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Dynamic compaction is a soil improvement technique which involves the repeated application of an impact load to a contact area of the soil surface induced by heavy weights. Experimental measurements were prepared for evaluation of the ground wave propagation induced by the rapid impact compaction. Influence of increase of subsoil stiffness during compaction and terrain configuration were investigated. When the shear strength of soil is exceeded during the penetration of the compaction footing, increase of peak particle velocities is slower but still clearly visible along the measurement line. The peak particle velocity is damped by the subsoil below 5 mm s−1 within 40–50 m from the source of excitation. Attenuation radius is affected by the terrain configuration, when terrain elevations cause the increase of acceleration amplitudes. Rigid bodies in the ground, such as foundation structures, could have similar influence. Dominant frequencies are in the interval from 20 to 40 Hz regardless the distance from the source of excitation or the density of the treated subsoil. Obtained results are preferably related to the given boundary conditions. However, wave propagation pattern and change of its characteristics during densification of the subsoil are applicable at another construction sites considering their particularities. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study is to explore the feasibility of using machine learning approaches to objectively differentiate the mobilization patterns, measured via accelerometer sensors, of patients pre- and post-intervention. Methods: The intervention tested the implementation of a Frailty Care Bundle to improve mobilization, nutrition and cognition in older orthopedic patients. The study recruited 120 participants, a sub-group analysis was undertaken on 113 patients with accelerometer data (57 pre-intervention and 56 post-intervention), the median age was 78 years and the majority were female. Physical activity data from an ankle-worn accelerometer (StepWatch 4) was collected for each patient during their hospital stay. These data contained daily aggregated gait variables. Data preprocessing included the standardization of step counts and feature computation. Subsequently, a binary classification model was trained. A systematic hyperparameter optimization approach was applied, and feature selection was performed. Two classifier models, logistic regression and Random Forest, were investigated and Shapley values were used to explain model predictions. Results: The Random Forest classifier demonstrated an average balanced accuracy of 82.3% (± 1.7%) during training and 74.7% (± 8.2%) for the test set. In comparison, the logistic regression classifier achieved a training accuracy of 79.7% (± 1.9%) and a test accuracy of 77.6% (± 5.5%). The logistic regression model demonstrated less overfitting compared to the Random Forest model and better performance on the hold-out test set. Stride length was consistently chosen as a key feature in all iterations for both models, along with features related to stride velocity, gait speed, and Lyapunov exponent, indicating their significance in the classification. Conclusion: The best performing classifier was able to distinguish between patients pre- and post-intervention with greater than 75% accuracy. The intervention showed a correlation with higher gait speed and reduced stride length. However, the question of whether these alterations are part of an adaptive process that leads to improved outcomes over time remains. [ABSTRACT FROM AUTHOR]

The commercial value of cookies is easily lost when they break due to mechanical stress, such as shock, during distribution. Therefore, it is important to prevent shock by incorporating a cushioning design into cookie packaging. To optimize packaging, we must first understand the type of breakage caused by shock. During distribution, fatigue failure due to repetitive shocks might be the main factor involved in breakage of cookies because events related to shock stress usually occur more than once. Accordingly, we investigated the relationships between acceleration, shock frequency, and the occurrence of breakage in two types of packaged cookies (soft and hard). A drop test was performed at heights of 0.15–0.45 m. The results suggest that both types of cookies are broken by repetitive shocks, and the relationship between the number of shocks, acceleration (related to drop height), and the occurrence of breakage can be expressed as power approximation curves. Generally, the breakage of cookies due to mechanical stress is known to be a result of brittle fractures. However, our results suggest that the visible breakage of packaged cookies due to repetitive shock may be the result of fatigue failure. In addition, we propose a simple method that uses a small accelerometer to simulate breakage during transportation. The results of this study contribute to the development of new cushioning packaging designs to reduce the breakage of cookies during transportation. [ABSTRACT FROM AUTHOR]

Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we designed a master protocol to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants' self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home. [ABSTRACT FROM AUTHOR]

Individuals with cancer are recommended to engage in regular physical activity (PA) even during cancer therapy. The aim of this study was to explore how patient-reported physician PA counseling influences their PA intention and behavior in addition to psycho-cognitive determinants derived from the theory of planned behavior (TPB). A longitudinal study during cancer treatment was conducted among N = 115 patients with breast, prostate, or colorectal cancer (Mage = 58.0, SD = 11.5; 55.7% female). The median time since diagnosis was 2 months, and 19.1% were diagnosed with metastases. Participants provided information on PA counseling by their physicians and on psycho-cognitive variables of the TPB at three measurement points. Additionally, they wore accelerometers for seven days at baseline and three months later. Nearly half of participants (48%) reported basic PA counseling and 30% reported in-depth PA counseling. Patients in poorer health and with lower education reported significantly less in-depth counseling. In addition to patient self-efficacy in performing PA, only in-depth physician PA counseling, but not basic physician counseling, predicted intention for PA four weeks later. Patients' PA three months after baseline was predicted by patients' PA at baseline and their intention for PA. Overall, the PA level at baseline was identified as the most important predictor of PA three months later. Nevertheless, physicians seem to have the ability to increase their cancer patients' intention for PA by in-depth counseling. [ABSTRACT FROM AUTHOR]

A three-axis seismic accelerometer was developed and created, containing three uniaxial mutually perpendicular sensors, the sensitivity axis of each sensor is located at an angle of 54.736° to the local gravitational vertical. For the first time, additional magnetic rigidity has been introduced into each device, allowing the device to be finely tuned to the conditions of a particular planet. However, the accumulated experience in conducting ground-based tests has shown the need to improve the uniaxial seismic accelerometer, which can significantly improve the characteristics of the device. First, we use a single material—beryllium bronze BrB-2—for the manufacture of massive test mass suspension elements with a thickness of 1.5 mm and an elastic element made of a thin strip 10–20 microns thick, which reduces the likelihood of instability. Secondly, we reduce the gap in the capacitors of capacitive converters from 0.25 to 0.1–0.15 mm to significantly increase the conversion slope of the capacitive sensor. Thirdly, we change the technology of setting up the device, for which we combine all the elements of the sensitive part of a uniaxial seismogravimeter using special vertical inserts. This allows us to place the device as a whole in a system for its configuration, eliminating the possibility of failure of individual elements, thereby increasing the reliability of the device. [ABSTRACT FROM AUTHOR]

