy consumption, and metabolic equivalent. One -way ANOVA tests were used to compare differences in the intensity of physical work among different workers. Results Finally, 23 workers completed the investigation. Except for the maximum counts, significant statistical differences were detected in total counts, average counts, and counts per minute among the waterproofers, steel bar workers, electric welders, and erecters (P < 0.05). Particularly, the activity meter value of waterproofing workers was the largest (P < 0.05). The energy expenditure per unit time for the four types of work was (1 303.0 ± 78.5), (760.8 ± 24.5), (554.8 ± 91.4) and (521.9 ± 30.4) kcals (1 kcal = 4.184 kJ), and the differences between the groups were statistically significant (F = 5.72, P = 0.006); the metabolic equivalents were (2.89 ± 0.22), (2.57 ± 0.39), (1.81 ± 0.07) and (1.70 ± 0.27) METs, respectively. The differences between the groups were statistically significant (F = 6.99, P = 0.002). Conclusions The intensity of physical work varied greatly among workers with different tasks in railway tunnel construction. The intensity of physical work of the waterproofers was equivalent to Level Ⅲ(heavy intensity), while Level Ⅱ (medium intensity)was for the other workers. The manager in tunnel construction should pay attention to the differences in intensity of physical work among workers with different tasks, reasonably set labor quotas, and allocate human resources for protecting workers' physical and mental health. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to establish three accelerometers and Flux-Gate magnetometer to estimate the affection level of Gümüşhane province from an earthquake occurred in the North Anatolian Fault Zone (NAFZ) zone and to observe the changes in earth magnetic field due to sun storms. It is considered that the accelerometers (3 units) must be installed in Karaca Cave which is an important place of Gümüşhane for its tourism; in hydroelectric station of Kürtün dam which is an important engineering structure in terms of energy production, agriculture, and water sports, and in Şiran which is a close to the NAFZ. Flux-gate magnetometer is built in Gümüşhane. The Torul and Kürtün towns are less affected whereas the Kelkit, Şiran and Köse towns are more affected from an earthquake which is occurred in the NAF zone. Karaca Cave, 17 km northwest of Gümüşhane, is one of the most important tourism in this region. The Kürtün Hydroelectric Power Plant, built on the Harşit River in Kürtün district, not only serves for energy purposes, but also provides the opportunity for fish production and water sports on the dam lake. Storms which are due to the sudden and severe explosions in the sun may cut the electricity and communications line, may cause the shortcomings in radio communications, and may cause radiation on humans. Gümüşhane has a great potential in terms of lead, zinc, copper, gold, and silver. Magnetic method, one of the Geophysical methods, has been successfully used to search such kind of mineral deposits. In this method, it will serve to continuously record the ground magnetic field while taking measurements in the field. [ABSTRACT FROM AUTHOR]

This article discusses a study that used multiple accelerometers to measure the 24-hour activity cycle (24-HAC) behaviors in university students. The study found that students spent the majority of their day in sedentary behavior or sleep, with less time spent in light physical activity and even less time in moderate-to-vigorous physical activity. Sleep patterns showed a clear nocturnal pattern, with a delay in sleep initiation and cessation on weekends. The study also developed a novel approach to harmonize data from multiple devices to accurately measure these behaviors. The findings highlight the importance of simultaneous measurement of these behaviors for a comprehensive understanding of their impact on health. [Extracted from the article]

Antipredator responses could affect nutrient intake, which could lead to nutritional deficits. However, little is known about the antipredator response of small herbivores because most are nocturnal or crepuscular and therefore very difficult to study in the field. Therefore, we experimentally assessed the effect of a reactive response to predation risk on the nutrient (i.e., phosphorous) intake of the European hare (Lepus europaeus) using three different playback sounds. Additionally, we studied the time spent being costly vigilant, the time spent foraging, and the vegetation height in which the hares were present using accelerometers and GPS. Our results showed that elevated predation risk from our playback experiment did not affect the (1) phosphorus intake, (2) time spent being costly vigilant, and (3) time spent in tall vegetation. However, elevated predation risk did increase the time spent foraging. Possibly hares spent more time foraging with an increased predation risk because hares cannot seek refuge from predators. Additionally, the effect on phosphorus intake could be weak because phosphorous intake does not benefit a flight escape, while the reactive response acts late in the predation sequence limiting the effect on hare ecology. Prey anti-predator responses seem strongly related to the escape tactics of prey species that can differ between different habitats and the time of the day. More detailed field studies are necessary to get a better insight into species' anti-predator-food tactics. [ABSTRACT FROM AUTHOR]

