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Progressive gait impairment is common among aging adults. Remote phenotyping of gait during daily living has the potential to quantify gait alterations and evaluate the effects of interventions that may prevent disability in the aging population. Here, we developed ElderNet, a self-supervised learning model for gait detection from wrist-worn accelerometer data. Validation involved two diverse cohorts, including over 1000 participants without gait labels, as well as 83 participants with labeled data: older adults with Parkinson's disease, proximal femoral fracture, chronic obstructive pulmonary disease, congestive heart failure, and healthy adults. ElderNet presented high accuracy (96.43 ± 2.27), specificity (98.87 ± 2.15), recall (82.32 ± 11.37), precision (86.69 ± 17.61), and F1 score (82.92 ± 13.39). The suggested method yielded superior performance compared to two state-of-the-art gait detection algorithms, with improved accuracy and F1 score (p < 0.05). In an initial evaluation of construct validity, ElderNet identified differences in estimated daily walking durations across cohorts with different clinical characteristics, such as mobility disability (p < 0.001) and parkinsonism (p < 0.001). The proposed self-supervised method has the potential to serve as a valuable tool for remote phenotyping of gait function during daily living in aging adults, even among those with gait impairments. [ABSTRACT FROM AUTHOR]

Wedgefishes have recently been recognised as one of the most imperilled marine fish families worldwide. However, many knowledge gaps about their biology and ecology hinder conservation efforts. Here we used a combination of acoustic telemetry and acceleration datalogger technology to gain fundamental insights into the fine-scale behaviour, habitat use, size of activity spaces, and residency of adult female bottlenose wedgefish (Rhynchobatus australiae) in the Ningaloo region of northwestern Australia. Acoustic tracking data over one year demonstrated that female bottlenose wedgefish continuously resided in a relatively small area of a productive coral reef lagoon. Acceleration data revealed that bottlenose wedgefish were nocturnal, with time of day having a greater influence on activity than tidal patterns. Bottlenose wedgefish also increased activity with seasonally increasing temperatures. We identified several discrete behavioural signatures in the acceleration data, inferred to correspond to chafing, settling/burying behaviour, foraging behaviour, and escape behaviour, based on their kinematics. Further observations are required to confirm these behaviours with certainty. Additionally, according to datalogger and acoustic data, tagged bottlenose wedgefish rarely inhabited areas greater than 2 m deep. Together, these first insights into behaviour and habitat use of adult female bottlenose wedgefish highlight the importance of nearshore habitats for this species and indicate that they may be highly resident to specific areas. Our findings provide important insight into the conservation of bottlenose wedgefish in northwestern Australia, including potential effectiveness of protected areas and interactions with specific anthropogenic threats such as shoreline development and recreational beach fishing. [ABSTRACT FROM AUTHOR]

Studies on spontaneous rupture mechanisms are limited because using existing experimental apparatuses is difficult to accurately observe parameters related to the rupture (e.g., slip history and frictional properties of fault). This study proposes multiple diagnostic systems combined with a biaxial loading device to study the rupture mechanism. Two isosceles right triangle PMMA plates are held together to form a fault model. The spontaneous rupture is achieved under various friction angles through different combinations of the x-axis and y-axis loading forces. A digital image correlation (DIC) method is systematically used to measure displacement and stress fields along the fault during rupture, thus accurately quantifying rupture velocity and stress distribution. Accelerometers are used to measure seismic waves. In this experiment, the supershear rupture can be detected, and the potential energy and the energy consumed are discussed. Typical results prove the feasibility and flexibility of the proposed system. [ABSTRACT FROM AUTHOR]

The uncertainty of hand gestures, the variability of gestures across subjects, and the high cost of collecting a large amount of annotated data lead to a great challenge to the robust recognition of gestures, and thus it remains quite crucial to capture the informative features of hand movements and to mitigate inter-subject variations. To this end, we propose a gesture recognition model that uses two different types of sensors and optimizes the feature space towards enhanced accuracy and better generalization. Specifically, we use an accelerometer and a surface electromyography sensor to capture kinematic and physiological signals of hand movements. We use a sliding window to divide the streaming sensor data and then extract time-domain and frequency-domain features from each segment to return feature vectors. Afterwards, the feature space is optimized with a feature selector and a gesture recognizer is optimized. To handle the case where no labeled training data are available for a new user, we apply the transfer learning technique to reuse the cross-subject knowledge. Finally, extensive comparative experiments concerning different classification models, different sensors, and different types of features are conducted. Results show that the joint use of kinematic and physiological sensors generally outperforms the use of single sensor, indicating the synthetic effect of different sensors, and that the use of transfer learning helps improve the cross-subject recognition accuracy. In addition, we quantitatively investigate the impact of null gesture on a gesture recognizer and results indicate that null gesture would lower its accuracy, enlightening related studies to consider it. [ABSTRACT FROM AUTHOR]

Background: We aimed to explore the association of sleep duration with depressive symptoms among rural-dwelling older adults in China, and to estimate the impact of substituting sleep with sedentary behavior (SB) and physical activity (PA) on the association with depressive symptoms. Methods: This population-based cross-sectional study included 2001 rural-dwelling older adults (age ≥ 60 years, 59.2% female). Sleep duration was assessed using the Pittsburgh Sleep Quality Index. We used accelerometers to assess SB and PA, and the 15-item Geriatric Depression Scale to assess depressive symptoms. Data were analyzed using restricted cubic splines, compositional logistic regression, and isotemporal substitution models. Results: Restricted cubic spline curves showed a U-shaped association between daily sleep duration and the likelihood of depressive symptoms (P-nonlinear < 0.001). Among older adults with sleep duration < 7 h/day, reallocating 60 min/day spent on SB and PA to sleep were associated with multivariable-adjusted odds ratio (OR) of 0.81 (95% confidence interval [CI] = 0.78–0.84) and 0.79 (0.76–0.82), respectively, for depressive symptoms. Among older adults with sleep duration ≥ 7 h/day, reallocating 60 min/day spent in sleep to SB and PA, and reallocating 60 min/day spent on SB to PA were associated with multivariable-adjusted OR of 0.78 (0.74–0.84), 0.73 (0.69–0.78), and 0.94 (0.92–0.96), respectively, for depressive symptoms. Conclusions: Our study reveals a U-shaped association of sleep duration with depressive symptoms in rural older adults and further shows that replacing SB and PA with sleep or vice versa is associated with reduced likelihoods of depressive symptoms depending on sleep duration. [ABSTRACT FROM AUTHOR]