This paper analyzes the influence of three different shape memory alloy (SMA) wires (i.e., Ni-Ti, Cu-Al-Ni, and Cu-Zn wires) for mitigating model cable vibration. Shape memory alloy (SMA) wires because of their superelasticity or shape memory properties can promise to be one of the effective damping materials in suppressing stay cable oscillations. The investigation explores through experimental works, the capabilities of three Ni-Ti, Cu-Al-Ni, and Cu-Zn SMA wires by estimating the vibration amplitude suppression of the model stay cable. The effective location of the SMA wires on the model cable for vibration suppression was estimated in free as well as forced vibration cases. To impart free and forced vibration, respectively, in the cable, a transducer-based hammer and exciter were used. An accelerometer and data acquisition system was used to record and acquire vibration data. It is also very appropriate to ascertain which wire among one of the three SMA wires performs well. It is found that the Ni-Ti SMA wire has a better performance in vibration suppression as compared to Cu-Al-Ni and Cu-Zn wires. It is also established that the attachment of the SMA wire at the mid-length of the cable gives better vibration suppression for the first mode. [ABSTRACT FROM AUTHOR]

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1113 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 48.2 min (95% limits of agreement (LoA): −50.3 to 146.8 min) for total sleep duration, −17.1 min for REM duration (95% LoA: −56.7 to 91.0 min) and 31.1 min (95% LoA: −67.3 to 129.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with ~100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,262 UK Biobank participants, 1644 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.36; 95% confidence intervals (CIs): 1.18 to 1.58) or high sleep efficiency (HRs: 1.29; 95% CIs: 1.04–1.61). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity. [ABSTRACT FROM AUTHOR]

Digital health technologies (DHTs) are increasingly being adopted in clinical trials, as they enable objective evaluations of health parameters in free-living environments. Although lumbar accelerometers notably provide reliable gait parameters, embedding accelerometers in chest devices, already used for vital signs monitoring, could capture a more comprehensive picture of participants' wellbeing, while reducing the burden of multiple devices. Here we assess the validity of gait parameters measured from a chest accelerometer. Twenty healthy adults (13 females, mean ± sd age: 33.9 ± 9.1 years) instrumented with lumbar and chest accelerometers underwent in-lab and outside-lab walking tasks, while monitored with reference devices (an instrumented mat, and a 6-accelerometers set). Gait parameters were extracted from chest and lumbar accelerometers using our open-source Scikit Digital Health gait (SKDH-gait) algorithm, and compared against reference values via Bland–Altman plots, Pearson's correlation, and intraclass correlation coefficient. Mixed effects regression models were performed to investigate the effect of device, task, and their interaction. Gait parameters derived from chest and lumbar accelerometers showed no significant difference and excellent agreement across all tasks, as well as good-to-excellent agreement and strong correlation against reference values, thus supporting the deployment of a single multimodal chest device in clinical trials, to simultaneously measure gait and vital signs. Trial Registration: The study was reviewed and approved by the Advarra IRB (protocol number: Pro00043100). [ABSTRACT FROM AUTHOR]

Inertial pendulum components are key parts of a miniature wire suspended pendulum accelerometer. They are with varied shapes and multiple scales individually, which results in a complex assembly structure and assembly process. At present, the assembly mainly relies on manual operation, and assembly consistency and productivity cannot be guaranteed. In our previous work, an assembly system based on a Cartesian-coordinate robot was presented, where the mechanical structure and control system composition was mainly described. Here, a detailed assembly principle and process are given besides structure, explaining how to achieve assembly accuracy by combination of well-designed fixtures and automatic position/attitude alignment. Moreover, error analysis and error compensation methods are supplemented, and the locking method of combination fixture/suspended wire is modified to be more reliable. The experimental results demonstrate that assembly accuracy is less than 15 µm, which meets the requirement. [ABSTRACT FROM AUTHOR]

Head acceleration events (HAEs) are acceleration responses of the head following external short-duration collisions. The potential risk of brain injury from a single high-magnitude HAE or repeated occurrences makes them a significant concern in sport. Instrumented mouthguards (iMGs) can approximate HAEs. The distinction between sensor acceleration events, the iMG datum for approximating HAEs and HAEs themselves, which have been defined as the in vivo event, is made to highlight limitations of approximating HAEs using iMGs. This article explores the technical limitations of iMGs that constrain the approximation of HAEs and discusses important conceptual considerations for stakeholders interpreting iMG data. The approximation of HAEs by sensor acceleration events is constrained by false positives and false negatives. False positives occur when a sensor acceleration event is recorded despite no (in vivo) HAE occurring, while false negatives occur when a sensor acceleration event is not recorded after an (in vivo) HAE has occurred. Various mechanisms contribute to false positives and false negatives. Video verification and post-processing algorithms offer effective means for eradicating most false positives, but mitigation for false negatives is less comprehensive. Consequently, current iMG research is likely to underestimate HAE exposures, especially at lower magnitudes. Future research should aim to mitigate false negatives, while current iMG datasets should be interpreted with consideration for false negatives when inferring athlete HAE exposure. [ABSTRACT FROM AUTHOR]

Nanoelectromechanical system accelerometers have the potential to be utilized in next-generation consumer electronics, inertial navigation, and seismology due to their low cost, small size, and low power consumption. There is an urgent need to develop resonant accelerometer with high sensitivity, precision and robustness. Here, a zinc oxide resonant nano-accelerometer with high sensitivity has been designed and prototyped using zinc oxide nanowires. Within a device two nanowires were symmetrically placed close to a notched flexure to evaluate acceleration based on differential resonant frequencies. Additionally, microleverages were integrated in the accelerometer to enhance its sensitivity by amplifying the inertial force. High performance of the accelerometer has been demonstrated by the measured absolute sensitivity (16.818 kHz/g), bias instability (13.13 μg at 1.2 s integration time) and bandwidth (from 4.78 to 29.64 kHz), respectively. These results suggest that zinc oxide nanowires could be a candidate to develop future nanoelectromechanical resonant accelerometer potentially used for inertial navigation, tilt measurement, and geophysical measurements. The authors report a zinc oxide resonant nano-accelerometer with sensitivity up to 16.818 kHz/g, which is attributed to the nano-resonators using zinc oxide nanowires and the optimized microleverages and push-pull structures. [ABSTRACT FROM AUTHOR]

