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Accurate physical activity monitoring is essential to understand the impact of physical activity on one's physical health and overall well-being. However, advances in human activity recognition algorithms have been constrained by the limited availability of large labelled datasets. This study aims to leverage recent advances in self-supervised learning to exploit the large-scale UK Biobank accelerometer dataset—a 700,000 person-days unlabelled dataset—in order to build models with vastly improved generalisability and accuracy. Our resulting models consistently outperform strong baselines across eight benchmark datasets, with an F1 relative improvement of 2.5–130.9% (median 24.4%). More importantly, in contrast to previous reports, our results generalise across external datasets, cohorts, living environments, and sensor devices. Our open-sourced pre-trained models will be valuable in domains with limited labelled data or where good sampling coverage (across devices, populations, and activities) is hard to achieve. [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]

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process. [ABSTRACT FROM AUTHOR]

Currently, wearable technology is an emerging trend that offers remarkable access to our data through smart devices like smartphones, watches, fitness trackers and textiles. As such, wearable devices can enable health monitoring without disrupting our daily routines. In clinical settings, electrocardiograms (ECGs) and photoplethysmographies (PPGs) are used to monitor heart and respiratory behaviors. In more practical settings, accelerometers can be used to estimate the heart rate when they are attached to the chest. They can also help filter out some noise in ECG signals from movement. In this work, we compare the heart rate data extracted from the built-in accelerometer of a commercial smart pen equipped with sensors (STABILO's DigiPen) to standard ECG monitor readouts. We demonstrate that it is possible to accurately predict the heart rate from the smart pencil. The data collection is carried out with eight volunteers writing the alphabet continuously for five minutes. The signal is processed with a Butterworth filter to cut off noise. We achieve a mean-squared error (MSE) better than 6.685 × 10 − 3 comparing the DigiPen's computed Δ t (time between pulses) with the reference ECG data. The peaks' timestamps for both signals all maintain a correlation higher than 0.99. All computed heart rates (HR = 60 Δ t ) from the pen accurately correlate with the reference ECG signals. [ABSTRACT FROM AUTHOR]

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter. [ABSTRACT FROM AUTHOR]

Sleep hygiene behaviours are recommendations given to both clinical and non-clinical populations with a focus on modifying behaviours to maximise sleep outcomes. However, methodological issues present in sleep hygiene research make it difficult to conclusively determine the impact of each behaviour. This study aimed to address these issues by adopting a two-week, repeated measures design which incorporated objective sleep measures and used linear mixed effect modelling to assess the daily association of a wide range of sleep hygiene behaviours on sleep in a non-clinical, university sample. Between-persons effects revealed that bedtime and frequency of daytime napping, alcohol use, and social media use were negatively related to sleep duration while waketime and frequency of too much water consumption were positively related to sleep duration. Within-person effects revealed that later than usual bedtime, earlier than usual waketime, no sunlight exposure, poor ventilation, having an unpleasant conversation before bed were negatively associated with sleep duration whereas using alcohol to deliberately help full asleep was positively related to sleep duration. In contrast, disproportionately more behaviours were not significantly related to either sleep outcome, only some of which could be explained by individual differences, which suggests that more research is needed to determine the conditions under which these behaviours affect sleep, if at all. [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]

The article addresses some of the novel technologies being increasingly utilised within sports biomechanics and highlights key factors to be considered in best practice. Topics include the benefits of these technologies, categories of measurements that can be obtained with inertial measurement units (IMUs,) and considerations for fixation selecting and using IMUs.

The aim was to compare the differences in kinematics of elbow and wrist and shock transmission of the upper extremity in wheelchair tennis players and able-bodied players (in standing and sitting position) during backhand strokes. Fifteen wheelchair tennis and 15 able-bodied tennis players enrolled. Electromagnetic system and trial-axial accelerometers were used to measure the difference in the kinematic parameters of the upper extremity and the impact vibration transferred across the wrist joint. The results indicated that wheelchair players demonstrated unique elbow and wrist kinematics, especially shorter total swing time, greater elbow flexion at preparation, lower wrist extension acceleration before impact, and smaller racket vibration at impact. Comparing to able-bodied players in standing, wheelchair players and players in sitting demonstrated significantly greater elbow joint flexion/extension angle, angular velocity, angular acceleration during extension, and wrist joint flexion angle. Wheelchair players also differ significantly with the players in sitting regarding elbow joint angular velocity and acceleration, and wrist joint flexion velocity. These adaptations and adjustments can be attributed to the missing lower extremity function and deficient trunk kinetic chain. The differences between wheelchair tennis players and able-bodied players in sitting could represent the progress as the wheelchair players moving from novices to experts. [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 use of assistive devices and the presence of caregivers in the living space of older people using daycare rehabilitation facilities may increase their physical activity. However, the exact relationship between these factors and physical activity has not been well established. We aimed to examine whether assistive devices and caregivers were life-space factors associated with light-intensity physical activity (LPA) and moderate to vigorous-intensity physical activity (MVPA) among daycare rehabilitation facility users. Forty-seven daycare rehabilitation facility users were recruited (mean age: 76.5 ± 8.6 years; men: 16; women: 31). LPA and MVPA were measured using accelerometers and averaged over 7 days. Life-space assessment (LSA) and the Geriatric Depression Scale score were assessed. The LSA includes composite life-space, maximal life-space (LS-M), life-space using equipment (LS-E), and independent life-space. Grip strength and maximum gait speed were measured two times, and the best results were used. Multiple regression analysis showed that LPA was significantly associated with sex (β = 0.773), grip strength (β = 0.434), and LS-M (β = 0.325), whereas MVPA was significantly associated with age (β = −0.421) and LS-E (β = 0.455). A close association was found between LS-M and LS-E and the intensity of physical activity in older people who used daycare rehabilitation facilities. [ABSTRACT FROM AUTHOR]