Motor neuron disease (MND) is a rapidly progressive condition traditionally assessed using a questionnaire to evaluate physical function, the revised amyotrophic lateral sclerosis functional rating scale (ALSFRS-R). Its use can be associated with poor sensitivity in detecting subtle changes over time and there is an urgent need for more sensitive and specific outcome measures. The ActiGraph GT9X is a wearable device containing multiple sensors that can be used to provide metrics that represent physical activity. The primary aim of this study was to investigate the initial suitability and acceptability of limb-worn wearable devices to group of people with MND in Scotland. A secondary aim was to explore the preliminary associations between the accelerometer sensor data within the ActiGraph GT9X and established measures of physical function. 10 participants with MND completed a 12-week schedule of assessments including fortnightly study visits, both in-person and over videoconferencing software. Participants wore the device on their right wrist and right ankle for a series of movements, during a 6-min walking test and for a period of 24-h wear, including overnight. Participants also completed an ALSFRS-R and questionnaires on their experience with the devices. 80% of the participants found wearing these devices to be a positive experience and no one reported interference with daily living or added burden. However, 30% of the participants experienced technical issues with their devices. Data from the wearable devices correlated with established measures of physical function. [ABSTRACT FROM AUTHOR]

The primary motivation behind this study is the aspiration to design a prosthetic foot that demonstrates enhanced functionality, enabling more active and prompt responses, particularly tailored for individuals with below-knee amputations. This goal underscores the intention to create a prosthetic foot with the capability to execute foot movements in a more natural and effective manner. A new 1D-ResCNN model has been proposed for the rapid and accurate classification of foot movements based on user intent in the context of a prosthetic limb. This research introduces an innovative approach by integrating inertial measurement units with deep learning algorithms to advance the development of more functional prosthetic feet, specifically tailored for below-knee amputees. Leveraging wearable technologies, this method allows for the prolonged monitoring of foot movements within the users' natural environments. The dual benefits of cost reduction and enhanced user experience are achieved through this combination of advanced technologies, providing a promising avenue for the evolution of prosthetic foot design and usage. The results obtained with this model are satisfying both in terms of speed and accuracy with 99.8% compared to other methods in the literature. [ABSTRACT FROM AUTHOR]

The measurement of low frequency vibration signals is of great significance in seismic monitoring, health monitoring of large and medium-sized engineering structures, resource exploration, etc. A cross spring leaf-based novel FBG accelerometer was designed against the problem that it's hard for FBG acceleration sensors to effectively pick up low frequency vibration signals. Firstly, the vibration model of acceleration sensor was built and its operating principle was analyzed; secondly, the effect of structural parameters of sensor on their sensitivity and natural frequency was analyzed, and the sensor was subjected to static stress analysis and dynamic characteristics analysis through the ANSYS simulation software; finally, the sensors were subjected to performance tests with a low frequency vibration test system. Experimental results suggested that the natural frequency of FBG acceleration sensor was 63.65 Hz, it gives a flat sensitivity response in the low frequency band of 0.1–40 Hz; its sensitivity was not lower than 2000 pm/g, and the transverse interference was not higher than 1.99%; moreover, it offered favorable self-temperature compensation. Such FBG acceleration sensors with high sensitivity response at low frequencies provide important reference for the design of like sensors. [ABSTRACT FROM AUTHOR]

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

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]

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

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

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

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]

Road abnormalities can be caused by man-made and natural disasters that affect the safety of drivers and damage vehicles. Therefore, several automatic road monitoring approaches have been proposed to monitor the road surface and detect road abnormalities like potholes. However, low accuracy in detecting the pothole in low-light conditions is taken as the main problem in this work. To address this issue, we presented an Improved Long Short Term Memory model (ILSTM) that combines a three-layer deep LSTM with a One-Dimensional Local Binary Pattern (1D-LBP) layer to detect the presence of potholes during low-light conditions and extract features such as the number of neighbouring samples and pixel values. The proposed strategy collects the pothole data from accelerometer and gyroscope sensor data using a smart phone. This model is used to classify the sensor data and label it either as a pothole or normal. Besides, this makes classification possible and extracts the location of the pothole. Evaluation results demonstrate that the proposed ILSTM approach is also robust to low lighting conditions with a detection accuracy of 99% and requires less execution time in classifying potholes and non-pothole regions on the pothole dataset collected with the help of an accelerometer and a gyroscope. [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]

Bradykinesia is a behavioral manifestation that contributes to functional dependencies in later life. However, the current state of bradykinesia indexing primarily relies on subjective, time-averaged categorizations of motor deficits, which often yield poor reliability. Herein, we used time-resolved analyses of accelerometer recordings during standardized movements, data-driven factor analyses, and linear mixed effects models (LMEs) to quantitatively characterize general, task- and therapy-specific indices of motor impairment in people with Parkinson’s disease (PwP) currently undergoing treatment for bradykinesia. Our results demonstrate that single-trial, accelerometer-based features of finger-tapping and rotational hand movements were significantly modulated by divergent therapeutic regimens. Further, these features corresponded well to current gold standards for symptom monitoring, with more precise predictive capacities of bradykinesia-specific declines achieved when considering kinematic features from diverse movement types together, rather than in isolation. Herein, we report data-driven, sample-specific kinematic profiles of diverse movement types along a continuous spectrum of motor impairment, which importantly, preserves the temporal scale for which biomechanical fluctuations in motor deficits evolve in humans. Therefore, this approach may prove useful for tracking bradykinesia-induced motor decline in aging populations the future. [ABSTRACT FROM AUTHOR]

The results of development and manufacture of an advanced device for seismic monitoring at industrial facilities are reported. The design and the principle of operation of the seismic monitoring device for nuclear power plants are described. The main results of trial operation, implementation, and application are presented. [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]