Piezoelectric accelerometers excel in vibration sensing. In the emerging trend of fully organic electronic microsystems, polymeric piezoelectric accelerometers can be used as vital front-end components to capture dynamic signals, such as vocal vibrations in wearable speaking assistants for those with speaking difficulties. However, high-performance polymeric piezoelectric accelerometers suitable for such applications are rare. Piezoelectric organic compounds such as PVDF have inferior properties to their inorganic counterparts such as PZT. Consequently, most existing polymeric piezoelectric accelerometers have very unbalanced performance metrics. They often sacrifice resonance frequency and bandwidth for a flat-band sensitivity comparable to those of PZT-based accelerometers, leading to increased noise density and limited application potentials. In this study, a new polymeric piezoelectric accelerometer design to overcome the material limitations of PVDF is introduced. This new design aims to simultaneously achieve high sensitivity, broad bandwidth, and low noise. Five samples were manufactured and characterized, demonstrating an average sensitivity of 29.45 pC/g within a ± 10 g input range, a 5% flat band of 160 Hz, and an in-band noise density of 1.4 µg/ Hz . These results surpass those of many PZT-based piezoelectric accelerometers, showing the feasibility of achieving comprehensively high performance in polymeric piezoelectric accelerometers to increase their potential in novel applications such as organic microsystems. [ABSTRACT FROM AUTHOR]

Introduction: Wrist-worn activity monitors have seen widespread adoption in recent times, particularly in young and sport-oriented cohorts, while their usage among older adults has remained relatively low. The main limitations are in regards to the lack of medical insights that current mainstream activity trackers can provide to older subjects. One of the most important research areas under investigation currently is the possibility of extrapolating clinical information from these wearable devices. Methods: The research question of this study is understanding whether accelerometry data collected for 7-days in free-living environments using a consumer-based wristband device, in conjunction with data-driven machine learning algorithms, is able to predict hand grip strength and possible conditions categorized by hand grip strength in a general population consisting of middle-aged and older adults. Results: The results of the regression analysis reveal that the performance of the developed models is notably superior to a simple mean-predicting dummy regressor. While the improvement in absolute terms may appear modest, the mean absolute error (6.32 kg for males and 4.53 kg for females) falls within the range considered sufficiently accurate for grip strength estimation. The classification models, instead, excel in categorizing individuals as frail/pre-frail, or healthy, depending on the T-score levels applied for frailty/pre-frailty definition. While cut-off values for frailty vary, the results suggest that the models can moderately detect characteristics associated with frailty (AUC-ROC: 0.70 for males, and 0.76 for females) and viably detect characteristics associated with frailty/pre-frailty (AUC-ROC: 0.86 for males, and 0.87 for females). Conclusions: The results of this study can enable the adoption of wearable devices as an efficient tool for clinical assessment in older adults with multimorbidities, improving and advancing integrated care, diagnosis and early screening of a number of widespread diseases. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to examine head acceleration event (HAE) propensity and incidence during elite-level men's and women's rugby union matches. Methods: Instrumented mouthguards (iMGs) were fitted in 92 male and 72 female players from nine elite-level clubs and three international teams. Data were collected during 406 player matches (239 male, 167 female) using iMGs and video analysis. Incidence was calculated as the number of HAEs per player hour and propensity as the proportion of contact events resulting in an HAE at a range of linear and angular thresholds. Results: HAE incidence above 10 g was 22.7 and 13.2 per hour in men's forwards and backs and 11.8 and 7.2 per hour in women's forwards and backs, respectively. Propensity varied by contact event, with 35.6% and 35.4% of men's tackles and carries and 23.1% and 19.6% of women's tackles and carries producing HAEs above 1.0 krad/s2. Tackles produced significantly more HAEs than carries, and incidence was greater in forwards compared with backs for both sexes and in men compared with women. Women's forwards were 1.6 times more likely to experience a medium-magnitude HAE from a carry than women's backs. Propensity was similar from tackles and carries, and between positional groups, while significantly higher in men than women. The initial collision stage of the tackle had a higher propensity than other stages. Conclusion: This study quantifies HAE exposures in elite rugby union players using iMGs. Most contact events in rugby union resulted in lower-magnitude HAEs, while higher-magnitude HAEs were comparatively rare. An HAE above 40 g occurred once every 60–100 min in men and 200–300 min in women. Future research on mechanisms for HAEs may inform strategies aimed at reducing HAEs. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular (CV) morbidity after kidney transplantation (KTx) in childhood is of increasing importance. In light of a high prevalence of CV risk factors, protective measures such as physical activity (PA) come into focus. Our aim was to comprehensively assess PA in pediatric KTx recipients and evaluate its impact on CV health. Methods: Forty-eight patients were assessed for frequency, duration, intensity, and setting of PA using the "Motorik–Modul" PA questionnaire. Walking-based activity was measured by accelerometer in a subgroup (n = 23). CV risk factors and subclinical CV organ damage were determined. The impact of PA on CV parameters was analyzed using linear regression models. Results: Fifty-two percent of pediatric KTx recipients did not reach WHO recommended PA level; 54% did not engage in PA with vigorous intensity (VPA). Twenty-nine percent indicated an extremely inactive lifestyle (< 120 min/week of moderate to vigorous intensity PA, MVPA). Compared to the healthy German KiGGS cohort, KTx recipients specifically lacked engagement in sport activities (KTx: 129 min/week; 95%CI, 97–162 vs. KiGGS, 242 min/week; 95%CI, 230–253). VPA was associated with lower systolic blood pressure (p = 0.024) and resting heart rate (p = 0.005), MVPA with fewer components of the post-transplant metabolic syndrome (p = 0.037), and better left ventricular diastolic function (p = 0.006). Conclusions: A considerable lack of PA, especially VPA, exists in young KTx recipients. PA was positively associated with important parameters of CV health. While long-term CV protection through PA seems promising in pediatric KTx recipients, specific educational approaches are most likely needed to increase patients' engagement in sport activities. [ABSTRACT FROM AUTHOR]