There is a well‐established relationship between physical activity (PA) and physical fitness in children, being the latter an important marker for present and future health; however, there is still insufficient knowledge for the transition from the preschool age to early childhood. Therefore, this study in Swedish children aimed to investigate the estimated effect of meeting the aerobic component of the PA guidelines at 4 and/or 9 years of age on physical fitness measured at 9 years of age. PA was assessed using a wrist‐worn ActiGraph accelerometer and identical data processing in 217 healthy children in Sweden (114 boys and 103 girls). Physical fitness test included cardiorespiratory (20 m shuttle run test), motor (4 × 10 m shuttle run), and muscular fitness (hand grip strength and long jump). A linear mixed model was run, investigating the interaction between meeting the PA guidelines and time (either 4 or 9 years of age) and each fitness component (at 4 and 9). Interactions by sex were also checked. Meeting the PA guidelines consistently (at 4 and 9 years) was significantly associated to better performance in physical fitness parameters for motor fitness (−0.76 s, p < 0.001) and lower body muscular fitness (+4.6 cm; p < 0.001) at 9 years. There was an interaction between meeting the PA guidelines and time point, for cardiorespiratory fitness (+4.58 laps; p < 0.001). This study shows that meeting the PA guidelines at 4 and 9 years of age is associated to higher physical fitness at 9 years of age. [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]

This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst wearing GENEActivs (both wrists) and ActiGraphs GT9X (both wrists and waist). Three supervised learning classifiers (K-Nearest Neighbour, Random Forest and eXtreme Gradient Boosted Decision Tree) were used to identify the input signal pattern for each PA type and intensity, with a 10-fold cross-validation utilized to assess the performance of the classifiers. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity PA activities. All other models, for activity type and intensities, exceeded 97% accuracy, with a sensitivity and specificity of greater than 95%, irrespective of accelerometer brand, placement or health condition. [ABSTRACT FROM AUTHOR]

The reduction of non‐gravitational accelerations through an onboard accelerometer is a vital part of accurately estimating gravity fields for Gravity Recovery and Climate Experiment (GRACE) Follow‐On (GRACE‐FO). Current accelerometer quality, specifically a defect on the GRACE‐D accelerometer, necessitates the use of extensive calibration techniques to optimally utilize the accelerometer measurements. This paper describes these techniques and correction algorithms as they are used for the official GRACE‐FO accelerometer products, which rest on the information present in the partially functional GRACE‐D accelerometer. First, transplanted data from GRACE‐C is used to remove common effects between the spacecraft. Then, GRACE‐D data is utilized to compute a correction to the transplanted data to account for solar radiation pressure (SRP) and albedo using a parity argument and to account for non‐common drag and thruster leaks in the attitude control system. The SRP and albedo term predominantly improves C3,0 while the drag and leaky thruster term improves the gravity field across the entire spectrum. Finally, some comments on alternative data usage, initial modifications to the algorithms to account for other spacecraft operational modes, and future outlook are provided. Key Points: GRACE‐D accelerometer data, while noisy, is calibrated to recover useable signals and optimize science dataUpdated accelerometer calibrations improve gravity field recovery [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]

Introduction: Frailty is conventionally diagnosed using clinical tests and self-reported assessments. However, digital health technologies (DHTs), such as wearable accelerometers, can capture physical activity and gait during daily life, enabling more objective assessments. In this study, we assess the feasibility of deploying DHTs in community-dwelling older individuals, and investigate the relationship between digital measurements of physical activity and gait in naturalistic environments and participants' frailty status, as measured by conventional assessments. Methods: Fried Frailty Score (FFS) was used to classify fifty healthy individuals as non-frail (FFS = 0, n/female = 21/11, mean ± SD age: 71.10 ± 3.59 years), pre-frail (FFS = 1–2, n/female = 23/9, age: 73.74 ± 5.52 years), or frail (FFS = 3+, n/female = 6/6, age: 70.70 ± 6.53 years). Participants wore wrist-worn and lumbar-worn GENEActiv accelerometers (Activinsights Ltd., Kimbolton, UK) during three in-laboratory visits, and at-home for 2 weeks, to measure physical activity and gait. After this period, they completed a comfort and usability questionnaire. Compliant days at-home were defined as follows: those with ≥18 h of wear time, for the wrist-worn accelerometer, and those with ≥1 detected walking bout, for the lumbar-worn accelerometer. For each at-home measurement, a group analysis was performed using a linear regression model followed by ANOVA, to investigate the effect of frailty on physical activity and gait. Correlation between at-home digital measurements and conventional in-laboratory assessments was also investigated. Results: Participants were highly compliant in wearing the accelerometers, as 94% indicated willingness to wear the wrist device, and 66% the lumbar device, for at least 1 week. Time spent in sedentary activity and time spent in moderate activity as measured from the wrist device, as well as average gait speed and its 95th percentile from the lumbar device were significantly different between frailty groups. Moderate correlations between digital measurements and self-reported physical activity were found. Conclusions: This work highlights the feasibility of deploying DHTs in studies involving older individuals. The potential of digital measurements in distinguishing frailty phenotypes, while unobtrusively collecting unbiased data, thus minimizing participants' travels to sites, will be further assessed in a follow-up study. [ABSTRACT FROM AUTHOR]