Telomere shortening is a biological aging hallmark. The effect of short telomere length may be targeted by increased physical activity to reduce the risk of multiple aging-related diseases, including coronary heart disease (CHD). The objective was to assess the moderation effect of accelerometer-based physical activity (aPA) on the association between shorter leukocyte telomere length (LTL) relatively in the population sample and incident CHD. Data were from the UK Biobank participants with well-calibrated accelerometer data for at least 6.5 days (n = 54,180). Relative mean LTL at baseline (5–6 years prior to aPA assessment) was measured in T/S ratio, using a multiplex quantitative polymerase chain reaction (qPCR) technology, by comparing the amount of the telomere amplification product (T) to that of a single-copy gene (S). aPA measures included total number of events (at least 10-s continued physical activity > 32 milligravities [mg]), total volume, mean duration, mean intensity, and peak intensity of all events. LTL, aPA measures, and their interactions were associated with incident CHD (mean follow-up 6.8 years) using Cox proportional hazards models adjusting for covariates. Longer LTL (relative to the sample distribution) was associated with reduced incidence of CHD (adjusted hazard ratio [aHR] = 0.94 per standard deviation [SD] increase in LTL, [95% CI, 0.90 to 0.99], P =.010). Incidence of CHD was reduced by higher total volume of aPA (aHR = 0.82 per SD increase in LTL, [95% CI, 0.71 to 0.95], P =.010) but increased by higher total number of events (aHR = 1.11 per SD increase in LTL, [95% CI, 1.02 to 1.21], P =.020) after controlling for other aPA measures and covariates. However, none of the interactions between LTL and aPA measures was statistically significant (P =.171). [ABSTRACT FROM AUTHOR]

In the article the parametric oscillations of an unreformed disk located in a contactless electromagnetic suspension with an electrostatic loop of the effective control stiffness were investigated analytically. The analytical expressions for the transition curves of the stationary position of the levitated object were obtained based on the asymptotical method of nonlinear dynamics for the areas of the main and secondary parametric resonances. The system parameters were estimated for which the contactless suspension with quasi-zero electromagnetic stiffness is asymptotically stable. [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to quantify the impact of concomitant meniscal lesions on knee laxity using a triaxial accelerometer in a large population of patients affected by anterior cruciate ligament (ACL) injury. Methods: A total of 326 consecutive patients (261 men and 65 women, mean age 31.3 ± 11.3) undergoing primary ACL reconstruction, were preoperatively evaluated through Lachman and pivot shift tests using a triaxial accelerometer to quantify knee laxity. An analysis based on the presence of meniscal tears assessed during surgery was performed to evaluate the impact of meniscal lesions on knee laxity. Results: The anterior tibial translation (Lachman test) presented significantly higher values in patients with medial meniscal lesions (7.3 ± 1.7 mm, p = 0.049) and both medial and lateral meniscal lesions (7.7 ± 1.6 mm, p = 0.001) compared to patients without concomitant meniscal lesions (6.7 ± 1.3 mm). Moreover, patients with both medial and lateral meniscal lesions presented significantly higher values of anterior tibial translation compared to patients with lateral meniscal lesions (p = 0.049). No statistically significant differences were found between the groups in terms of tibial acceleration (pivot shift test). Conclusion: This study demonstrated that the contribution of concomitant meniscal lesions to knee laxity can be objectively quantified using a triaxial accelerometer in ACL‐injured knees. In particular, medial meniscus lesions, alone or in association with lateral meniscus lesions, determine a significant increase of the anterior tibial translation compared to knees without meniscus tears. Level of Evidence: Level IV. [ABSTRACT FROM AUTHOR]

Abstract 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.

Assessment of ataxic movements is usually based on clinical judgment. Technical devices can be employed in the quantification of ataxic movements in addition to clinical evaluation. The effect of maximal speed in upper limb movements in ataxia patients has not been quantified. The aim was to quantify upper limb movements in patients with hereditary or idiopathic ataxia and to find features of movement that are characteristic for ataxia. We examined 19 patients with degenerative ataxia and 21 healthy controls. An ad hoc system comprising a touch screen, an accelerometer, and a gyroscope was used to measure speed, angular acceleration, consistency, and accuracy of upper limb movements. The movements were quantified during finger-to-nose test that the patients were asked to perform at their own pace and as fast as possible. Disease severity was estimated by using the Scale for the Assessment and Rating of Ataxia (SARA). The mean SARA score of the patients was 13.5. Compared to the controls the performance of the patients was slow (p < 0.001) and arrhythmic (p < 0.001), but end-point accuracy on the touch screen was intact. The SARA score correlated with the standard deviation of amplitude of angular acceleration in Z-axis (F(1,17) = 15.00, p < 0.001 with R2 = 0.47). Upper limb movements of the patients with degenerative ataxia were slower and more arrhythmic than those in the controls. The patients retained spatial end-point accuracy. [ABSTRACT FROM AUTHOR]

The objective of this study was to use a sensor-based accelerometer (ACC) to identify changes in lying, rumination, and activity times in weaned calves during the moving and regrouping process. Overall, 270 healthy Holstein calves (from approximately 16 regrouping events) at the age of approximately 4 months were enrolled and equipped with an ear-attached ACC (SMARTBOW, Smartbow GmbH/ Zoetis LLC). Sensor data were recorded for 5 d before (d -5) until 4 d after moving and regrouping (d 4). The day of regrouping was defined as d 0. Acceleration data were continuously processed by specific algorithms (developed by SMARTBOW) for lying, rumination, and activity. Lying, rumination, and activity times were averaged from d -5 to d -3 to generate a baseline value for each parameter. Parameters on d 0 to d 4 after regrouping were compared to this baseline. All parameters showed significant changes compared with the baseline at d 0. Significant decreases in rumination and inactive times were seen up to d 2. Lying time was significantly lower until d 3. The study results indicate that the ACC can be used to monitor the disruptive effects of regrouping on lying and rumination behaviors. Further research is necessary to elucidate how these changes have an impact on health, performance, and welfare and to evaluate how to reduce these potentially negative effects. [ABSTRACT FROM AUTHOR]