Background: Esports is competitive video gaming, performed within teams or individually, across multiple genres. Players are required to be sedentary for extended periods and require a high-level of cognitive skills for successful competitive performance. There are conflicting findings within the physical activity research in the esports industry. The aim of this research is to explore self-reported physical activity through accelerometer-assessed physical activity, to gain a better insight into the physical activity behaviours of international e'athletes. Method: Participants (n = 796) across multiple popular esports games, holding any in-game rank, competing at any level, were recruited. The survey consisted of demographic details, esports experience, the International Physical Activity Questionnaire-Long Form (IPAQ-LF), and Behavioural Regulations towards Exercise Questionnaire (BREQ-3). Within a convenience sample, local intervarsity e'athletes (n = 18) were recruited to wear a wrist-worn accelerometer to measure physical activity for 7-days and then complete the survey. Results from the accelerometers were compared to the survey results to explore physical activity reporting within this population. Results: When comparing IPAQ-LF to accelerometer data, players significantly over-report moderate-to-vigorous physical activity and weekly MET-min− 1 (p =.018, r =.63 and p ≤.001, r =.92). The BREQ-3 showed that e'athletes categorised as high physical activity displayed significantly higher levels of intrinsic motivation, when compared to players categorised as low and moderate physical activity. Conclusions: E'athletes significantly over report physical activity time when measured through the IPAQ-LF, suggesting previous surveys may overestimate physical activity and further research is needed. Given the exponential growth of the industry and the level of physical inactivity, esports may contribute to global physical inactivity levels. Key Points: Despite in-game rank not influencing physical activity time or motivation, higher levels of intrinsic motivation were associated with increased physical activity among e'athletes. This suggests the importance of fostering intrinsic motivation to promote sustained engagement in physical activity within esports communities. The study reveals concerning levels of sedentary behaviour among e'athletes, with implications for their overall health and well-being. Addressing sedentary behaviour and promoting light physical activity are crucial for mitigating associated health risks within the esports population. The study highlights significant disparities between self-reported physical activity data and accelerometer-assessed data among e'athletes, indicating potential over-estimation of physical activity levels when self-reported. [ABSTRACT FROM AUTHOR]

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]

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]

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 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]

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]

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]

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]

Silicon carbide (SiC) is recognized as an excellent material for microelectromechanical systems (MEMS), especially those operating in challenging environments, such as high temperature, high radiation, and corrosive environments. However, SiC bulk micromachining is still a challenge, which hinders the development of complex SiC MEMS. To address this problem, we present the use of a carbon nanotube (CNT) array coated with amorphous SiC (a-SiC) as an alternative composite material to enable high aspect ratio (HAR) surface micromachining. By using a prepatterned catalyst layer, a HAR CNT array can be grown as a structural template and then densified by uniformly filling the CNT bundle with LPCVD a-SiC. The electrical properties of the resulting SiC-CNT composite were characterized, and the results indicated that the electrical resistivity was dominated by the CNTs. To demonstrate the use of this composite in MEMS applications, a capacitive accelerometer was designed, fabricated, and measured. The fabrication results showed that the composite is fully compatible with the manufacturing of surface micromachining devices. The Young's modulus of the composite was extracted from the measured spring constant, and the results show a great improvement in the mechanical properties of the CNTs after coating with a-SiC. The accelerometer was electrically characterized, and its functionality was confirmed using a mechanical shaker. [ABSTRACT FROM AUTHOR]

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings. [ABSTRACT FROM AUTHOR]

Annotating accelerometer-based physical activity data remains a challenging task, limiting the creation of robust supervised machine learning models due to the scarcity of large, labeled, free-living human activity recognition (HAR) datasets. Researchers are exploring self-supervised learning (SSL) as an alternative to relying solely on labeled data approaches. However, there has been limited exploration of the impact of large-scale, unlabeled datasets for SSL pre-training on downstream HAR performance, particularly utilizing more than one accelerometer. To address this gap, a transformer encoder network is pre-trained on various amounts of unlabeled, dual-accelerometer data from the HUNT4 dataset: 10, 100, 1k, 10k, and 100k hours. The objective is to reconstruct masked segments of signal spectrograms. This pre-trained model, termed SelfPAB, serves as a feature extractor for downstream supervised HAR training across five datasets (HARTH, HAR70+, PAMAP2, Opportunity, and RealWorld). SelfPAB outperforms purely supervised baselines and other SSL methods, demonstrating notable enhancements, especially for activities with limited training data. Results show that more pre-training data improves downstream HAR performance, with the 100k-hour model exhibiting the highest performance. It surpasses purely supervised baselines by absolute F1-score improvements of 7.1% (HARTH), 14% (HAR70+), and an average of 11.26% across the PAMAP2, Opportunity, and RealWorld datasets. Compared to related SSL methods, SelfPAB displays absolute F1-score enhancements of 10.4% (HARTH), 18.8% (HAR70+), and 16% (average across PAMAP2, Opportunity, RealWorld). [ABSTRACT FROM AUTHOR]

This work presents a novel three-axis accelerometer that uses capacitive and piezoelectric sensing mechanisms. In the proposed structure, the out-of-plane accelerometer senses the vertical acceleration with a piezoelectric thin-film transducer and the lateral accelerations are sensed by differential capacitances. This structure has a size of 1 × 0.8 mm2 with a measurement range of ± 5 g. Simulated results showed that the sensitivities (nonlinearity) of the three-axis accelerometer are 43.58 mV/g (0.18%), 48.51 mV/g (0.19%), and 1.93 mV/g (0.018%) for the x-, y-, and z-axes, respectively. Moreover, the cross-axis sensitivities of the structure are less than 1%. Brownian noises in each direction are 1.1 µg/√Hz for the x-axis, 0.9 µg/√Hz for the y-axis, and 1.5 µg/√Hz for the z-axis. To validate the analytical results, they are compared with simulation results achieved by COMSOL software. [ABSTRACT FROM AUTHOR]

The impact of cutting the f-holes in a completed violin was investigated based on two measurable characteristics of the instrument. One of these aspects focused on the signature modes, while the other involved monitoring the graph of the bridge mobility. A particular emphasis was placed on analyzing this graph due to existing literature suggesting the connection of the bridge hill to the presence of f-holes. In the case of two real violins, bridge mobility was measured using an ultra-miniature accelerometer and an impact hammer. Concurrently, signature modes were recorded using Electronic Speckle Pattern Interferometry, an optical technique that performs well on rough surfaces; hence, the violins employed were intentionally left unvarnished. One of the violins lacked f-holes but included the rest of the structural elements. It was measured in stages as the f-holes were cut. The second violin adhered to a standard design but featured slightly wider f-holes, serving as a reference. Additionally, bridge mobility and signature modes were simulated for a finite element violin soundbox both with and without f-holes. Contrary to expectations, both the experiments and simulations revealed the emergence of the Bridge Hill even in the absence of f-holes. However, the alteration of the middle and high-frequency response was evident during the f-hole cutting process, accompanied by the observation of CBR as well as B 1 - and B 1 + modes. [ABSTRACT FROM AUTHOR]