Quantitative measurement of physical activity may complement neurological evaluation and provide valuable information on patients' daily life. We evaluated longitudinal changes of physical activity in patients with Friedreich ataxia (FRDA) using remote monitoring with wearable sensors. We performed an observational study in 26 adult patients with FRDA and 13 age-sex matched healthy controls (CTR). Participants were asked to wear two wearable sensors, at non-dominant wrist and at waist, for 7 days during waking hours. Evaluations were performed at baseline and at 1-year follow-up. We analysed the percentage of time spent in sedentary or physical activities, the Vector Magnitude on the 3 axes (VM3), and average number of steps/min. Study participants were also evaluated with ataxia clinical scales and functional tests for upper limbs dexterity and walking capability. Baseline data showed that patients had an overall reduced level of physical activity as compared to CTR. Accelerometer-based measures were highly correlated with clinical scales and disease duration in FRDA. Significantly changes from baseline to l-year follow-up were observed in patients for the following measures: (i) VM3; (ii) percentage of sedentary and light activity, and (iii) percentage of Moderate-Vigorous Physical Activity (MVPA). Reduction in physical activity corresponded to worsening in gait score of the Scale for Assessment and Rating of Ataxia. Real-life activity monitoring is feasible and well tolerated by patients. Accelerometer-based measures can quantify disease progression in FRDA over 1 year, providing objective information about patient's motor activities and supporting the usefulness of these data as complementary outcome measure in interventional trials. [ABSTRACT FROM AUTHOR]

Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes, and a lack of lifetime reserve has made continuous fatigue life monitoring become an operational concern. In this contribution, we discuss a data‐driven methodology for farm‐wide tower‐transition piece fatigue load estimation. We specifically debate the employment of this methodology in a real‐world farm‐wide setting and the implications of continuous monitoring. With reliable nacelle‐installed accelerometer data at all locations, along with the customary 10‐min supervisory control and data acquisition (SCADA) statistics and three strain gauge‐instrumented 'fleet‐leaders', we discuss the value of two distinct approaches: use of either fleet‐leader or population‐based data for training a physics‐guided neural network model with a built‐in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data are missing, then its fatigue loads should be modeled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of 9 months, with monthly accumulated errors always kept below ±5%. A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of 10‐min statistics, but mitigation strategies have been identified. Finally, the farm‐wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous data‐driven, population‐based approach here presented can serve as a springboard for further lifetime‐based decision‐making. [ABSTRACT FROM AUTHOR]

While the availability of low-cost micro electro-mechanical systems (MEMS) accelerometers, gyroscopes, and magnetometers initially seemed to promise the possibility of using them to easily track the position and orientation of virtually any object that they could be attached to, this promise has not yet been fulfilled. Navigation-grade accelerometers and gyroscopes have long been the basis for tracking ships and aircraft, but the signals from low-cost MEMS accelerometers and gyroscopes are still orders of magnitude poorer in quality (e.g., bias stability). Therefore, the applications of MEMS inertial measurement units (IMUs), containing tri-axial accelerometers and gyroscopes, are currently not as extensive as they were expected to be. Even the addition of MEMS tri-axial magnetometers, to conform magnetic, angular rate, and gravity (MARG) sensor modules, has not fully overcome the challenges involved in using these modules for long-term orientation estimation, which would be of great benefit for the tracking of human–computer hand-held controllers or tracking of Internet-Of-Things (IoT) devices. Here, we present an algorithm, GMVDμK (or simply GMVDK), that aims at taking full advantage of all the signals available from a MARG module to robustly estimate its orientation, while preventing damaging overcorrections, within the context of a human–computer interaction application. Through experimental comparison, we show that GMVDK is more robust to magnetic disturbances than three other MARG orientation estimation algorithms in representative trials. [ABSTRACT FROM AUTHOR]