Introduction: Objective measurement of compliance is mandatory to evaluate therapeutic effects and clinical benefits of medical compression stockings. This Valitrack study evaluates the Accutrack sensor equipped with an accelerometer. The evaluation is done under controlled and free conditions and during washing. Results: Global accordance results are excellent: Sensitivity (98.6%) and specificity (95.2%) and 92% of patients are not disturbed by the sensors when putting on the stockings. Discussion: Thanks to its specificities one sensor per patient is sufficient. Its sensitivity distinguishes still and active positions. Only hand washing remains a false positive. The Accutrack sensor objectively measures compliance and will not be a bias because 96% of patients forget it. Conclusions: Objective compliance measurement with Accutrack allows us to evaluate the efficacity of compression, the reasons of non-compliance, and the patient's actual physical activity according to its accelerometer time-stamped. [ABSTRACT FROM AUTHOR]

Background Pain is associated with reports of restricted physical activity (PA), yet the association between musculoskeletal pain characteristics and objectively measured PA quantities and patterns in late life is not well understood. Methods A total of 553 adults (mean age 75.8 ± 8.4 years, 54.4% women) in the Baltimore Longitudinal Study of Aging (BLSA) completed a health interview and subsequent 7-day wrist-worn ActiGraph assessment in the free-living environment between 2015 and 2020. Pain characteristics, including pain presence in 6x sites (ie, shoulders, hands/wrists, low back, hip, knees, and feet), pain laterality in each site, and pain distribution were assessed. PA metrics were summarized into total daily activity counts (TAC), activity fragmentation, active minutes/day, and diurnal patterns of activity. Linear regression models and mixed-effects models examined the association between pain characteristics and PA outcomes, adjusted for demographics and comorbidities. Results Unilateral knee pain was associated with 184 070 fewer TAC (p  = .039) and 36.2 fewer active minutes/day (p  = .032) compared to those without knee pain. Older adults with shoulder pain or hand/wrist pain had more active minutes compared to those without pain (p  < .05 for all). For diurnal patterns of activity, participants with knee pain had fewer activity counts during the afternoon (12:00 pm to 5:59 pm). Analyses stratified by sex showed that these associations were only significant among women. Conclusions Our study highlights the importance of assessing pain laterality in addition to pain presence and suggests that pain interferes with multiple aspects of daily activity. Longitudinal studies are needed to assess the temporality of these findings. [ABSTRACT FROM AUTHOR]

The main aim of this study is to use comprehensive statistical analyses to evaluate measurement reliability of selected variables that characterize postural stability. The study examined twenty-nine healthy non-athlete students. The examinations were performed twice, with a one-week interval. The Microgate GYKO inertial sensor system was used to evaluate the reliability of variables that characterize postural stability. The relative reliability of the repeated test was evaluated using the intraclass correlation coefficient (ICC) with 95% confidence interval (95% CI). Next, the standard error of measurement (SEM) and minimal detectable change (MDC) were computed. Relative reliability of the repeated test for all analysed variables of ICC ranged from 0.31 to 0.75. For four variables, ICC values were ca. 0.7, i.e., they can be considered as good. For four other variables, ICC ranged from 0.41 to 0.54, with these values considered fair. Satisfactory reproducibility of postural stability measurements using the GYKO inertial sensor system demonstrates that it can offer an inexpensive and efficient alternative to measurements that use force balance platforms. [ABSTRACT FROM AUTHOR]