While digital phenotyping provides opportunities for unobtrusive, real-time mental health assessments, the integration of its modalities is not trivial due to high dimensionalities and discrepancies in sampling frequencies. We provide an integrated pipeline that solves these issues by transforming all modalities to the same time unit, applying temporal independent component analysis (ICA) to high-dimensional modalities, and fusing the modalities with linear mixed-effects models. We applied our approach to integrate high-quality, daily self-report data with BiAffect keyboard dynamics derived from a clinical suicidality sample of mental health outpatients. Applying the ICA to the self-report data (104 participants, 5712 days of data) revealed components related to well-being, anhedonia, and irritability and social dysfunction. Mixed-effects models (55 participants, 1794 days) showed that less phone movement while typing was associated with more anhedonia (β = −0.12, p = 0.00030). We consider this method to be widely applicable to dense, longitudinal digital phenotyping data. [ABSTRACT FROM AUTHOR]

Aim: Teaching is known as a high-stress occupation. Managing fatigue and promoting work engagement, teachers' expectations and perceptions can impact work-related stress–recovery–balance and physical activity levels. The main objective of the current study was to investigate the relationship between intrinsic motivational factors and the autonomic nervous system, such as heart rate and heart rate variability regulation, sleep, and physical activity levels. Methods: The research included 66 primary and high school teachers in Tampere, Finland, in 2020–2021. The Reiss Motivation Profile® (RMP) was selected as an instrument to provide a practical approach to understanding people's intrinsic motivators and the effects of those on the Firstbeat®-measured heart rate and heart rate variability, and accelerometry. Stepwise multiple linear regression models were used to assess whether the RMP motives reflected Firstbeat®-measured outcomes. Results: Motives such as Acceptance, Eating, Family, Physical Activity, Power, Tranquillity, and Vengeance were found to have a relationship with Firstbeat®-measured outcomes. An increase in the Physical Activity motive was related to lower stress and less light physical activity. Furthermore, an increase in the Physical Activity motive showed a relationship with better recovery and a higher amount of vigorous physical activity. However, the statistical analysis of heart rate and heart rate variability showed only low explanatory power (R2 = 0.111–0.140) for stress, recovery, and sleep. On the contrary, the explanatory power of measurements related to physical activity and sedentary behavior was higher (R2 = 0.171–0.298). In addition, the need for Acceptance as an important intrinsic motive that may prevent people from vigorous physical activity deserves further research. Conclusion: The results indicated that there are several factors affecting the autonomous nervous system but also behavior. The basic desires explained sedentary and physical activity behavior better than the functioning of the autonomic nervous system. The 16 life motives can serve as a valuable theory for understanding better how to encourage people to have healthier habits. [ABSTRACT FROM AUTHOR]

The last few decades have witnessed a remarkable amount of research addressing numerous challenges in the domain of human activity recognition. One popular problem in this domain has been that of gait analysis. A subproblem in this domain is to identify the speed of a mobile object through gait analysis. Apart from clinical diagnostic applications, the detection of the speed of a person is also important in remote health monitoring, tracking of the mentally incompetent, and determining proper ambulatory assistive devices for the orthopaedically impaired. Gait analysis-related problems commonly deal with large volumes of interrelated data for which machine-learning techniques have been proven effective. However, the size of the feature set used in such problems is a crucial factor. The choice of a large feature set may complicate the approach for long-term analysis. The present work addresses the problem of human walking speed classification through the machine learning approach. Data was experimentally collected with the mobile phone sensors carried by volunteers of different physiques. Only the acceleration readings along the three axes of the accelerometer are considered for further experimentation. Although walking speed is a personal trait, four classes of data have been curated, namely, slow walking, moderate walking, fast walking, and sitting. The speeds of the walks were not pre-defined so the volunteers performed the walks as per their own comfort, which enhances the challenge of distinguishing between sensor signals of varying speed. Experiments have been performed using different supervised learning algorithms with only acceleration data. The performance of the learning models has been analyzed with the help of accuracy, precision, recall, f1-score, and the ROC curve in a One-vs-Rest approach. The results demonstrate that the performance of this system for walking speed identification is comparable to state-of-the-art works. Our work has a unique perspective as it uses a primary dataset comprising only three features. [ABSTRACT FROM AUTHOR]

Young adults entering college are exposed to new and ever-changing stressors that powerfully affect health and academic achievement. While engaging in physical activity can help to manage the experience of stress, stress itself is an important barrier to activity. The purpose of this study is to examine the bidirectional relationships between physical activity and momentary stress among college students. We further examined whether these relationships were modified by trait mindfulness. Undergraduate students (N = 61) completed a single measure of trait mindfulness and up to 6 daily ecological momentary assessments of stress for one week while wearing an ActivPAL accelerometer. Activity variables were aggregated in the 30, 60, and 90 min before and following each stress survey. Multilevel models revealed significant negative relationships between stress ratings and total volume of activity both preceding and following the survey. Mindfulness did not modify these relationships but was independently and negatively related to momentary reports of stress. These results underscore the importance of developing activity programming for college students that addresses stress as a powerful and dynamic barrier to behavior change. [ABSTRACT FROM AUTHOR]

Background: Few adolescents meet guideline levels of physical activity associated with good health, highlighting the need for intervention. Interventions promoting adolescents' physical activity should be guided by research applying behavioral theory to identify potentially modifiable correlates and associated processes. We applied an integrated social cognition model to identify theory-based constructs and processes that relate to physical activity intentions in a secondary analysis of two samples of Finnish adolescents using a correlational design. Method: Participants in the first sample (n = 455) completed self-report measures of social cognition constructs from theory of planned behavior, habit, self-discipline, and past and current physical activities. Participants in the second sample (n = 3878) completed identical measures plus measures of socio-structural and socio-environmental factors. Participants from the first sample also wore accelerometers for 1 week. Hypothesized model effects were tested using variance-based structural equation modeling in data from the first sample and subsequently confirmed in a pre-registered analysis of data from the second sample. Results: Across both samples, habit, attitude, perceived behavioral control, and self-reported past behavior were associated with physical activity intention. Effects of self-reported past physical activity on intention were partially mediated by social cognition constructs. Effects of accelerometer-based physical activity were small by comparison. Effects of socio-structural and socio-environmental factors on intention in the second sample were partially mediated by the social cognition constructs. Conclusion: Results corroborate beliefs and habit as consistent correlates of adolescents' physical activity intentions and provide preliminary evidence that social cognition constructs account for effects of socio-structural and socio-environmental factors on intentions. [ABSTRACT FROM AUTHOR]