MEMS accelerometers are significantly impacted by temperature and noise, leading to a considerable compromise in their accuracy. In response to this challenge, we propose a parallel denoising and temperature compensation fusion algorithm for MEMS accelerometers based on RLMD-SE-TFPF and GRU-attention. Firstly, we utilize robust local mean decomposition (RLMD) to decompose the output signal of the accelerometer into a series of product function (PF) signals and a residual signal. Secondly, we employ sample entropy (SE) to classify the decomposed signals, categorizing them into noise segments, mixed segments, and temperature drift segments. Next, we utilize the time-frequency peak filtering (TFPF) algorithm with varying window lengths to separately denoise the noise and mixed signal segments, enabling subsequent signal reconstruction and training. Considering the strong inertia of the temperature signal, we innovatively introduce the accelerometer's output time series as the model input when training the temperature compensation model. We incorporate gated recurrent unit (GRU) and attention modules, proposing a novel GRU-MLP-attention model (GMAN) architecture. Simulation experiments demonstrate the effectiveness of our proposed fusion algorithm. After processing the accelerometer output signal through the RLMD-SE-TFPF denoising algorithm and the GMAN temperature drift compensation model, the acceleration random walk is reduced by 96.11 % , with values of 0.23032 g/h / Hz for the original accelerometer output signal and 0.00895695 g/h / Hz for the processed signal. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of the designed MEMS dual axis accelerometer is to act as a seismic sensor by measuring low-level vibration at low frequencies, usually including those below 1 Hz. Piezoelectric materials have demonstrated broad applicability in numerous devices and electronic applications. Since piezoelectric energy harvesting devices create their highest power at the resonance frequency, there is a strong relationship between frequency and power density, where ω is the fundamental vibration mode frequency, the potential output power is proportional to 1/ω. Methods: A dual-axis MEMS piezoelectric accelerometer for low-frequency seismic sensing was modelled and simulated with different suspension systems. For a bandwidth range of 1 KHz, COMSOL Multiphysics has been used to simulate the accelerometer supported by a U beam, Parallel beam, folded beam, and Swastik structures. The dual detection of acceleration in the X–Y direction and the ability to produce voltage was detected in all four variations of MEMS accelerometers. Results: The dual detection of acceleration in the X–Y direction and the ability to produce voltage was detected in all four variations of MEMS accelerometers. Based on the investigation, it has been found that the Swastik design produces the highest stress overall, with a value of 0.4 × 105 N/m2 and has a maximum sensitivity of 0.8 × 10-3 Volts/g, making it more suited for seismic sensing applications. Conclusion: The proposed dual-axis MEMS piezoelectric accelerometer with the Swastik suspension system can be used as a seismic sensor to detect low-frequency seismic signals, particularly P-waves. The findings of this paper can be applied in the development of new and improved MEMS accelerometers for various applications, such as vibration sensing, structural health monitoring, and seismic sensing. [ABSTRACT FROM AUTHOR]

Increased physical activity may prevent disease onset and severity in individuals with cardiovascular disease. However, studies evaluating physical activity in people with cardiovascular disease are limited. This prospective observational study aimed to objectively assess the level of physical activity in patients with cardiovascular disease and determine the actual extent of physical activity in their daily lives. Participants aged 20 years or older with cardiovascular disease at a cardiology clinic were included. Physical activity was measured using an activity meter with a three-axis acceleration sensor. Overall, 58 patients were included in the study. Household activities were found to be more frequent sources of physical activity. The step count was related to age and housework, while total physical activity and household activity were related to age and work. Locomotive activity was related to sex and housework. Total physical and household activities tended to decrease with age. These findings indicate the influence of work and household chores on physical activity and suggest that physical activity may be underestimated if household activity is not also assessed. These fundamental findings may provide clinical evidence to underpin physical activity for patients with cardiovascular disease. [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]

BACKGROUND: Offshore wind technicians (WTs) have been identified as having an occupation with high physical demands. OBJECTIVE: To characterise the physical demands of WTs, during the working day and when performing casualty rescues. METHODS: Data collection consisted of two components. Component A, 14 WTs (n = 13 male, 1 female) were monitored over three consecutive working days. The data collected consisted of: a questionnaire; heart rate and movement data; and accelerometer data. Component B, 5 WTs (n = 5 male) were assessed undertaking two simulated casualty evacuations. Heart rate and metabolic data were collected. RESULTS: A working day lasted 7.5 hrs to 9 hrs, with WTs covering, on average, 4.7 km on the turbine. WTs spent 28% of their time undertaking "light activity", 69% "moderate activity" and 3% working "vigorously". The greatest amount of time in a day was spent working on manual handling tasks such as torque and tensioning (up to 4 hours in total). Both rescues were performed largely aerobically, with similar cardiac and metabolic demands (14.56 (4.84) mL.kg–1.min–1 vs. 17.07 (3.54) mL.kg–1.min–1). Higher RPE values were reported for the rescue from within the Hub (median (range), 13 (12–14)) compared to down the ladder (11 (7–12)), likely due to a greater requirement to manoeuvre the casualty in tight spaces. CONCLUSION: The results presented characterise the physical requirements of WTs during the working day and two casualty evacuations. These data play an important role in supporting the job task analysis for WTs working in the offshore wind industry. [ABSTRACT FROM AUTHOR]

