Association of accelerometer-derived physical activity with all-cause and cause-specific mortality among individuals with cardiovascular diseases: A prospective cohort study.

AIMS: To investigate the association of accelerometer-measured intensity-specific physical activity (PA) with all-cause and cause-specific mortality among individuals with cardiovascular disease (CVD). METHODS: In this prospective cohort study, 8,024 individuals with pre-existing CVD (mean age: 66.6 years, female: 34.1%) from the UK Biobank had their PA measured using wrist-worn accelerometers over a 7-day period in 2013-2015. All-cause, cancer, and CVD mortality was ascertained from death registries. Cox regression modelling and restricted cubic splines were used to assess the associations. Population-attributable fractions (PAFs) were used to estimate the proportion of preventable deaths if more PA were undertaken. RESULTS: During an average of 6.8 years of follow-up, 691 deaths (273 from cancer and 219 from CVD) were recorded. An inverse non-linear association was found between PA duration and all-cause mortality risk, irrespective of PA intensity. The hazard ratio (HR) of all-cause mortality plateaued at 1800 minutes/week for light-intensity PA (LPA), 320 minutes/week for moderate-intensity PA (MPA) and 15 minutes/week for vigorous-intensity PA (VPA). The highest quartile of PA associated lower risks for all-cause mortality, with HRs of 0.63 (95% confidence interval [CI]: 0.51-0.79), 0.42 (0.33-0.54) and 0.47 (0.37-0.60) for LPA, MPA, and VPA, respectively. Similar associations were observed for cancer and CVD mortality. Additionally, the highest PAF were noted for VPA, followed by MPA. CONCLUSION: We found an inverse non-linear association between all intensities of PA (LPA, MPA, VPA, and MVPA) and mortality risk in CVD patients using accelerometer-derived data, but with larger magnitude of the associations than that in previous studies based on self-reported PA.

This study investigated the associations of accelerometer-derived intensity-specific physical activity (PA) with the risks of all-cause and cause-specific mortality among individuals with cardiovascular disease (CVD). L-shaped dose-response relationships between the duration of PA and all-cause mortality were observed across all levels of PA intensities. The risk reduction for mortality exhibited a sharp decline from 0 to 1800 minutes/week of light-intensity PA, followed by reaching a plateau. Notably, the inflection points for moderate-intensity PA and vigorous-intensity PA were found at 320 and 15 minutes per week, respectively. The population-attributable fraction analysis indicated that a significant number of deaths could potentially be prevented if individuals with CVD engaged in more vigorous physical activities.

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study.

BACKGROUND: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity. OBJECTIVE: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals. METHODS: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition. RESULTS: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs. CONCLUSIONS: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

Device-measured physical activity and cardiac structure by magnetic resonance.

BACKGROUND AND AIMS: Although extreme cardiac adaptions mirroring phenotypes of cardiomyopathy have been observed in endurance athletes, adaptions to high levels of physical activity within the wider population are under-explored. Therefore, in this study, associations between device-measured physical activity and clinically relevant cardiac magnetic resonance volumetric indices were investigated. METHODS: Individuals without known cardiovascular disease or hypertension were included from the UK Biobank. Cardiac magnetic resonance data were collected between 2015 and 2019, and measures of end-diastolic chamber volume, left ventricular (LV) wall thickness, and LV ejection fraction were extracted. Moderate-to-vigorous-intensity physical activity (MVPA), vigorous-intensity physical activity (VPA), and total physical activity were assessed via wrist-worn accelerometers. RESULTS: A total of 5977 women (median age and MVPA: 62 years and 46.8 min/day, respectively) and 4134 men (64 years and 49.8 min/day, respectively) were included. Each additional 10 min/day of MVPA was associated with a 0.70 [95% confidence interval (CI): 0.62, 0.79] mL/m2 higher indexed LV end-diastolic volume (LVEDVi) in women and a 1.08 (95% CI: 0.95, 1.20) mL/m2 higher LVEDVi in men. However, even within the top decile of MVPA, LVEDVi values remained within the normal ranges [79.1 (95% CI: 78.3, 80.0) mL/m2 in women and 91.4 (95% CI: 90.1, 92.7) mL/m2 in men]. Associations with MVPA were also observed for the right ventricle and the left/right atria, with an inverse association observed for LV ejection fraction. Associations of MVPA with maximum or average LV wall thickness were not clinically meaningful. Results for total physical activity and VPA mirrored those for MVPA. CONCLUSIONS: High levels of device-measured physical activity were associated with cardiac remodelling within normal ranges.