Background: Body mass index (BMI) above ≥ 25 kg/m2 is associated with increased risk for cancer-related morbidity and mortality. Achieving recommended amounts of physical activity (PA), sedentary time (ST), and sleep can help cancer survivors (CS) attain a healthy BMI. This cross-sectional study examined the potential role of reallocating time between moderate and light PA, ST, and sleep on BMI in CS. Method: A sample of CS (N = 73, Mage = 53.7 ± 12.9) wore an activPAL and Actiwatch accelerometer for 7 days, 24 h per day to measure PA intensity and sleep, respectively. Self-reported height and weight or scale/stadiometer were used to calculate BMI. Isotemporal substitution models were used to reallocate time, averaged over the 7-day period, from one activity of interest to another and examine the associations with BMI. Statistical significance was set at p <.05. Results: The following reallocations of 30 min were significantly associated with BMI: sleep to ST (+ 0.80 kg/m2, p = 0.02) and ST to light PA (− 0.53 kg/m2, p = 0.008). No significant associations with BMI were observed for reallocating time to or away from moderate-vigorous PA. Conclusion: The results of this study suggest that sleep and light PA may have important implications for achieving a healthy BMI in CS. Therefore, future research should include interventions which target light PA and sleep to determine if they can improve BMI in CS. [ABSTRACT FROM AUTHOR]

Researchers are paying increasing attention to the development of low-cost and microcontroller-based accelerometers, in order to make structural health monitoring feasible for conventional bridges with limited monitoring budget. Parallel with the low-cost sensor development, the use of the embedded accelerometers of smartphones for eigenfrequency analysis of bridges is becoming popular in the civil engineering literature. This paper, for the first time in the literature, studies these two promising technologies by comparing the noise density and eigenfrequency analysis of a self-developed, validated and calibrated low-cost Internet of things based accelerometer LARA (low cost adaptable reliable accelerometer) with those of a state of the art smartphone (iPhone XR). The eigenfrequency analysis of a footbridge in San Sebastian, Spain, showed that the embedded accelerometer of the iPhone XR can measure the natural frequencies of the under study bridge. [ABSTRACT FROM AUTHOR]

Tri-axis MEMS accelerometers are used in a wide range of applications such as health monitoring, industrial electronics and navigation guidance due to their low cost, high performance and integration. At this stage, the main structure of commercial products of triaxial accelerometers is the integration of three single-axis capacitive accelerometers. However, many reports have shown that resonant accelerometers can achieve better performance than capacitive accelerometers. In this paper, a novel triaxial accelerometer include a dual-axis resonantors and two torsional elements is proposed. The prototype was fabricated using MEMS processing technology and had a footprint of 10.8 mm × 10.8 mm. A non-vacuum package is adopted to guarantee the stability of the torsional accelerometers included. As a result, a low Q value is obtained for each part included. The effect of low Q is analyzed and its control circuit is optimized to obtain excellent stability. Finally, the triaxial accelerometer prototype was tested to evalute its performance, the Q value of each resonantors is lower than 100, the cross-axis coupling coefficient is below than 0.5%, the nonlinearity is belower than 0.053% at a range of ± 10 g and the bias instability at each axis is 5.7 μg, 4.8 μg and 73 μg respectively (X, Y and Z aixs). [ABSTRACT FROM AUTHOR]

In this study, simulation of a silicon sensitive element (SE) of capacitive accelerometer using the analytical model to obtain the optimized sensitivity of the capacitive type accelerometer structure with comb-drives is presented. The objective of the article is to consider the design of the silicon SE, deformation results, mechanical stresses and capacitance changes between the gaps of comb-drives on the silicon SE of MEMS-based capacitive type accelerometer. At first, the structural design of the silicon SE of MEMS-based capacitive accelerometer structure with the sensitive comb-drives using the software of SOLIDWORKS is given. The deformation results and mechanical stresses of the SE of MEMS-based capacitive accelerometer along the working axes of X and Y, which play an essential role for calculation of capacitance changes between the comb-drives of the structure by using the ANSYS software are analyzed. The deformation results of the silicon SE at the temperature ranges from –40 to 80°С with an acceleration range of 300 g by using the program of ANSYS are considered. Capacitance changes between comb-drives based on the deformation results on the silicon SE of capacitive accelerometer with the membrane of 40 µm thickness when exposed to the structure of the SE with the acceleration ranges from 50 to 300 g by using the program of Ansys Maxwell are studied. To enhance the sensitivity of the capacitive comb type accelerometer, the presented design is more appropriate for manufacturing of sensitive devices in the field of MEMS sensors. [ABSTRACT FROM AUTHOR]

To examine the associations between physical activity (PA) levels and mental health in children and adolescents with intellectual disabilities (IDs) during the COVID-19 pandemic, 117 participants aged between 6 and 17 years with IDs from 10 Hong Kong special schools were included. There were positive dose–response associations between PA (i.e., light PA, moderate PA, and vigorous PA) and mental health, and participants with higher levels of moderate-to-vigorous PA (MVPA) and self-concept (SC) had better social quality of life (QoL) than those with lower levels of MVPA and SC. Moreover, personal and environmental factors such as age, body mass index, school, sex, ID level, and parental education level influenced the PA levels and QoL in children and adolescents with IDs. [ABSTRACT FROM AUTHOR]

A new, improved approach in the design of broadband seismometers is presented. The design results in the implementation of a high performance, low cost, and simple-to-operate instrument. The proposed seismometer is based on a modified accelerometer followed by a continuous time integrator for providing velocity voltage output. It has a broadband response, flat in velocity from 120 s to 75 Hz, high sensitivity 1200 V/(m/s), and 40 Vpp differential output range. The acceleration integration method provides high performance at low frequencies, with self-noise well below the New Low Noise Model at the range 80 s–16 Hz. The mechanical system provides a perfectly linear response of its displacement sensing system. Evaluation, classification, and noise determination of the presented instrument are performed in terms of direct experimental measurements, simulations, and calculations based on raw data from the proposed sensor and from a commercial product with approximately equivalent performance. Its technical features and performance specifications guarantee accurate sensing of local events, with maximum power at the frequency range of 5 to 10 Hz, but also make it ideal for the recording of distant tectonic activity, where extremely weak motions at long periods are expected. The whole design is robust, lightweight, and weatherproof, comprising in this way a useful tool to geoscientists. [ABSTRACT FROM AUTHOR]