Introduction: Physical activity (PA) after metabolic and bariatric surgery (MBS) can influence weight loss, health status, and quality of life. Known mediators to participate in PA are enjoyment, self-efficacy, and social support. Little is known about PA behavior in MBS individuals. The aim of this study was to explore levels of PA and the PA mediators enjoyment, self-efficacy, and social support before and after MBS and to investigate changes over time. Methods: Adults scheduled to undergo MBS were recruited from a Swedish university hospital. Accelerometer-measured and self-reported PA, body weight, and PA mediators were collected at baseline and at 12 to 18 months post-surgery. Results: Among 90 individuals included, 50 completed the follow-up assessment and had valid accelerometer data. Sedentary time (minutes/day) was unchanged, but sedentary time as percentage of wear time decreased significantly from 67.2% to 64.5% (p<0.05). Time spent in light PA and total PA increased significantly from 259.3 to 288.7 min/day (p < 0.05) and from 270.5 to 303.5 min/day (p < 0.01), respectively. Step counts increased significantly from 6013 to 7460 steps/day (p < 0.01). There was a significant increase in self-reported PA, enjoyment, self-efficacy for exercise, and positive social support from family. The increase in PA mediators did not lead to a significant change in time spent in moderate to vigorous PA. Conclusion: The increase in PA-mediators was not associated with an increase in moderate to vigorous PA, but the strengthened PA mediators suggest potential for an increase in moderate to vigorous PA in patients undergoing MBS. [ABSTRACT FROM AUTHOR]

The accurate measurement of low-frequency signal is the main research focus at present. Aiming at the problem that FBG accelerometer is difficult to measure vibration signal efficiently, we propose a low-frequency FBG accelerometer based on dual mass. Build the vibration model of the sensor, derive the theoretical formula of the sensitivity and resonant frequency, and use COMSOL software to optimize the design and simulation of the key parameters. According to the theoretical analysis results of finite element verification, the sensor is manufactured and its performance is tested. The experimental results show that the transverse crosstalk of the sensor is − 28.07 dB, the relative standard deviation of repeatability is 0.90%, the fitting determination coefficient R2 is 99.98%, the sensitivity is 1194.91 pm/g, the dynamic range is 81.55 dB, the working frequency range is 1–40 Hz, and the inherent frequency is 58 Hz. In the low frequency band, the developed sensor exhibits good stability and sensitivity. This design provides a reference for the application of FBG accelerometer in low-frequency exploration. [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]

Paddler ability in kayaking is reflected through boat motion. Through the application of accelerometers, an easy to use and readily available assessment tool, the current study attempts to determine whether skill level can be ascertained through such measures in order to better assess performance and training. In all, 79 male canoe sprint kayakers participated in this study and paddled a 200 m straight course. According to the time they finished the course, they were divided into three skill level groups and labelled as slow, moderate, and fast speed. Tri-axial boat acceleration data were recorded using an accelerometer system mounted on the back of the kayak. The maximum and standard deviation of boat acceleration along superoinferior, mediolateral, and anteroposterior axes were compared among the three groups. Mixed analysis of variance (ANOVA) test revealed that maximum and standard deviation of acceleration data along the superoinferior axis were significantly greater in the fast speed group than in the moderate and slow ones, and higher in the moderate group than in slow one. These findings suggest that the kayaker's body movements will eventually be reflected in the boat's total acceleration, which could be a measure of overall performance. Given that accelerometers are a small, portable, and easy-to-use devices, and that the results of this study also revealed that boat acceleration differs between different skill groups, it appears that studying acceleration time series in real competition and outside of laboratory environments could open a new window into kayak research. [ABSTRACT FROM AUTHOR]

The study aims to assess the concurrent validity of the SENS motion® accelerometer system for device-based measurement of physical activity and sedentary behavior in healthy children and adolescents. Thirty-six healthy children and adolescents (mean ± standard deviation (SD) age, 10.2 ± 2.3 years) were fitted with three SENS sensors while performing standardized activities including walking, fast walking, sitting/lying, and arm movements. Data from the sensors were compared with video observations (reference criteria). The agreement between SENS motion® and observation was analyzed using Student's t-test and illustrated in Bland–Altman plots. The concurrent validity was further evaluated using intraclass correlation coefficient (ICC) and was expressed as standard error of measurement (SEM) and minimal detectable change (MDC). Strong agreement was found between SENS and observation for walking time, sedentary time, and lying time. In contrast, moderate agreement was observed for number of steps, sitting time, and time with and without arm movement. ICC2.1 values were overall moderate to excellent (0.5–0.94), with correspondingly low SEM% for walking time, sedentary time, lying time, and time with arm movement (2–9%). An acceptable SEM% level was reached for both steps and sitting time (11% and 12%). For fast walking time, the results showed a weak agreement between the measurement methods, and the ICC value was poor. Conclusion: SENS motion® seems valid for detecting physical activity and sedentary behavior in healthy children and adolescents with strong agreement and moderate to excellent ICC values. Furthermore, the explorative results on arm movements seem promising. What is Known: • Inactivity and sedentary behavior follow an increasing trend among children and adolescents. • SENS motion®seems to be valid for measuring physical activity and sedentary behavior in adults and elderly patients. What is New: • SENS motion®seems valid with strong agreement between video observations and SENS measurement, and ICC values are moderate to excellent when measuring physical activity and sedentary behavior in healthy children and adolescents. • SENS motion®seems promising for detection of arm movements. [ABSTRACT FROM AUTHOR]

The purpose of this study was to examine factors affecting physical activity (PA) and sedentary pursuits of children with autism spectrum disorder (ASD) throughout the week, during weekdays, and on weekend days. Sixty-eight children with ASD aged 6–17 years participated in this study. An ActiGraph accelerometer and activity log were used to assess PA and sedentary pursuits. The findings were (a) 6-8-year-old children with ASD demonstrating the highest level of overall PA throughout the week; (b) the weekends' PA of the group of 6-8-year-old children with ASD was significantly higher than 9-11-year-old, 12-14-year-old, and 15-17-year-old children with ASD; (c) the single-parented children with ASD showed significantly less PA level during weekend days compared with two-parented children with ASD; (d) the children with ASD having comorbidities demonstrated significantly more time in sedentary pursuits compared to those children without comorbidities; (e) age, household structure, and comorbidities were significant predictors in the overall PA, weekend PA, and weekend sedentary pursuits, respectively. Future programs or interventions are required to promote PA participation in children with ASD, especially for older children with ASD, based on their personal (i.e., age and comorbidities) and environmental (i.e., household structure) profile and day of the week. [ABSTRACT FROM AUTHOR]