Lower physical activity levels in youth with Cystic Fibrosis compared to healthy controls: A multicentre comparative study.

BACKGROUND: Regular physical activity (PA) offers significant health benefits on both short (i.e., emotional well-being) and long term (i.e., fewer hospitalizations) in Youth with Cystic Fibrosis (YwCF). Regardless, evidence on PA levels in YwCF compared to healthy controls (HC) is inconsistent. Additionally, PA is a multidimensional outcome influenced by several factors such as Quadriceps strength and functional performance. Therefore, we aimed to assess whether PA, Quadriceps strength and functional performance differ between YwCF and HC across different age groups (i.e., children and adolescents). METHODS: YwCF aged 6-17 from two Belgian CF centres and age- and sex-matched HC were recruited. PA was measured with an ActiGraph GT3X + BT during 7 consecutive days. Isometric Quadriceps strength was assessed with a Hand Held Dynamometer and functional performance with a sit-to stand test (STS) and standing long jump (SLJ). RESULTS: A total of 49 YwCF (44 % male; 11.3 ± 3.3 years) and 49 HC (48 % male; 11.9 ± 3.5 years) were included. On average days, YwCF performed 4 ± 6.4 min less light PA and 7.5 ± 6.7 min less moderate-to-vigorous PA compared to HC (p = 0.04; p = 0.01). The differences in moderate-to-vigorous PA seem more pronounced in children (6-11 years)(p = 0.04). Furthermore, YwCF had similar Quadriceps strength to HC but had lower scores on the STS and SLJ (p = 0.50, p = 0.08; p = 0.02). CONCLUSIONS: This study shows lower PA levels and functional performance for YwCF, indicating that there is an urgent need for interventions promoting PA in YwCF. PA promotion will become increasingly important in the post modulator area to prevent health risks associated with low PA.

Associations of 24-hour movement behaviors with depressive symptoms in rural-dwelling older adults: a compositional data analysis.

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.

Association between "weekend warrior" physical activity and the incidence of neurodegenerative diseases.

While guidelines recommend 150 â€‹min of moderate to vigorous physical activity (MVPA) weekly to enhance health, it remains unclear whether concentrating these activities into 1-2 days of the week, "weekend warrior" (WW) pattern, has the same benefit for neurodegenerative diseases (NDDs). This study aimed to evaluate the associations of WW pattern and the risk of NDDs. This prospective study was conducted using accelerometer-based physical activity data for a full week from June 2013 to December 2015 in the UK Biobank. These individuals were categorized into distinct physical activity patterns, including the WW pattern (i.e., over 50% or 75% of recommended MVPA achieved over 1-2 days), regular pattern, and inactive pattern. Cox proportional hazards model was used to evaluate the association between physical activity patterns and outcomes. Compared to inactive group, WW pattern and regular pattern was similarly linked to a reduced risk of all-cause dementia (WW: Hazard Ratio [HR]: 0.68, 95% Confidence Interval [CI]: 0.56-0.84; regular: HR: 0.86, 95% CI: 0.67-1.1) and all-cause Parkinsonism (WW: HR: 0.47, 95% CI: 0.35-0.63; regular: HR: 0.69, 95% CI: 0.5-0.95). When the exercise threshold was increased to 75% of MVPA, both patterns still were associated with decreased risk of incident all-cause dementia (WW: HR: 0.61, 95% CI: 0.41-0.91; regular: HR: 0.76, 95% CI: 0.63-0.92) and all-cause Parkinsonism (WW: HR: 0.22, 95% CI: 0.10-0.47; regular: HR: 0.59, 95% CI: 0.46-0.75). Concentrating recommended physical activities into 1-2 days per week is associated with a lower incidence of NDDs.

Leveraging feature selection for enhanced fall risk prediction in elderly using gait analysis.