Key summary points: Aim: We investigated the effectiveness of a self-monitoring intervention to promote step count and reduce sedentary behavior in older people covered by the long-term care insurance system (LTCI) in Japan. Findings: Results from a randomized controlled trial of a self-monitoring intervention using accelerometers with a 5-week follow-up: improvement in a number of steps, light physical activity, and sedentary behavior in the intervention group compared to the control group. Message: Self-monitoring with an accelerometer may be effective in increasing the number of steps taken and amount of light physical activity per day and in reducing sedentary behavior in older people with LTCI. Purpose: This study aimed to investigate the effects of a self-monitoring intervention to promote an increase in physical activity, as measured by step count, and reduce sedentary behavior in older people covered by the long-term care insurance system (LTCI) in Japan. Methods: This was a randomized controlled trial conducted at a daycare center from October 2022 to January 2023. Fifty-two older adults with LTCI who were able to walk with or without aids were assigned to an intervention (n = 26) group and control (n = 26) group. During the 5-week follow-up period, the intervention group received education on physical activity and self-monitoring such as goal setting, self-management and feedback. The primary outcome was step count, and the secondary outcome was sedentary behavior. Results: Participants who completed the study to the end of the 5-week follow-up and drop-out participants for whom outcome data were available were included in the final analysis of 57 participants, n = 24 (79.8 ± 8.8 years, male 25.5%) in the intervention group and n = 23 (82.5 ± 8.5 years, male 39.1%) in the control group. Comparisons between the two groups at baseline showed no significant differences. In the results of a two-way mixed analysis of variance (ANOVA) including 2 (group: control, intervention) × 2 (term: baseline, 5-week follow-up) factors, an interaction was observed in the number of steps, sedentary behavior, and light physical activity (p < 0.05). Conclusion: Self-monitoring of physical activity using an accelerometer may be effective in increasing the number of steps and light physical activity and in reducing sedentary behavior in older people with LTCI. Clinical trial registration: UMIN000052044, registered on 2023/08/29. [ABSTRACT FROM AUTHOR]

Basketball is a high-risk team sport for lower extremity injuries, with ankle sprains being the most common injury. Non-elastic tape is widely used in injury prevention and quick return to play after ankle sprains, but its impact on stiffness, particularly global stiffness, has not been thoroughly investigated. The aim of this study was to investigate the effects of non-elastic ankle taping on vertical stiffness, among basketball players during the jump shot tasks; and to assess the reliability of accelerometers to evaluate vertical stiffness. Single group, repeated measures study. Thirty healthy semi-professional basketball players (15 males and 15 females) participated in the study. Vertical stiffness was compared among three conditions: 1) without taping, 2) while the non-elastic tape was applied to their ankles, and 3) after running while taped. Vertical stiffness was calculated from acceleration data using a mass-spring model. The result of a one-way repeated measures ANOVA showed that vertical stiffness was not significantly different between the three conditions (P = 0.162). Within-day and between-day reliability for average measurements were found to be high or very high. The findings showed that the vertical stiffness is unaffected by non-elastic taping. Therefore, while non-elastic tape can limit ankle range of motion, it may not have an impact on vertical stiffness, a global parameter which reflects the musculoskeletal performance. On the other hand, the high reliability of the stiffness variable supports the use of an accelerometer as a small portable instrument for outdoor sports measurements. [ABSTRACT FROM AUTHOR]

Many studies have highlighted the importance of physical activity in fostering health and preventing diseases among children and adolescents. Concurrently, mounting evidence underscores the detrimental health effects associated with high levels of sedentary behavior in children. Understanding the temporal changes in children's physical activity and sedentary behavior patterns is crucial for identifying evolving contexts that may require tailored interventions. This study investigates gender-specific changes in accelerometer-measured physical activity and sedentary behavior patterns among 8-11-year-old children from the same region of Greece, juxtaposing data from two different time frames (2014 and 2021). The cohort comprised 144 school-age children, with 68 participants from the 2014 cohort and 76 from the 2021 cohort. Physical activity and sedentary time were tracked using hip-worn accelerometers (Actigraph GT3X+). Participants were directed to wear the device continuously for seven consecutive days during their waking hours. Vector magnitude counts per minute and daily step counts were used to assess the total volume of physical activity, and Freedson population-specific cut-points for children were applied to classify physical activity levels. Factorial ANOVA models were conducted independently for each physical activity metric to explore discrepancies between the two measurement periods for each gender. The study's findings revealed a temporal trend indicating a decline in light physical activity alongside a significant increase in the percentage of total daily sedentary behavior. However, steps per day, counts per minute, and levels of moderate-to-vigorous physical activity remained relatively stable and did not significantly change. The average duration of sedentary bouts significantly increased only among girls. The results also indicated a slight increase from 2014 to 2021 regarding the proportion of children meeting physical activity recommendations. Modern children allocate less time to light physical activity and more time to sedentary pursuits, significantly increasing uninterrupted sedentary periods during the day. Our results underscore the need for tailored initiatives to promote and enable physical activity, particularly among girls. Addressing barriers and fostering inclusive opportunities is imperative to empower all children to adopt active and healthy lifestyles. [ABSTRACT FROM AUTHOR]