Purpose: The deep breathing test (DBT) is a sensitive test of cardiovagal function. The aim of this study was to explore associations between physical activity and sedentary time, measured by accelerometer, and autonomic function, using DBT. Methods: In the Swedish Cardio-Pulmonary bioImage Study, men and women aged 50–64 were randomly invited from the general population. A total of 4325 subjects who underwent DBT and assessment of physical activity and sedentary time by accelerometery were included. ECG files from 1-min DBT were used to calculate measures of respiratory sinus arrhythmia [RSA; expiration–inspiration (E-I) difference and E/I ratio], heart rate variability [HRV; root mean square of successive differences (RMSSD), standard deviation of heart rates and mean circular resultant]. Low RSA and HRV was defined as the lowest 10% in the population. Results: For accelerometer-assessed physical activity, there were significant associations between high percentage of sedentary time and low E/I (p < 0.01), and low RMSSD (p < 0.01) in an age- and sex-adjusted model, and between percentage of sedentary time and low RMSSD (p = 0.04) in a risk factor-adjusted model. Low RMSSD was less common in those with a high percentage of moderate to vigorous physical activity (p = 0.04, after risk-factor adjustment). These associations became non-significant when further adjusting for heart rate. Conclusion: We report associations between degree of physical activity and indices of autonomic dysfunction in a large population. The relationships were no longer significant after adjustments for heart rate, indicating that the relationship between physical activity and cardiovagal function partly is accounted for by reduced heart rate. [ABSTRACT FROM AUTHOR]

The accelerometers and the angular velocity sensor, which are part of the meteorological complex of the ExoMars-2022 landing platform (LP), are designed to measure the acceleration during the deceleration of the lander in the Martian atmosphere. Based on the data, the main parameters of the Martian atmosphere are calculated: density, pressure, and temperature. After landing, the sensors are used to determine the acceleration on the surface and vibration effects on the lander of various nature. The sensors are activated prior to entry into the atmosphere and operate during the entire descent until landing. After landing, a long-term monitoring is carried out to identify vibration effects from the atmosphere and the surface. In the paper we consider the scientific problems solved by the sensors, describe the measurement program and consider in detail the design of the sensors and their characteristics. [ABSTRACT FROM AUTHOR]

Drop tower is an important facility for conducting microgravity experiments on the ground. Since the Beijing drop tower was put into operation in 2003, a large number of scientific experiments have been carried out in the fields of microgravity fluid physics, combustion, materials science and fundamental physics. The drop tower has two operation modes: single capsule falling and double capsule falling, with the microgravity levels reaching 10−2g ~ 10−3g and 10−5g respectively. In order to meet the demand of multi-level microgravity levels in ground microgravity experiments, a double capsule microgravity level control system based on the cold-gas micro thruster is developed. The micro thrusters are mounted in the inner capsule of the double capsule system. During the fall of the double capsule, the cold-gas micro thrusters thrust in the opposite direction of the movement of the structure, which increases the resistance of the inner capsule to realize the control of microgravity level. The measurement results of the high-precision accelerometer show that the 10–3 ~ 10−5g microgravity level can be controlled by this system. [ABSTRACT FROM AUTHOR]

Aim: This study compared indirect measurement of physical activity (PA) using the International Physical Activity Questionnaire-Short Form (IPAQ-SF) and direct using accelerometers in African American adults. Subject and methods: African Americans in a southeastern US metropolitan city were recruited to participate in a lifestyle intervention. Participants were predominantly female (79%) and spanned a 40-year age range (min = 25, max = 65). Participants completed the IPAQ-SF and wore an Actigraph GT1M accelerometer on the right hip for a minimum of 7 days, including 1 weekend day. IPAQ-SF data were reduced using IPAQ scoring protocol; accelerometer data were reduced using Freedson cutpoints. We calculated minutes/day of moderate, vigorous, and moderate to vigorous PA, and sedentary time. Descriptive statistics summarized minutes/day. Paired-sample t-tests and Bland–Altman plots compared differences between methods. Pearson correlations assessed associations between the methods. Results: In total, 158 participants completed the IPAQ-SF and 141 participants had usable accelerometer data. Participants reported significantly more vigorous and moderate to vigorous PA (12.6 ± 29.4 and 10.7 ± 39.1 min/day, respectively) and reported significantly less sedentary time (−189.6 ± 207.0 min/day) on the IPAQ-SF compared to the accelerometer. Correlations of interest ranged from 0.24 to 0.31. Bland–Altman analyses demonstrated measurement error of higher reporting of PA and lower reporting of sedentary time. Conclusions: Measurement differences were in expected directions. Both measures correlated similarly in our sample compared to other populations. Future studies may compare other indirect measures of PA with direct. Measurement method may influence relationships of physical activity or sedentary time on other outcomes in clinical and practice settings. [ABSTRACT FROM AUTHOR]

A multicomponent force balance was designed to measure the drag and rolling moment using an accelerometer-based technique. The force balance system used a linear ball bush as a new model mount system to minimize the constraint of the test model motion in both the axial and rotational directions. The accelerations of the test model were measured in the axial and rotational directions using accelerometers that were externally mounted on the test model. The drag and rolling moment were recovered from the measured accelerations using the system response functions, which included the dynamic characteristics of the force balance system. The system response functions were determined from the force balance calibration processes by applying a series of point loads in the axial and rotational directions and deconvolving the resulting accelerations. The drag and rolling moment measurements on the wedge model, including the flaps, were performed in a shock tunnel with a test time of approximately 3 ms at a nominal freestream Mach number of 6. A computational fluid dynamics (CFD) analysis assuming a laminar boundary layer was performed. Good agreement was obtained between the measured and calculated results. An uncertainty analysis of the measurements was conducted with regard to the influence of the fundamental properties of the test condition and force balance system. [ABSTRACT FROM AUTHOR]