There is no effective fall risk screening tool for the elderly that can be integrated into clinical practice. Developing a system that can be easily used in primary care services is a current need. Current studies focus on the use of multiple sensors or activities to achieve higher accuracy. However, multiple sensors and activities reduce the availability of these systems. This study aims to develop a system to perform fall prediction for the elderly by using signals recorded from a single sensor during a short-term activity. A total of 168 features in the time and frequency domains were created using acceleration signals obtained from 71 elderly people. The features were weighted based on the ReliefF algorithm, and the artificial neural networks model was developed using the most important features. The best classification result was obtained using the 17 most important features of those weighted for K = 20 nearest neighbors. The highest accuracy was 82.2% (82.9% Sensitivity, 81.6% Specificity). The partially high accuracy obtained in our study shows that falling can be detected early with a sensor and a simple activity by determining the right features and can be easily applied in the assessment of the elderly during routine follow-ups.

Exploring Associations Between Device-Based Occupational Sedentary Behavior and Need for Recovery in White Collar Workers: A Compositional Data-Analysis.

Objectives: White collar workers spend an increasing amount of time in occupational sedentary behavior (OSB) and are thereby at risk for adverse health outcomes. Nevertheless, the association between OSB and the need for recovery (NFR), an important indicator of wellbeing, is unknown and therefore examined. Methods: Baseline data from a cluster randomized controlled trial was used. A subgroup of 89 white collar workers wore a triaxial accelerometer for 7 days. NFR was measured using the Questionnaire on the Experience and Evaluation of Work. Compositional data analysis was applied to determine the composition of different OSB bouts (short, medium and long) and occupational physical activity (OPA) (light, moderate and vigorous and standing). Linear regression analyses were performed to explore the associations between occupational compositions and NFR. Results: Relatively more time spent in long OSB bouts was associated with a lower NFR (ß: -11.30, 95% CI: -20.2 to -2.4). Short and medium OSB bouts and OPA were not associated with NFR. Conclusion: Associations between OSB bouts, OPA and NFR hinted at contrasting trends, suggesting the need to consider different bout lengths of OSB in future studies.

Identifying prey capture events of a free-ranging marine predator using bio-logger data and deep learning.

Marine predators are integral to the functioning of marine ecosystems, and their consumption requirements should be integrated into ecosystem-based management policies. However, estimating prey consumption in diving marine predators requires innovative methods as predator-prey interactions are rarely observable. We developed a novel method, validated by animal-borne video, that uses tri-axial acceleration and depth data to quantify prey capture rates in chinstrap penguins (Pygoscelis antarctica). These penguins are important consumers of Antarctic krill (Euphausia superba), a commercially harvested crustacean central to the Southern Ocean food web. We collected a large data set (n = 41 individuals) comprising overlapping video, accelerometer and depth data from foraging penguins. Prey captures were manually identified in videos, and those observations were used in supervised training of two deep learning neural networks (convolutional neural network (CNN) and V-Net). Although the CNN and V-Net architectures and input data pipelines differed, both trained models were able to predict prey captures from new acceleration and depth data (linear regression slope of predictions against video-observed prey captures = 1.13; R 2 ≈ 0.86). Our results illustrate that deep learning algorithms offer a means to process the large quantities of data generated by contemporary bio-logging sensors to robustly estimate prey capture events in diving marine predators.

An Effective Deep Learning Framework for Fall Detection: Model Development and Study Design.