Apnea detection is critical to the management of infant apnea. Effective monitoring and management of apnea using apnea monitors is known to reduce complications and possible fatalities in infants. There is a need to determine the critical design considerations in apnea monitors. This article reviews the design and development of infant apnea monitors. We conducted a targeted literature review from different databases, including PubMed, ScienceDirect, and Google Scholar. We reviewed articles published between January 1995 and February 2023. The search was done using combinations of key terms, namely, “apnea,” “apnea monitors,” and “apnea monitor design.” Articles that met the inclusion criteria were extracted and analyzed. The review investigated common physiological parameters, sensor types, and validation results of apnea monitors. The review revealed important design considerations adopted in the literature as well as the different sensor types and methods of apnea detection. It was found that thoracic impedance pneumography, thermistors, respiratory inductance plethysmography, pneumotachograph, and MEMS accelerometer are the most common sensor types used to design apnea monitors. The review revealed that most apnea monitors were designed to measure multiple physiological parameters. Device validation results varied from one device to another, with sensitivity and specificity metrics ranging between 80% and 96%. With the high burden of infant apnea in developing countries and its associated mortality and morbidity, the design of functional infant apnea monitors has become increasingly important to complement the expensive and complex polysomnography technique. Therefore, we must use appropriate sensors and design approaches for effective detection of infant apnea. [ABSTRACT FROM AUTHOR]

Wireless Mesh Networks are gaining traction as a solution for delivering reliable connectivity without centralized infrastructure. They operate through wireless node interconnections, forming self-configuring networks ideal for scenarios where wired networks are impractical. Routing is crucial in Wireless Mesh Networks to ensure efficient communication among nodes. However, the suitability of routing algorithms for Wireless Mesh Network's topology requires further investigation. This paper proposes an investigation into the effectiveness of routing algorithms like AODV, DSDV, and OLSR across various Wireless Mesh Networks topologies using NS-3 simulation. It also aims to determine the optimal number of nodes and protocols to maximize throughput and minimize packet loss within a limited area. Through rigorous NS-3 simulations, the study demonstrates that AODV, DSDV, and OLSR exhibit differing effectiveness across random, mesh grid, and Fruchterman-Reingold topologies. These results emphasize the importance of considering topology-specific factors when selecting and optimizing routing protocols for Wireless Mesh Networks. In summary, Wireless Mesh Networks offer decentralized connectivity, but the effectiveness of routing algorithms in different topologies remains understudied. This investigation addresses this gap by evaluating routing algorithms across various topologies, shedding light on their suitability and performance in Wireless Mesh Networks. [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]

Physical activity (PA) improves the quality of life in many ways and is of major importance in the lives of children. Almost all three- to six-year-old Norwegian children are in preschool most of their waking hours, and therefore the preschool constitutes the main arena for children’s PA. This study aims to examine the effect of PA videos on children and preschool employees’ PA levels during preschool time according to moderate to vigorous activity (MVPA) and counts per minute (CPM). Six preschools were randomly selected and participated in an intervention study. Two preschools served as a control group and four served as an intervention group. The study included 110 children aged 4-6 years, and 31 preschool employees, all wearing accelerometers during preschool time for two weeks. In the first week, both the control group and the intervention group carried out their ordinary activities. In the second week, the four preschools in the intervention group used the activity videos, while the control group kept doing their ordinary activities. The results show that the activity videos significantly increased CPM at preschool time in children. However, the activity videos did not significantly increase MVPA during preschool time in children. The findings indicate that the activity videos did not offer the children a high enough intensity level to increase PA in MVPA. Furthermore, the results show that the activity videos increased both MVPA and CPM during preschool time in the preschool employees. This is the first study to examine both children and employees PA level using activity videos at preschool time as a strategy. submission progresses knowledge on this subjectOur findings indicate that the use of activity videos can be an important contribution to more PA in preschools, both in children and employees.

The elastic modulus of concrete is one of the most important parameters in the analysis and design of concrete structures. However, determining the elastic modulus in civil structures using core-drilled samples is time-consuming and labor-intensive. Additionally, the elastic modulus of Ultra-High Performance Concrete (UHPC) varies significantly depending on its composition. This paper proposes an improved, non-destructive application to identify the elastic modulus of UHPC materials in in-service structures. The elastic modulus is estimated through calibration between a numerical model and experimental UHPC plate vibration test results, using frequency and mode shapes. This calibration involves solving an inverse problem using optimization techniques such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Cuckoo Search, and the YUKI algorithm. Updating the plate characteristics is made possible by the development of numerous iterations, where each iteration updates the elastic modulus, thickness, and width values in the term to find the best solution. The highest accuracies compared to experimental data natural frequency values were found in models updated by GA, PSO, YUKI, and Cuckoo algorithms, with errors of 10.77%, 6.58%, 6.87%, and 6.87%, respectively. An experimental sample was tested to determine the elastic modulus of the UHPC, and the proposed application showed a 0.55% error compared to the experimental value. Thus, the estimated elastic modulus value is highly accurate.