Context: Landscape composition and configuration, as well as seasonal landscape dynamics shape the behaviour, movement and energy expenditure of animals, i.e. foraging, hiding or fleeing, and ultimately survival. Especially in highly modified agricultural systems, it is crucial to understand how animal behaviour is influenced by landscape context to develop sustainable land management concepts. Objectives: We show how landscape composition and configuration, together with seasonal dynamics affect animal behavioural types, accounting for the different life-history events in both sexes. Methods: We investigated 34 European hares in two contrasting agricultural landscapes (a simple and a complex landscape) by using tri-axial accelerometer data to classify the animals' behaviour into five categories: resting, foraging, moving, grooming and standing upright (i.e. vigilance behaviour). We tested whether the amount of behaviours per category changed with landscape composition and configuration, season and sex. Results: During peak breeding, hares in areas of high habitat diversity rested more, moved less and spent less time searching for resources. During winter, hares moved more and rested less. Females rested less and foraged more in areas with large agricultural fields. Conclusions: A complex landscape is particularly important during the breeding season, allowing animals to allocate enough energy into reproduction. In winter, hares in areas of low habitat diversity may not find enough thermal and anti-predator shelter to move as much as they would need to meet their requirements. Hence, high habitat diversity and small field sizes guarantee species persistence in human-altered agricultural areas throughout the year. [ABSTRACT FROM AUTHOR]

The piezoelectric coupling principle is widely used (along with capacitive coupling and piezoresistive coupling) for MEMS accelerometers. Piezoelectric MEMS accelerometers are used primarily for vibration monitoring. Polymer piezoelectric MEMS accelerometers offer the merits of heavy-metal-free structure material and simple microfabrication flow. More importantly, polymeric piezoelectric MEMS accelerometers may be the basis of novel applications, such as fully organic inertial sensing microsystems using polymer sensors and organic integrated circuits. This paper presents a novel polymer piezoelectric MEMS accelerometer design using PVDF films. A simple and rapid microfabrication flow based on laser micromachining of thin films and 3D stereolithography was developed to fabricate three samples of this design. During proof-of-concept experiments, the design achieved a sensitivity of 21.82 pC/g (equivalent open-circuit voltage sensitivity: 126.32 mV/g), a 5% flat band of 58.5 Hz, and a noise density of 6.02 µg/√Hz. Thus, this design rivals state-of-the-art PZT-based counterparts in charge sensitivity and noise density, and it surpasses the performance capabilities of several commercial MEMS accelerometers. Moreover, this design has a 10-times smaller device area and a 4-times larger flat band than previous state-of-the-art organic piezoelectric MEMS accelerometers. These experimentally validated performance metrics demonstrate the promising application potential of the polymeric piezoelectric MEMS accelerometer design presented in this article. [ABSTRACT FROM AUTHOR]

Traditionally, in the oil and gas industry, accelerometers are mounted externally on motors for condition monitoring of vertically suspended, closed suction hydrocarbon pumps due to their inability to withstand harsh downhole environments, preventing the detection of impeller failures. This study addresses the need for encapsulation solutions for accelerometers submerged in hydrocarbon fluid environments. It evaluates the feasibility of epoxy and fluoroelastomer as encapsulation materials for long-term immersion in high-temperature hydrocarbon fluid and determines their impact on the accelerometer's performance. Extensive testing involved submersion in high-temperature hydrocarbon fluid at 150 °C for over 10,000 h and six months in brine. Material characterization, including mass variation, microscopic imaging, and FTIR spectroscopy, revealed negligible degradation. Encapsulated accelerometers effectively detected vibrations with an acceptable alteration in amplitude. In comparison with commercial alternatives, our encapsulation outperformed them. While oil traces became evident within just 24 h in the alternatives, our solution exhibited no signs of leakage. This research pioneers a novel packaging solution employing epoxy and fluoroelastomer for side-exit commercial sensors tailored for high-temperature hydrocarbon fluid applications, addressing a critical gap in the industry. Our work enhances reliability and safety for vertical oil pump condition monitoring in downhole applications, benefiting the oil and gas sector. [ABSTRACT FROM AUTHOR]

The unremitting development of portable vertical jump equipment emphasizes the need for continual examination of its validity and reliability. Equal representation between sexes should be endorsed in this process to improve precision and application. This study aimed to examine the concurrent validity and reliability of the Exsurgo g-Flight™ photoelectric boxes and the Output Sports V2™ accelerometer for calculating vertical jump height when compared to a dual force plate system (Hawkin Dynamics Force Plate, ME, USA). Twenty (n = 20) female soccer players performed three counter movement jumps on two different sessions for a total of 120 jumps. Comparisons between the criterion method and secondary measures were performed using Bland–Altman plots and were independently examined using a paired samples t-test. Reliability in instrument error was determined by comparing the residuals of each secondary measure over two assessments. Bland–Altman plots revealed ≤ 5.0% of residuals for the accelerometer and photoelectric devices fell outside the limits of agreement. The accelerometer revealed a mean bias (95% CI) of 0.30 ± 0.33 cm, while the photoelectric device demonstrated a negative bias of −1.64 ± 0.45 cm. Mean (± SD) comparisons revealed an overestimation in jump height with the photoelectric device (1.6 ± 4.0 cm, p < 0.0001) and insignificant differences with the accelerometer (0.30 ± 3.7 cm, p > 0.05). Mean differences in residuals between testing dates for secondary measures were insignificant (p ≥ 0.23). Both the photoelectric and accelerometer instruments displayed high reliability, but differences in the accuracy between devices were observed. [ABSTRACT FROM AUTHOR]

The monitoring of human activity using a variety of sensors and sensory devices is currently an important research topic in healthcare, sports, daily life, and many other fields. Due to their versatility, wearable devices based on accelerometry technology have found wide application in our lives. This article presents an analysis of the state of this area over the past 5½ years. A set of about 1000 articles from four international databases (Scopus, Web of Science, IEEE Xplore, Springer) was analyzed, from which 218 articles published from 2017 to 2022 were selected using a number of sorting steps. Analysis of these articles led to development of a classification of studies depending on the areas of application of accelerometer-based devices. Data on the times and places of publication of these studies were also analyzed, such that development trends in this area over the period under review could be tracked visually. The results obtained here provided an evaluation of the general directions of research in the field of biomedical accelerometry technology, as well as publication trends. [ABSTRACT FROM AUTHOR]