BACKGROUND: Fall detection is of great significance in safeguarding human health. By monitoring the motion data, a fall detection system (FDS) can detect a fall accident. Recently, wearable sensors-based FDSs have become the mainstream of research, which can be categorized into threshold-based FDSs using experience, machine learning-based FDSs using manual feature extraction, and deep learning (DL)-based FDSs using automatic feature extraction. However, most FDSs focus on the global information of sensor data, neglecting the fact that different segments of the data contribute variably to fall detection. This shortcoming makes it challenging for FDSs to accurately distinguish between similar human motion patterns of actual falls and fall-like actions, leading to a decrease in detection accuracy. OBJECTIVE: This study aims to develop and validate a DL framework to accurately detect falls using acceleration and gyroscope data from wearable sensors. We aim to explore the essential contributing features extracted from sensor data to distinguish falls from activities of daily life. The significance of this study lies in reforming the FDS by designing a weighted feature representation using DL methods to effectively differentiate between fall events and fall-like activities. METHODS: Based on the 3-axis acceleration and gyroscope data, we proposed a new DL architecture, the dual-stream convolutional neural network self-attention (DSCS) model. Unlike previous studies, the used architecture can extract global feature information from acceleration and gyroscope data. Additionally, we incorporated a self-attention module to assign different weights to the original feature vector, enabling the model to learn the contribution effect of the sensor data and enhance classification accuracy. The proposed model was trained and tested on 2 public data sets: SisFall and MobiFall. In addition, 10 participants were recruited to carry out practical validation of the DSCS model. A total of 1700 trials were performed to test the generalization ability of the model. RESULTS: The fall detection accuracy of the DSCS model was 99.32% (recall=99.15%; precision=98.58%) and 99.65% (recall=100%; precision=98.39%) on the test sets of SisFall and MobiFall, respectively. In the ablation experiment, we compared the DSCS model with state-of-the-art machine learning and DL models. On the SisFall data set, the DSCS model achieved the second-best accuracy; on the MobiFall data set, the DSCS model achieved the best accuracy, recall, and precision. In practical validation, the accuracy of the DSCS model was 96.41% (recall=95.12%; specificity=97.55%). CONCLUSIONS: This study demonstrates that the DSCS model can significantly improve the accuracy of fall detection on 2 publicly available data sets and performs robustly in practical validation.

Smartphone GPS-Based Exposure to Greenspace and Walkability and Accelerometer-Assessed Physical Activity During Pregnancy and Early Postpartum-Evidence from the MADRES Cohort.

A growing number of studies have associated walkability and greenspace exposure with greater physical activity (PA) in women during pregnancy. However, most studies have focused on examining women's residential environments and neglected exposure in locations outside the home neighborhood. Using 350 person-days (N = 55 participants) of smartphone global positioning system (GPS) location and accelerometer data collected during the first and third trimesters and 4-6 months postpartum from 55 Hispanic pregnant women from the Maternal and Developmental Risks from Environmental and Social Stressors (MADRES) study, we examined the day-level effect of women's exposure to walkability and greenspace on their PA outcomes during pregnancy and in the early postpartum period. Moderate-to-vigorous physical activity [MVPA] minutes per day was assessed using accelerometers. Walkability and greenspace were measured using geographic information systems (GIS) within women's daily activity spaces (i.e., places visited and routes taken) recorded using a smartphone GPS and weighted by time spent. We used a generalized linear mixed-effects model to estimate the effects of daily GPS-derived environmental exposures on day-level MVPA minutes. Results showed that women engaged in 23% more MVPA minutes on days when they had some versus no exposure to parks and open spaces in activity spaces (b = 1.23; 95%CI: 1.02-1.48). In addition, protective effects of daily greenspace and walkability exposure on MVPA were stronger in the first and third trimesters, among first-time mothers, and among women who had high pre-pregnancy body mass index (BMI) and lived in least-safe neighborhoods. Our results suggest that daily greenspace and walkability exposure are important for women's PA and associated health outcomes during pregnancy and early postpartum.

A standardised approach to quantifying activity in domestic dogs.

Objective assessment of activity via accelerometry can provide valuable insights into dog health and welfare. Common activity metrics involve using acceleration cut-points to group data into intensity categories and reporting the time spent in each category. Lack of consistency and transparency in cut-point derivation makes it difficult to compare findings between studies. We present an alternative metric for use in dogs: the acceleration threshold (as a fraction of standard gravity, 1 g = 9.81 m/s2) above which the animal's X most active minutes are accumulated (MXACC) over a 24-hour period. We report M2ACC, M30ACC and M60ACC data from a colony of healthy beagles (n = 6) aged 3-13 months. To ensure that reference values are applicable across a wider dog population, we incorporated labelled data from beagles and volunteer pet dogs (n = 16) of a variety of ages and breeds. The dogs' normal activity patterns were recorded at 200 Hz for 24 hours using collar-based Axivity-AX3 accelerometers. We calculated acceleration vector magnitude and MXACC metrics. Using labelled data from both beagles and pet dogs, we characterize the range of acceleration outputs exhibited enabling meaningful interpretation of MXACC. These metrics will help standardize measurement of canine activity and serve as outcome measures for veterinary and translational research.