This article from Methods in Ecology & Evolution discusses the use of a new technology called the animal-attached (DOME) archival tag, which includes an optical oxygen sensor, tri-axial accelerometer, temperature and depth sensors. The tag is designed to record dissolved oxygen levels in the ocean, as well as other environmental data, while attached to animals such as the blue shark. The researchers believe that this technology has the potential to provide valuable insights into how animals perform in low oxygen environments, which could help in understanding the effects of climate-driven ocean deoxygenation on marine species. [Extracted from the article]

This study evaluated prospective associations between self-reported and objectively measured physical activity (PA) and risk of falls among older adults. A cohort study started in 2014 with 1,451 community-dwelling older adults living in Pelotas, Brazil. Leisure-time PA was obtained by the International Physical Activity Questionnaire and 7-day raw accelerometer data evaluated for total, light PA, and moderate to vigorous PA. In 2016–2017, participants recorded their falls in the previous 12 months. Around 23% of the 1,161 participants followed-up in 2016–2017 experienced a fall in the last 12 months. Participants who did not spend any time in self-reported leisure-time PA at baseline had on average 34% higher risk of falls, and individuals in the lowest tertile for moderate to vigorous PA had on average 51% higher risk of falls compared to those in the highest tertile. Low levels of self-reported and objectively measured moderate to vigorous PA were related to higher risk of falling among Brazilian older adults. [ABSTRACT FROM AUTHOR]

Purpose: To enhance performance in race riding, knowledge of current training workload is required. The objectives of this study were to quantify the physiological demands and profile the muscle activity of jockeys riding track-work. Methods: Ten apprentice jockeys and 48 horses were instrumented with heart-rate monitors, accelerometers, and a surface electromyography BodySuit (recording 8 muscle groups: quadriceps, hamstrings, gluteal, lower back, obliques, abdominal, trapezial, and pectoral) that recorded continuously while riding their normal morning track-work. Data were extracted and time matched into 200-m sections for analysis once the jockey reached steady-state canter (6.9 m·s−1). Results: Jockeys rode a mean (±SD) of 6 (1) horses each morning over 2.5 hours, spending ∼30 minutes at a canter (8.8 [ 0.7] m·s−1), with mean heart rate of 129 (11) beats·min–1 and ratings of perceived exertion representing easy-/moderate-intensity exercise. Mean magnitude of horse (0.17 [0.01] m) and jockey center of mass (0.16 [0.02] m) displacement per stride differed from that of the jockey's head (0.11 [0.01] m, P <.05). The majority of horse oscillation was damped in the upper body with a 3-fold reduction in the medio/lateral and fore/aft planes (P <.05), to minimize jockey head movement. Lower-body muscles absorbed horse motion, with core and upper-body muscles important for postural stabilization. Conclusions: The physiological demands of riding track-work were low, with no evidence of fatigue. Future research on jockeys in races as comparison would identify the specific requirements of a jockey-specific physical conditioning program. [ABSTRACT FROM AUTHOR]

A cross-sectional study was conducted to compare the habitual physical activity level, measured by accelerometry, gait performance, assessed by the GAITRite® system, handgrip strength, and static balance between older Brazilian women who participate (n = 50; 70.7 ± 5.5 years) and do not participate (n = 50; 70.1 ± 5.6 years) in a regular physical exercise program, and to investigate whether participation in a regular exercise program ensures compliance with physical activity recommendations. Older women who participated in a regular physical exercise program had significantly shorter sedentary activity time (effect size [ES] = 0.54), longer moderate activity time (ES = 0.85), and higher energy expenditure (ES = 0.64), number of steps (ES = 0.82), gait speed (ES = 0.49), and step length (ES = 0.45). However, regular participation in an exercise program did not guarantee compliance with physical activity recommendations. Behavioral changes to increase physical activity levels among older women who do and do not participate in a regular exercise program are necessary. [ABSTRACT FROM AUTHOR]

Abstract Background There is a lack of longitudinal studies examining changes in device-measured physical activity and sedentary time from childhood to young adulthood. We aimed to assess changes in device-measured physical activity and sedentary time from childhood, through adolescence, into young adulthood in a Norwegian sample of ostensibly healthy men and women. Methods A longitudinal cohort of 731 Norwegian boys and girls (49% girls) participated at age 9 years (2005–2006) and 15 years (2011–2012), and 258 of these participated again at age 24 years (2019–2021; including the COVID-19 pandemic period). Physical activity and sedentary time were measured using ActiGraph accelerometers. Linear mixed models were used to analyse changes in physical activity and sedentary time and whether low levels of childhood physical activity track, i.e., persist into young adulthood (nchange=721; ntracking=640). Results The most prominent change occurred between the ages of 9 to 15 years, with an increase in sedentary time (150 min/day) and less time spent in light (125 min/day), moderate (16 min/day), and vigorous physical activity (8 min/day). Only smaller changes were observed between the ages of 15 and 24 years. Changes in moderate-to-vigorous physical activity from childhood to young adulthood differed between subgroups of sex, tertiles of body mass index at baseline and tertiles of peak oxygen uptake at baseline. While the tracking models indicated low absolute stability of physical activity from childhood to young adulthood, children in the lowest quartiles of moderate-to-vigorous (OR:1.88; 95%CI: 1.23, 2.86) and total physical activity (OR: 1.87; 95%CI: 1.21, 2.87) at age 9 years were almost 90% more likely to be in these quartiles at age 24 years compared to those belonging to the upper three quartiles at baseline. Conclusions We found a substantial reduction in physical activity and increase in time spent sedentary between age 9 and 15 years. Contrary to previous studies, using mainly self-reported physical activity, little change was observed between adolescence and young adulthood. The least active children were more likely to remain the least active adults and could be targeted for early intervention.