Background: Phase angle (PhA) is an indicator of cellular conditions. Recent studies have suggested that PhA supports healthy aging. Identifying modifiable lifestyle factors for PhA is important. The associations of PhA in 24-h movement behaviors, including physical activity (PA), sedentary behavior (SB), and sleep, have not been studied in older adults. Objectives: We investigated the cross-sectional associations between 24-h movement behaviors and PhA in community-dwelling older adults while appropriately considering the co-dependent nature of daily time use using compositional data analysis. Methods: The participants were 113 healthy older adults. PhA was measured using a bioelectrical impedance device. Time spent in light-intensity PA (LPA), moderate- to vigorous-intensity PA (MVPA), and SB was measured using a tri-axial accelerometer. Sleep duration information was self-reported in a questionnaire. Compositional multiple linear regression and compositional isotemporal substitution were performed to examine the associations of 24-h movement behaviors with PhA and hypothetical time reallocation in movement behaviors with PhA, respectively. Results: Even after adjusting for potential confounders, relative to other behaviors more time spent in MVPA was significantly associated with higher PhA (p < 0.001). The 30 min/day of time reallocation from the other behaviors (SB, LPA, and sleep) to MVPA was predicted to be 0.12 higher PhA (corresponding to 2.3% increase; 95% CI 0.01, 0.24). Conclusions: Our results suggest that increasing or maintaining the daily time spent in MVPA is important for managing PhA in older adults, regardless of the other behaviors time consumed instead. [ABSTRACT FROM AUTHOR]

Accelerometers are widely used for tracking human movement and provide minute-level (or even 30 Hz level) physical activity (PA) records for detailed analysis. Instead of using day-level summary statistics to assess these densely sampled inputs, we implement functional principal component analysis (FPCA) approaches to study the temporal patterns of PA data from 245 overweight/obese women at three visits over a 1-year period. We apply longitudinal FPCA to decompose PA inputs, incorporating subject-specific variability, and then test the association between these patterns and obesity-related health outcomes by multiple mixed effect regression models. With the proposed methods, the longitudinal patterns in both densely sampled inputs and scalar outcomes are investigated and connected. The results show that the health outcomes are strongly associated with PA variation, in both subject and visit-level. In addition, we reveal that timing of PA during the day can impact changes in outcomes, a finding that would not be possible with day-level PA summaries. Thus, our findings imply that the use of longitudinal FPCA can elucidate temporal patterns of multiple levels of PA inputs. Furthermore, the exploration of the relationship between PA patterns and health outcomes can be useful for establishing weight-loss guidelines. [ABSTRACT FROM AUTHOR]

Background: Physical inactivity increases the risk of chronic disease and mortality. The high prevalence of physical inactivity in the UK is likely to increase financial pressure on the National Health Service. The UK Biobank Study offered an opportunity to assess the impact of physical inactivity on healthcare use and spending using individual-level data and objective measures of physical activity. The objective of this study was to assess the associations between objectively measured physical activity levels and future inpatient days and costs in adults in the UK Biobank study. Methods: We conducted an econometric analysis of the UK Biobank study, a large prospective cohort study. The participants (n = 86,066) were UK adults aged 43–79 who had provided sufficient valid accelerometer data. Hospital inpatient days and costs were discounted and standardised to mean monthly values per person to adjust for the variation in follow-up times. Econometric models adjusted for BMI, long-standing illness, and other sociodemographic factors. Results: Mean follow-up time for the sample was 28.11 (SD 7.65) months. Adults in the most active group experienced 0.037 fewer days per month (0.059–0.016) and 14.1% lower inpatient costs (– £3.81 [ – £6.71 to – £0.91] monthly inpatient costs) compared to adults in the least active group. The relationship between physical activity and inpatient costs was stronger in women compared to men and amongst those in the lowest income group compared to others. The findings remained significant across various sensitivity analyses. Conclusions: Increasing physical activity levels in the UK may reduce inpatient hospitalisations and costs, especially in women and lower-income groups. [ABSTRACT FROM AUTHOR]

The use of sensors for livestock monitoring has grown in the last decade, motivated not only by the search for improvements in animal production and health, but also by the better use of physical, financial, and human resources in the management of livestock. Since precision livestock farming depends on technological innovation, including the use of sensors, it is essential to validate their applicability in confined animals. Therefore, the objective was to evaluate the functioning of the SCR Heatime® HR System sensor in heifers and analyze the behavioral differences of these animals in two feedlot systems (tie-stall and loose-housing). Eleven Gyr heifers of 10 months of age and an average body weight of 179 ± 26 kg were used. Animals were equipped with the SCR Heatime® HR System 7 days before the evaluations for adaptation, with the behavioral and system studies carried out during two periods each of 5 days, for 8 h a day. Five heifers were housed in tie-stall bedding and six were housed in loose-housing. After the evaluations for a period, animals have been switched systems in the following period. While the animals had the sensors attached to their bodies, behavioral evaluations were also performed visually, every 3 min by two evaluators per system. Behavior activities were characterized as standing, lying, idle standing, idle lying, ruminating standing, ruminating lying, feed, and water intake. The regression for the rumination activity recorded between the sensor and the visual assessment was significant only for loose-housing (P = 0.0002), but the Pearson correlation between both was negative and low (r = − 0.25; P = 0.0002). The sensor overestimated rumination by 27.3% in loose-housing (28 vs. 22 min/2 h) and 38.5% in tie-stall (36 vs. 26 min/2 h). Thus, when considering the different feedlot systems, validation was not possible due to the overestimation of time spent on rumination activity determined by the sensor. In the behavioral comparison between the systems by visual assessment, there was a significant difference (P < 0.05) for the behaviors of motor activity, idle lying, total idle time, and feed intake between the animals in the two housing systems. As for idle standing, ruminating standing, total rumination time, and water intake, there was no difference (P > 0.05). However, the time spent ruminating lying was greater from 10:00 to 12:00 h (43 ± 3 min/2 h) in the tie-stall, whereas in the loose-housing the animals distribute more uniformly their rumination from 08:00 to 16:00 h (26 min/2 h on average). Therefore, the use of the SCR Heatime® HR System sensor is not recommended for Gyr heifers housed in tie-stall and loose-housing systems. Regarding the daytime range of rumination and the accuracy improvements of similar sensors, it is recommended to develop them in both feedlot systems using the periods of less rumination in early morning in tie-stall and late afternoon in loose-housing. [ABSTRACT FROM AUTHOR]