Objectives: Patterns of physical behaviors including physical activity, sedentary time, and sleep are unknown during pregnancy, but are likely to influence health outcomes. The purpose was to first identify "physical behavior phenotypes" from accelerometer-measured physical behaviors in pregnant women during the first trimester and secondly, to explore the associations between the identified phenotypes with demographic variables and body-mass-index (BMI). Methods: Data were from the Glowing Study (gov ID: NCT01131117), collected between 2011 and 2017 with accelerometer-measured physical behaviors of women in their 12th week of pregnancy. Latent class analysis was used to identify patterns of total physical activity, sleep time, sedentary time, and variation in physical activity. Maternal Body-Mass-Index (BMI). BMI and sociodemographic characteristics were compared between physical behavior phenotypes. Results: A total of 212 pregnant women were included in the study (mean age 30.2 years (range 22.1 to 42.4), mean days wear 4.3 (SD 0.7)). Three physical behavior phenotypes were identified from the four physical behavior constructs: low sedentary and stable activity (n = 136, 64%), variable activity (n = 39, 18%), high sedentary and low sleep (n = 37, 17%). BMI, race, and education were significantly different between the three phenotypes, with the low sedentary and stable activity phenotype having the lowest BMI and a higher percentage of white and college educated women. Conclusions for Practice: Total physical activity and physical behavior phenotypes during the first trimester were associated with early-pregnancy BMI, race, and education. Future research should examine whether these physical behavior phenotypes are associated with maternal and child health outcomes. Significance: What is already known on this subject? Physical activity and sleep are complex behaviors that influence health, however, little is known on patterns of these behaviors among women in their first trimesters. What this study adds? Three physical behavior phenotypes were identified in women during their first trimester. Maternal BMI, race, and education differed between the groups. Examining these behaviors together may help to better understand health in pregnant women and to help improve physical behaviors. [ABSTRACT FROM AUTHOR]

Background: Children with visual impairment (VI) tend to maintain the insufficient physical activity (PA) engagement and excessive sedentary behavior (SB) that could elevate health risks of various domains. Anxiety have been shown to correlate with PA and SB in general population, whereas the longitudinal or temporal relationships between PA, SB, and anxiety have yet to be examined in children with VI. Therefore, the primary purpose of this study is to investigate the bidirectional relationships between these variables over time. Methods: 72 Chinese children with VI (mean age = 14.65 ± 3.34 years) recruited from a special school for individuals with VI participated in this study. Moderate-to-vigorous physical activity (MVPA) and SB were assessed using ActiGraph GT3x + accelerometers across seven consecutive days. Anxiety was assessed by a self-reported questionnaire. Cross-lagged panel models were employed to examine the potential bidirectional relationships between PA, SB, and anxiety at baseline (T1) and 12-week follow-up (T2). Results: T1 anxiety significantly predicted T2 MVPA only in children with low vision (β= -0.30, p = 0.01). In additional, children with blindness showed a cross-lagged effect of T1 MVPA on T2 anxiety (β=-0.29, p = 0.02). Lastly, anxiety significantly predicted T2 SB in the total sample (β = 0.21, p = 0.04) and children with low vision (β = 0.42, p < 0.01). Conclusion: The relationships between PA, SB and anxiety were not bidirectional in children with VI. It is suggested that higher anxiety predicted less PA and more SB in children with low vision. [ABSTRACT FROM AUTHOR]

Evidence-based online behavioral weight loss (BWL) treatment targets a combination of diet, physical activity, and behavioral skills training. While weight loss outcomes are well documented, little is known about changes in physical activity. This study examined changes in objectively measured physical activity across the energy expenditure spectrum during a fully automated, online BWL program. Adults with overweight or obesity (n = 63) completed a 12-week, online BWL program. Participants wore an accelerometer for 7 days and body weight was measured in-clinic at pre- and post-treatment. At post-treatment, mean daily moderate-to-vigorous physical activity increased by about 4 min (SE = 1.59, p = 0.01). There were no statistically significant changes in light physical activity or time spent sedentary (p's > 0.05). Despite only minimal changes in moderate-to-vigorous physical activity overall, larger increases correlated with greater weight loss (r = − 0.28, p = 0.02), which averaged 6.1% of baseline weight at post-treatment. Though achieving important weight loss outcomes, online programs may fail to produce clinically relevant improvements in physical activity, which can put weight loss maintenance at risk. [ABSTRACT FROM AUTHOR]

Prior work suggests physical activity (PA) is related to quantity and quality of sleep. Questions remain regarding directionality, and whether relationships vary by age, sex, and race. We examined daily bidirectional associations between PA and sleep over one week. Participants were 427 adults from the MIDUS Biomarker study, with a mean age of 54.21 ± 11.67 (61% female, 30% non-white). PA (total activity) and sleep (total sleep time; TST and waking after sleep onset; WASO) were measured with an ActiWatch 64. Multilevel mixed-effects models showed differences in the relationships between PA and sleep by age, race, and sex. Following a day with increased PA, younger and nonwhite participants had significantly shorter TST. Days with higher PA were also associated with less WASO for younger participants. Bidirectional effects also emerged; following a night with shorter TST, men, not women, engaged in less next-day PA. Like other studies of daily PA and sleep, effect sizes were small. Future studies should assess potential mechanisms that could explain these demographic differences. [ABSTRACT FROM AUTHOR]

In this paper, a dynamic analysis of a tunable novel triple-electrode variable capacitor is presented. It is subjected to a base excitation that can act as an accelerometer with a wide measurement range. A clamped–clamped porous microbeam connected to a frame and a proof mass in the middle of it form the structure. The sensing section of the accelerometer is in the form of capacitive sensing combs in which the fixed fingers connected to the frame are subjected to a biasing DC voltage ( V S ). In the tuning section of the accelerometer, two fixed fingers (electrodes) connected to the frame are subjected to a DC tuning voltage ( V T ), and two moving free electrical charge fingers connected to the beam act as a sliding connector. A DC tuning voltage applied to the fixed electrodes generates an electrostatic force that works as a tunable voltage-sliding nonlinear spring in the structure. Coupled nonlinear differential equations of the transversal and longitudinal vibration of the structure in the presence of the base excitation are extracted. The steady-state solution of the accelerometer is obtained based on a physically based learning method that makes it possible to obtain its frequency response for the first harmony as well as for the higher harmonies and to predict primary and secondary resonances in different harmonies of the response. The transient response of the accelerometer in exposure to various types of external acceleration fields such as step, single pulse, and repetitive pulse is investigated. The frequency response of the accelerometer for different values of tuning voltage is studied. It is shown that the tuning stiffness of this voltage-sliding spring provides a highly effective solution to realize instrumentation with wide-range measurement. The presented tunability can enable the structure to act as an effective accelerometer, microresonator, energy harvester, and so on. [ABSTRACT FROM AUTHOR]