Aim: It is unclear whether intervention effects on school-aged children's physical activity differ across specific periods of the week or day. This study aimed to assess the time-specific intervention effects on accelerometer-measured physical activity in primary school children. Subject and methods: This was a nested study in a cluster-randomized controlled trial conducted from September 2018 to June 2019 in Beijing, China. The intervention group included four schools (119 children) and the control group included four schools (99 children). The obesity prevention intervention engaged schools and families to improve children's physical activity. Outcome measures included accelerometer-assessed intensity and amounts of physical activity within specific periods of a week (weekday/weekend day) or a day (in-school/out-of-school periods). Linear mixed models were used to estimate intervention effects. Results: The intervention led to an increase in time engaged in moderate-to-vigorous physical activity (MVPA) within in-school periods of a day (adjusted mean difference 0.54 minutes/hour; 95% confidence interval 0.13, 0.94, P = 0.012) but it did not improve physical activity within out-of-school periods (P > 0.05) compared with the control group. There was no evidence of difference across sex, body mass index, physical activity, and accelerometer compliance. No intervention effects were observed in physical activity within the whole weekday or weekend day (P > 0.05). Conclusions: The intervention effectively increased MVPA within in-school periods but did not improve out-of-school physical activity. Findings support the tailoring of intervention components to specific periods of a day to improve school-aged children's whole pattern of physical activity. [ABSTRACT FROM AUTHOR]

The article describes the on-board control-measurement system designed to record the main flight parameters of a micro-convertiplane type unmanned aerial vehicle and the element base used for this purpose. The methodology of conducting tests in stationary mode through the on-board control-measurement system was developed and tests were carried out. The engine's diagnostic check and condition assessment has been performed using the on-board control-measuring system. The possibility of on-ground check of the micro unmanned aerial vehicles power elements before and after the flight, as well as the possibility of monitoring the flight events that may occur in the air during the flight has been shown. The data on the power consumption and temperature of each engine in the stationary mode, the direction of the aircraft axes and the velocity vector have been recorded by mean of the developed on-board control-measurement system, as well as time dependence diagrams of the corresponding parameters have been built in comparative order based on the obtained values. A methodology has been developed for the diagnostics of the engine operation of the micro-unmanned aerial vehicle before and during the flight. It has been shown that the temperature, power consumption and number of revolutions of the engines included in the power system of the aircraft, as well as the aircraft axes and velocity vector direction data is recorded during the flight, by mean of the on-board control-measurement system designed to collect data on the flight-technical parameters of the convertiplane-type micro-unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

This paper addresses accelerometer array calibration, focusing on determining the errors between multiple sensors. Micro-electromechanical system (MEMS) based triaxial accelerometers, key components of Inertial Measurement Units (IMUs), are used in localization, robotics, and navigation systems. The requirements of these applications necessitate low-cost sensors, which makes MEMS IMUs a reasonable choice. However, these low-cost IMUs are significantly affected by systematic (i.e., bias, misalignment, scale-factor) and random errors. Achieving reliable sensor output depends on the precision of the executed calibration method. While traditional laboratory-based sensor calibration using specialized equipment (i.e., three-axis turntable) is accurate, it is time-consuming and costly. In contrast, in-field calibration techniques, which can be performed using a mechatronic actuator or a robotic arm, have gained popularity. These techniques involve comparing sensor measurements to established reference values. The MEMS sensors are increasingly being used in multi-sensor applications, which demands not only individual sensor error calibration but also important to determine the axis misalignment between the used sensors. During calibration process, various optimization algorithms (e.g., GA, PSO) can also be used to find the error parameters. The proposed measurement system allows for individual calibration of misalignment, bias, and scale factor of the sensor array, and eliminates between-sensor misalignment errors. [ABSTRACT FROM AUTHOR]