Device-measured sedentary time and intensity-specific physical activity in relation to all-cause and cardiovascular disease mortality: the UK Biobank cohort study.

BACKGROUND AND AIMS: Understanding the amounts of intensity-specific movement needed to attenuate the association between sedentary time and mortality may help to inform personalized prescription and behavioral counselling. Herein, we examined the joint associations of sedentary time and intensity-specific physical activity with all-cause and cardiovascular disease (CVD) mortality. METHODS: Prospective cohort study including 73,729 adults from the UK Biobank who wore an Axivity AX3 accelerometer on their dominant wrist for at least 3 days, being one a weekend day, between June 2013 and December 2015. We considered the median tertile values of sedentary time and physical activity in each intensity band to determine the amount of physical activity needed to attenuate the association between sedentary time and mortality. RESULTS: During a median of 6.9 years of follow-up (628,807 person-years), we documented 1521 deaths, including 388 from CVD. Physical activity of any intensity attenuated the detrimental association of sedentary time with mortality. Overall, at least a median of 6 min/day of vigorous physical activity, 30 min/day of MVPA, 64 min/day of moderate physical activity, or 163 min/day of light physical activity (mutually-adjusted for other intensities) attenuated the association between sedentary time and mortality. High sedentary time was associated with higher risk of CVD mortality only among participants with low MVPA (HR 1.96; 95% CI 1.23 to 3.14). CONCLUSIONS: Different amounts of each physical activity intensity may attenuate the association between high sedentary time and mortality.

Accelerometer Thresholds for Estimating Physical Activity Intensity Levels in Infants: A Preliminary Study.

Lack of physical activity (PA) at a young age can result in health issues. Thus, monitoring PA is important. Wearable accelerometers are the preferred tool to monitor PA in children. Validated thresholds are used to classify activity intensity levels, e.g., sedentary, light, and moderate-to-vigorous, in ambulatory children. No previous work has developed accelerometer thresholds for infancy (pre-ambulatory children). Therefore, this work aims to develop accelerometer thresholds for PA intensity levels in pre-ambulatory infants. Infants (n = 10) were placed in a supine position and allowed free movement. Their movements were synchronously captured using video cameras and accelerometers worn on each ankle. The video data were labeled by activity intensity level (sedentary, light, and moderate-to-vigorous) in two-second epochs using observational rating (gold standard). Accelerometer thresholds were developed for acceleration and jerk using two optimization approaches. Four sets of thresholds were developed for dual (two ankles) and for single-worn (one ankle) accelerometers. Of these, for a typical use case, we recommend using acceleration-based thresholds of 1.00 m/s to distinguish sedentary and light activity and 2.60 m/s to distinguish light and moderate-to-vigorous activity. Acceleration and jerk are both suitable for measuring PA.

Evaluation of the Diagnostic Sensitivity of Digital Vibration Sensors Based on Capacitive MEMS Accelerometers.

In recent years, there has been an increasing use of digital vibration sensors that are based on capacitive MEMS accelerometers for machine vibration monitoring and diagnostics. These sensors simplify the design of monitoring and diagnostic systems, thus reducing implementation costs. However, it is important to understand how effective these digital sensors are in detecting rolling bearing faults. This article describes a method for determining the diagnostic sensitivity of diagnostic parameters provided by commercially available vibration sensors based on MEMS accelerometers. Experimental tests were conducted in laboratory conditions, during which vibrations from 11 healthy and faulty rolling bearings were measured using two commercial vibration sensors based on MEMS accelerometers and a piezoelectric accelerometer as a reference sensor. The results showed that the diagnostic sensitivity of the parameters depends on the upper-frequency band limit of the sensors, and the parameters most sensitive to the typical fatigue faults of rolling bearings are the peak and peak-to-peak amplitudes of vibration acceleration. Despite having a lower upper-frequency range compared to the piezoelectric accelerometer, the commercial vibration sensors were found to be sensitive to rolling bearing faults and can be successfully used in continuous monitoring and diagnostics systems for machines.