This paper focuses on the use of wearable sensors to acquire and process motion data, which is essential for monitoring physiological movement and identifying gait disorders. It is particularly relevant in pediatrics, neurology, and rehabilitation. The research evaluates body motion symmetry in children using accelerometric data, taking into account factors such as age, diagnosis, and gender. Signals were recorded from 35 children (average age 10.8 years) using mobile sensors and were analyzed using digital signal processing techniques and classification methods. The proposed methodology includes data acquisition by smartphone sensors, wireless data export to a remote drive, and data processing through a graphical user interface. The highest classification accuracy of walking features, at 92.0%, was achieved with a two-layer neural network. The findings underscore the effectiveness of these tools in rehabilitation, fitness monitoring, and neurological studies.

Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use – thresholded activity counts (TAC) and the gross movement (GM) score suffer from high sensitivity and low specificity, and vice versa. We previously proposed a hybrid version of these two measures – the GMAC – that showed better overall detection performance than TAC and GM. In this paper, we answer two critical questions to improve the GMAC measure’s usefulness: (a) can it be implemented using only the accelerometer data? (b) what are its optimal parameter values? Here, we propose a modified GMAC using only the accelerometer data and optimize its parameters to develop: (a) a generic measure that is both limb- and subject-independent, and (b) limb-specific measures that were only subject-independent. The optimized GMAC showed better detection performance than the previous GMAC and surprisingly had comparable performance to the best-performing machine learning-based measure (random forest inter-subject model). In hemiparetic data, its performance was similar to the previous GMAC and the random forest inter-subject model; the limb-specific GMAC measure, however, had a better performance than the generic measure. The optimized limb-specific GMAC is a simple, interpretable alternative to a machine learning-based inter-subject model. The optimized GMAC can be a valuable measure for offline or real-time detection and feedback of upper limb use. The preliminary results of this study, based on a small dataset, need to be validated on a larger dataset to evaluate its generalizability.

Physical activity is consistently associated with reduced mortality, decreased risk for non-communicable diseases, and improved mental health in observational studies. Randomized controlled trials and observational Mendelian randomization studies support causal links between physical activity and health outcomes. However, the scarcity of evidence from randomized controlled trials, along with their inherent challenges like exposure contrasts, healthy volunteer biases, loss to follow-up, and limited real-world dose–response data, warrants a comprehensive approach. This review advocates synthesizing insights from diverse study designs to better understand the causal relationship between physical activity, mortality risk, and other health outcomes. Additionally, it summarizes recent research since the publication of current physical activity recommendations. Novel observational studies utilizing device-measured physical activity underscore the importance of every minute of activity and suggest that all intensity levels confer health benefits, with vigorous-intensity potentially requiring lower volumes for substantial benefits. Future guidelines, informed by device-measured physical activity studies, may offer refined age-specific recommendations, emphasize vigorous-intensity physical activity, and include daily step counts as a simple, easily assessable metric using commercial wearables.

Abstract Introduction Poor sleep health is associated with increased asthma morbidity and mortality. Accelerometers have been validated to assess sleep parameters though studies using this method in patients with asthma are sparse and none have compared mild to difficult-to-treat asthma populations. Methods We performed a retrospective analysis from two recent in-house trials comparing sleep metrics between patients with mild and difficult-to-treat asthma. Participants wore accelerometers for 24-hours/day for seven days. Results Of 124 participants (44 mild, 80 difficult-to-treat), no between-group differences were observed in sleep-window, sleep-time, sleep efficiency or wake time. Sleep-onset time was ~ 40 min later in the difficult-to-treat group (p = 0.019). Discussion Broadly, we observed no difference in accelerometer-derived sleep-metrics between mild and difficult-to-treat asthma. This is the largest analysis of accelerometer-derived sleep parameters in asthma and the first comparing groups by asthma severity. Sleep-onset initiation may be delayed in difficult-to-treat asthma but a dedicated study is needed to confirm.

Steps per day were measured by accelerometer for 7 days among 5,545 women aged 63–97 years between 2012 and 2014. Incident falls were ascertained from daily fall calendars for 13 months. Median steps per day were 3,216. There were 5,473 falls recorded over 61,564 fall calendar-months. The adjusted incidence rate ratio comparing women in the highest versus lowest step quartiles was 0.71 (95% confidence interval [0.54, 0.95]; ptrend across quartiles =.01). After further adjustment for physical function using the Short Physical Performance Battery, the incidence rate ratio was 0.86 ([0.64, 1.16]; ptrend =.27). Mediation analysis estimated that 63.7% of the association may be mediated by physical function (p =.03). In conclusion, higher steps per day were related to lower incident falls primarily through their beneficial association with physical functioning. Interventions that improve physical function, including those that involve stepping, could reduce falls in older adults. [ABSTRACT FROM AUTHOR]