Background and objectivePhysical activity (PA) is paramount for childhood development and growth. However, children diagnosed with Cerebral Palsy (CP) were often considered sedentary, and their physical inactivity was associated with adverse health conditions and complications. Therefore, this study aimed to objectively describe and compare the PA levels and SB levels of children with and without CP of the same age group. It also studied the factors correlating with PA, SB, and step count per day in children with CP.Subjects and methodsA cross-sectional study using a wrist-worn accelerometer was conducted. PA and SB were measured over seven consecutive days.ResultsEighty-five children aged 6–12 years, consisting of 41 children with CP and 44 TD children, participated in this study with a mean age of 9.18 ± 1.95 and 8.45 ± 1.78 years, respectively. According to the gross functional measures, 53.6% of children with CP were classified as first classification. A significant amount of time was spent in SB and Light PA (LPA) by children with CP compared to TD children, and no significant differences were observed in moderate PA (MPA) or step count. Gender mainly affected MPA as girls spent more time in MPA than boys. The age, height, and weight of children with CP correlate significantly with SB. As children's age, height, and weight increase, SB increases. Additionally, children with higher weights have lower step counts per day.ConclusionThis study showed that children with CP spend more time in LPA and SB than typically developed children. Therefore, concerted efforts are needed to encourage physical activity and reduce the sedentary lifestyle, to take into account the gender and anthropometric measures of children to enhance the quality of life among children with CP, and to consider gender and anthropometric measures of the children.

Data from real systems is an important resource for research in machine diagnostics and prognostics. The demand for data has increased exponentially in recent years due to the growing interest in prognostics and the development of AI technologies for predictive maintenance. When used for fault detection and predictive maintenance, data must be able to provide information about the degradation phenomena that occur in machines. In addition, one goal of prognostics is to predict the remaining useful life (RUL), which requires a large amount of data to apply data-driven techniques or validate physics-based models. Bearings are subject to a wide range of loads and fatigue stresses, and their failure can be catastrophic for the entire machine or plant. The Department of Engineering of the University of Ferrara has carried out an extensive experimental campaign to record the evolution of vibration signals throughout the life of self-aligning double row rolling element bearings. Six accelerated run-to-failure tests were performed, while the acceleration signals were continuously recorded by a uniaxial accelerometer. A radial load was applied to the bearing housing and controlled by a load cell. The shaft speed was kept constant and controlled by an electric motor driven by an inverter. The data set provided contains acceleration signals in the radial direction for the entire duration of the tests and can be used for research or industrial purposes.

IntroductionFalls are a major cause of accidents that can lead to serious injuries, especially among geriatric populations worldwide. Ensuring constant supervision in hospitals or smart environments while maintaining comfort and privacy is practically impossible. Therefore, fall detection has become a significant area of research, particularly with the use of multimodal sensors. The lack of efficient techniques for automatic fall detection hampers the creation of effective preventative tools capable of identifying falls during physical exercise in long-term care environments. The primary goal of this article is to examine the benefits of using multimodal sensors to enhance the precision of fall detection systems.MethodsThe proposed paper combines time–frequency features of inertial sensors with skeleton-based modeling of depth sensors to extract features. These multimodal sensors are then integrated using a fusion technique. Optimization and a modified K-Ary classifier are subsequently applied to the resultant fused data.ResultsThe suggested model achieved an accuracy of 97.97% on the UP-Fall Detection dataset and 97.89% on the UR-Fall Detection dataset.DiscussionThis indicates that the proposed model outperforms state-of-the-art classification results. Additionally, the proposed model can be utilized as an IoT-based solution, effectively promoting the development of tools to prevent fall-related injuries.

Given the popularity of wrist-worn devices, particularly smartwatches, the identification of manual movement patterns has become of utmost interest within the research field of Human Activity Recognition (HAR) systems. In this context, by leveraging the numerous sensors natively embedded in smartwatches, the HAR functionalities that can be implemented in a watch via software and in a very cost-efficient way cover a wide variety of applications, ranging from fitness trackers to gesture detectors aimed at disabled individuals (e.g., for sending alarms), promoting behavioral activation or healthy lifestyle habits. In this regard, for the development of artificial intelligence algorithms capable of effectively discriminating these activities, it is of great importance to have repositories of movements that allow the scientific community to train, evaluate, and benchmark new proposals of movement detectors. The UMAHand dataset offers a collection of files containing the signals captured by a Shimmer 3 sensor node, which includes an accelerometer, a gyroscope, a magnetometer and a barometer, during the execution of different typical hand movements. For that purpose, the measurements from these four sensors, gathered at a sampling rate of 100 Hz, were taken from a group of 25 volunteers (16 females and 9 males), aged between 18 and 56, during the performance of 29 daily life activities involving hand mobility. Participants wore the sensor node on their dominant hand throughout the experiments.