The present study is devoted to mathematical modeling of the proposed new architecture of a microelectromechanical mode-localized acceleration sensor (MEMS accelerometer/gravimeter) with a sensitive element in the form of a clamped-clamped microbeam with an initial curvature, made in the form of the first asymmetric mode of free oscillations. The article demonstrates that with an asymmetric form of the initial curvature in the region of positive axial forces, there are zones of proximity of the frequency branches corresponding to the second symmetric and the first asymmetric vibration modes. When the required value of the axial tensile force in the microbeam is provided structurally, this effect can be used, in particular, to measure the axial component of the transfer acceleration according to the principle of amplitude modal localization. The possibility of heating the sensitive element with the help of an electric current flowing through the microbeam, provided in the sensor layout, makes it possible to control the operating point of the oscillation mode and, thus, to vary the range of measured accelerations and the degree of sensor sensitivity within a very wide range. The configuration of the oscillation excitation and output signal pickup electrodes proposed in the article makes it possible, with the help of a feedback loop, to stabilize the oscillation amplitude at the required level in the working (third) symmetrical form and, at the same time, to measure the oscillation amplitude associated with the change in the value of the measured component of the portable acceleration according to the asymmetric form. Thus, a mathematical model of an original mode-localized accelerometer (gravimeter) containing a single sensitive microbeam element and involving the effect of energy exchange between its various modes of vibration is proposed and investigated in the article. [ABSTRACT FROM AUTHOR]

This study examined sleep and its cognitive and affective correlates in adults with and without autism spectrum disorder (ASD), utilizing UK Biobank data. There were no group differences in subjective sleep duration [n = 220 ASD; n = 2200 general population (GP)]. Accelerometer measures of sleep duration or nighttime activity did not differ by group, but sleep efficiency was marginally lower in ASD (n = 83 ASD; n = 824 GP). Sleep efficiency was associated with wellbeing and mental health, and pathways between accelerometer sleep measures and wellbeing and mental health were significantly stronger for adults with ASD (who also reported substantially poorer wellbeing and > 5 × likelihood of experiencing mental distress). These findings highlight the need to monitor sleep to maintain good mental health in adult ASD. [ABSTRACT FROM AUTHOR]

Structural health monitoring (SHM) is important in ensuring safety of large civil engineering structures. Global Navigation Satellite Systems (GNSS)-based technology has been commonly used in SHM systems due to its unique ability to obtain real-time 3D displacement information. GNSS-based SHM systems are often integrated with other sensors (e.g., accelerometers) to take advantage of the complementary natures of different technologies and to enhance the overall performance of the integrated systems (e.g., more accurate displacement information). The conventional multi-rate Kalman filter (conventional MRKF) has been frequently used to integrate data from different technologies as the data sampling rate of a GNSS system is usually much lower than that of most other sensors. It is, however, well known that GNSS observations often contain outliers that can significantly reduce the accuracy and reliability of either the GNSS-only or integrated SHM systems. We propose a new robust multi-rate Kalman filter-based approach to integrate observations from GNSS and other sensors more robustly. The approach allows for the first time to mitigate the influence of outliers in the observations of an integrated GNSS and accelerometer SHM system, especially those in the GNSS observations in a real-time mode, which is critically important for an SHM system. Extensive experiments with data from a shaking table and from Stonecutters Bridge, a large cable-stayed bridge in Hong Kong, captured during a strong typhoon have demonstrated that the proposed method can reduce the effects of outliers effectively. The shaking table test has shown that when the method was used with an integrated GNSS and accelerometer system and outliers were present in the observations, the overall accuracy of the system was increased by up to about 70% compared with a GNSS-only system and up to about 44% higher compared to the same integrated system, but the data were processed with the conventional MRKF. It should be noted that the improvements depend on the number and magnitudes of the outliers in the observations. From the frequency perspective, the new method can capture a much broader band and more accurate vibration frequency. [ABSTRACT FROM AUTHOR]

Smartphone embedded sensors have created unprecedented opportunities to study human behavior in natural conditions through continuous mobile sensing. However, continuous mobile sensing poses critical energy challenge to smartphone's daily usage. There is an urgent need to enhance energy efficiency of mobile sensing applications while capture sufficient data to accurately predict user state. In this work, we propose an adaptive passive sensing framework to control low-level sensing cycles using an off-policy reinforcement learning (RL) algorithm namely Q-learning with linear approximation and decaying exploration. We propose two different formulations to meet different energy efficiency demands with various designs in their state spaces, action spaces, and reward signals. Using real continuous mobile sensing data from 220 participants for more than 2 weeks, we show consistently better performances on energy saving for our proposed RL strategies when compared to four different baseline methods. To verify the impacts of our proposed strategies on data utility, we predict social anxiety and daily negative affect using active data collected during the same study window. Our proposed RL strategies show equivalent prediction performance when compared to the baseline strategies and continuous sensing. [ABSTRACT FROM AUTHOR]