Purpose: To assess the training load, energy expenditure, dietary intake, and sleep quality and quantity of junior tennis players during a tennis training camp. Methods: Ten junior academy tennis players (14 [1] y) completed a 6-day camp with daily morning and afternoon training. Players wore accelerometer watches to measure activity energy expenditure and sleep. Global positioning system units were worn to monitor external training load (distance covered, maximum velocity, and PlayerLoad™). Dietary intake was obtained from a food diary and supplementary food photography. Results: Players covered significantly more distance and had higher PlayerLoad™ during morning sessions than afternoon sessions (5370 [505] m vs 4726 [697] m, P <.005, d = 3.2; 725 [109] a.u. vs 588 [96] a.u., P <.005, d = 4.0). Players also ran further (5624 [897] m vs 4933 [343] m, P <.05, d = 1.0) and reached higher maximum velocities (5.17 [0.44] m·s−1 vs 4.94 [0.39] m·s−1, P <.05, d = 0.3) during simulated match play compared with drill sessions. Mean daily energy expenditure was 3959 (630) kcal. Mean energy intake was 2526 (183) kcal, resulting in mean energy deficits of 1433 (683) kcal. Players obtained an average of 6.9 (0.8) hours of sleep and recorded 28 (7) nightly awakenings. Conclusions: Junior academy tennis players failed to achieve energy balance and recorded suboptimal sleep quantity and quality throughout the training camp. [ABSTRACT FROM AUTHOR]

Purpose: The authors examined associations between preschoolers' daily glycemic variability, parents' report of hypoglycemia fear, and preschoolers' daily moderate to vigorous physical activity (MVPA) and sedentary behavior (SB) in 25 families of preschoolers with type 1 diabetes. Methods: Parents completed a valid measure of hypoglycemia fear, and their child wore an accelerometer for up to 7 days. Parents provided glucose data from their child's devices. The authors used multiple regression and multilevel modeling to analyze their data. Results: Preschoolers (mean age 4.2 [1.7] y; 50% boys) engaged in a mean of 154.5 (59.6) and 339.2 (85.1) minutes of MVPA and SB per day, respectively, and parents reported relatively low levels of hypoglycemia worry and avoidance behaviors. Preschoolers' SB (r =.19, P =.02) and MVPA (r = −.20, P =.01) levels were significantly correlated with parental hypoglycemia worry scores but not with parents' hypoglycemia behavior scores (P =.15 and P =.92, respectively). While multilevel models did not show an association between MVPA and preschoolers' glycemic variability, preschoolers who engaged in more daily SB experienced higher glycemic variability (P =.04). Conclusions: Research exploring MVPA, SB, and parental hypoglycemia fear in preschoolers with type 1 diabetes could have important clinical implications because it may reveal modifiable treatment targets that can impact preschoolers' health and activity patterns. [ABSTRACT FROM AUTHOR]

Self-report and device-based measures of physical activity (PA) both have unique strengths and limitations; combining these measures should provide complementary and comprehensive insights to PA behaviours. Therefore, we aim to 1) identify PA clusters and clusters of change in PA based on self-reported daily activities and 2) assess differences in device-based PA between clusters in a lifestyle intervention, the PREVIEW diabetes prevention study. In total, 232 participants with overweight and prediabetes (147 women; 55.9 ± 9.5yrs; BMI ≥25 kg·m-2; impaired fasting glucose and/or impaired glucose tolerance) were clustered using a partitioning around medoids algorithm based on self-reported daily activities before a lifestyle intervention and their changes after 6 and 12 months. Device-assessed PA levels (PAL), sedentary time (SED), light PA (LPA), and moderate-to-vigorous PA (MVPA) were assessed using ActiSleep+ accelerometers and compared between clusters using (multivariate) analyses of covariance. At baseline, the self-reported "walking and housework" cluster had significantly higher PAL, MVPA and LPA, and less SED than the "inactive" cluster. LPA was higher only among the "cycling" cluster. There was no difference in the device-based measures between the "social-sports" and "inactive" clusters. Looking at the changes after 6 months, the "increased walking" cluster showed the greatest increase in PAL while the "increased cycling" cluster accumulated the highest amount of LPA. The "increased housework" and "increased supervised sports" reported least favourable changes in device-based PA. After 12 months, there was only minor change in activities between the "increased walking and cycling", "no change" and "increased supervised sports" clusters, with no significant differences in device-based measures. Combining self-report and device-based measures provides better insights into the behaviours that change during an intervention. Walking and cycling may be suitable activities to increase PA in adults with prediabetes. [ABSTRACT FROM AUTHOR]

Pre-shaped microbeams, curved or inclined, are widely used in MEMS for their interesting stiffness properties. These mechanisms allow a wide range of positive and negative stiffness tuning in their direction of motion. A mechanism of pre-shaped beams with opposite curvature, connected in a parallel configuration, can be electrothermally tuned to reach a near-zero or negative stiffness behavior at the as-fabricated position. The simple structure helps incorporate the tunable spring mechanism in different designs for accelerometers, even with different transduction technologies. The sensitivity of the accelerometer can be considerably increased or tuned for different applications by electrothermally changing the stiffness of the spring mechanism. Opposite inclined beams are implemented in a capacitive micromachined accelerometer. The measurements on fabricated prototypes showed more than 55 times gain in sensitivity compared to their initial sensitivity. The experiments showed promising results in enhancing the resolution of acceleration sensing and the potential to reach unprecedent performance in micromachined accelerometers. [ABSTRACT FROM AUTHOR]