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The Bender-type constellation has become a feasible option of the next-generation gravimetric mission (NGGM) for improving the quality of temporal gravity field solutions. In the context of Bender-type NGGM, the payload requirement is limited to approximately 20 nm/Hz1/2 at the measurement bandwidth (MBW) of 0.01–1 Hz for laser ranging instrument (LRI), and 10–11 m/s2 at 0.001–0.1 Hz for accelerometer (ACC). Low-frequency noise is also, respectively, limited to 1/f and 1/f2 for LRI and ACCs due to unavoidable electronic noise. However, due to the complex payload manufacture procedures and volatile space observation environment, in reality, LRI or ACCs noise may not be rigorously consistent with the designed noise models in frequency domain. Frequency-dependent noise in in situ observations always results in different MBW boundaries or different low-frequency features. In this study, the potential impacts of this unaccounted frequency-dependent instrument noise are analyzed via various detailed simulations, and the conclusions are summarized as follows. (1) In the instrument noise only scenarios, a similar behavior is shown between the frequency spectrum of instrument’s frequency-dependent noise in terms of amplitude spectral density and the corresponding gravity solution in terms of geoid height error. (2) The ACC MBWs seriously affect the quality of gravity solution, while the impacts of low-frequency features (e.g., 1/f3/2 and 1/f2 behavior of LRI noise, 1/f5 and 1/f6 behavior of ACC noise) are quite minor. (3) Frequency-dependent instrument noise plays less role when the background force model errors are included, and significant improvement is observed when the atmospheric- and oceanic-induced aliasing error is excluded, indicating the necessity of accurate background force models as well as proper de-aliasing strategy. (4) In the full noise contaminated scenarios, to achieve the scientific goal of Bender-type NGGM, it is feasible to shift the low MBW boundary from 0.001 to 0.004 Hz for ACC, and from 0.01 to 0.1 Hz for LRI. The results are helpful to specify the requirement of manufacturing key payloads for the Bender-type NGGM. [ABSTRACT FROM AUTHOR]

Arctic-nesting geese face energetic challenges during spring migration, including ecological barriers and weather conditions (e.g., precipitation and temperature), which in long-lived species can lead to a trade-off to defer reproduction in favor of greater survival. We used GPS location and acceleration data collected from 35 greater white-fronted geese of the North American midcontinent and Greenland populations at spring migration stopovers, and novel applications of Bayesian dynamic linear models to test daily effects of minimum temperature and precipitation on energy expenditure (i.e., overall dynamic body acceleration, ODBA) and proportion of time spent feeding (PTF), then examined the daily and additive importance of ODBA and PTF on probability of breeding deferral using stochastic antecedent models. We expected distinct responses in behavior and probability of breeding deferral between and within populations due to differences in stopover area availability. Time-varying coefficients of weather conditions were variable between ODBA and PTF, and often did not show consistent patterns among birds, indicating plasticity in how individuals respond to conditions. An increase in antecedent ODBA was associated with a slightly increased probability of deferral in midcontinent geese but not Greenland geese. Probability of deferral decreased with increased PTF in both populations. We did not detect any differentially important time periods. These results suggest either that movements and behavior throughout spring migration do not explain breeding deferral or that ecological linkages between bird decisions during spring and subsequent breeding deferral were different between populations and across migration but occurred at different time scales than those we examined. [ABSTRACT FROM AUTHOR]

The ownership of user actions in computer and mobile applications is an important concern, especially when using shared devices. User identification using physical biometric authentication methods permits the actual user to access the device. However, in cases where different users may access a shared device during the same active session, the person who owns the active session will be accountable for any actions performed by the other users. Thus, user identification using behavioral characteristics has come into the picture. Human activity recognition from mobile sensor data is gaining more interest with the advent of mobile devices and the emergence of the Internet of Things, where different applications such as elderly health monitoring, athletic evaluation, and context-aware behavior are being developed. In this paper, we show how human activity data can be utilized to identify the actual device user. We build deep learning models that are capable of identifying the users of mobile and wearable devices based on their body movements and daily activities. We use the Long Short-Term Memory classifier for building the user identification model based on time-series data from mobile motion sensors. The model targets the users that were involved in the training process. We tested our approach on two publicly available human activity datasets that contain daily activities and fall states data from accelerometer and gyroscope mobile sensors. The results show that the models are capable of identifying the actual mobile device users from their motion data with an accuracy of up to 90%. Further, the results show that the model from the accelerometer data outperforms the one from gyroscope data. [ABSTRACT FROM AUTHOR]

Psychological well-being is linked to healthy aging in older women, but associations with health behaviors are not well understood. Our study aims to evaluate the relationships between objectively-measured physical behavior (including physical activity and sedentary behavior) with optimism and positive affect in a diverse sample of older women. Our cross-sectional study of 4168 American women (aged 63–99) with accelerometer-measured physical behavior from the Objective Physical Activity and Cardiovascular Health Study assessed associations using multiple linear regression. Effect modification by age, race and ethnicity, social support, and number of chronic conditions was examined as well. In unadjusted models, positive associations for physical activity and negative associations for sedentary behaviors were generally linear for optimism and positive affect. In adjusted models, every one-hour increment in weekly moderate-vigorous physical activity was associated with higher optimism by 0.4 score points [Revised 6-item Life Orientation Test, 95% CI = 0.2, 0.6] and positive affect by 0.6 score points [modified Differential Emotions Scale, 95% CI = 0.2, 0.9]. One-hour increments in light physical activity were associated with higher positive affect [0.2 score points; 95% CI = 0.03, 0.33] while one-hour increments in sedentary behavior patterns were associated with lower positive affect [-0.1 score points; 95% CI = − 0.10, − 0.02). Effect modification by age, race and ethnicity, social support, and number of chronic conditions was not observed. In conclusion, associations between physical behavior with optimism and positive affect were modest but suggest greater activity and less sedentary time are associated with greater psychological well-being in older women. [ABSTRACT FROM AUTHOR]

In this paper, a high-precision accelerometer based on single-level diffraction of a single grating is optimized and improved. The sensor consists of an optical system displacement measurement system using grating interferometry and a mechanical structural part made of silicon. The sensor is analyzed by finite element analysis (FEA) and strictly coupled wave analysis methods. The optical accelerometer has an optical sensitivity of 2.21%/μm over the entire measurement range, and total sensitivity of system is 424%/g (note: 1 g = 9.8 m/s2) with and a cross sensitivity of 3.21%. Furthermore, the first-order resonant frequency is 35.995 Hz and the linear measurement range is ± 0.0177 g. Compared with other low-frequency sensors, a high-performance accelerometer sensor is realized. And can be applied to high-tech fields such as inertial navigation and guidance system, space ocean gravity distribution measurement, and artificial intelligence. [ABSTRACT FROM AUTHOR]