Circular SAW Resonators: Influence of Sensitive Element Dimensions on Strength Characteristics and First Experimental Samples.

In preceding research endeavors, the frequency characteristics of a ring resonator on surface acoustic waves made of various materials were studied. Investigations encompassed fixation techniques within the housing, the impact of external variables on these components, and the most efficient configuration of the interdigital transducer within the ring resonator to curtail bandwidth. This current study is dedicated to investigating the correlation between sensitivity and the highest measurable acceleration concerning the dimensions of these sensitive elements. Furthermore, it involves assessing the attributes of produced experimental samples to verify the simulation results. The results obtained represent the possibility of creating a micromechanical accelerometer that can be used in the automotive industry as a g-sensor shock, as well as in industries where the numerical value of high overloads is required.

A Positioning Alarm System for Explosive Impact Debris Protective Suit Based on an Accelerometer Array.

Protection suits are vital for firefighters' safety. Traditional protection suits physically protect firemen from burns, but cannot locate the position of bodily injuries caused by impact debris. Herein, we present a wearable impact debris positioning system for firefighter protection suits based on an accelerometer array. Wearable piezoelectric accelerometers are distributed regularly on the suit to detect the vibration on different body parts, which is conducive to determining the position of injured body parts. In addition, the injured parts can be displayed on a dummy body model on the upper computer with a higher localization accuracy of 4 cm. The positioning alarm system has a rapid response time of 0.11 ms, attributed to the smart signal processing method. This work provides a reliable and smart method for locating and assessing the position of bodily injuries caused by impact debris, which is significant because it enables fire commanders to rescue injured firefighters in time.

Temperature Compensation for MEMS Accelerometer Based on a Fusion Algorithm.

This study proposes a fusion algorithm based on forward linear prediction (FLP) and particle swarm optimization-back propagation (PSO-BP) to compensate for the temperature drift. Firstly, the accelerometer signal is broken down into several intrinsic mode functions (IMFs) using variational modal decomposition (VMD); then, according to the FE algorithm, the IMF signal is separated into mixed components, temperature drift, and pure noise. After that, the mixed noise is denoised by FLP, and PSO-BP is employed to create a model for temperature adjustment. Finally, the processed mixed noise and the processed IMFs are rebuilt to obtain the enhanced output signal. To confirm that the suggested strategy works, temperature experiments are conducted. After the output signal is processed by the VMD-FE-FLP-PSO-BP algorithm, the acceleration random walk has been improved by 23%, the zero deviation has been enhanced by 24%, and the temperature coefficient has been enhanced by 92%, compared with the original signal.

A Low-Power and Robust Micromachined Thermal Convective Accelerometer.

This paper presents a micromachined thermal convective accelerometer with low power and high reliability. This accelerometer comprises a heater and two thermistors. The central heater elevates the temperature of the chip above ambient levels while the symmetrically arranged thermistors monitor the temperature differentials induced by acceleration. The heater and thermistors are fabricated on a glass substrate using a standard micro-electromechanical systems (MEMS) process flow, and the fabricated sensor is installed on a rotation platform and a shaking table experimental setup to perform the experiment. The results indicate that the sensor has the capability to measure accelerations surpassing 80 m/s2, with an approximate linear sensitivity of 110.69 mV/g. This proposed thermal convective accelerometer offers promising potential for various applications requiring precise acceleration measurements in environments where low power consumption and high reliability are paramount.

Comparison of step-count outcomes across seven different activity trackers: a free-living experiment with young and older adults.

BACKGROUND: There are now many different types of activity trackers, including pedometers and accelerometers, to estimate step counts per day. Previous research has extensively examined step-count measurements using activity trackers across various settings while simultaneously wearing different devices.; however, older adults frequently display distinct walking patterns and gait speeds compared to younger adults. This study aimed to compare the step-count between older and younger adults by having them simultaneously wear seven different activity trackers in free-living experiments. METHODS: This study included 35 younger adults (21-43 yrs) and 57 physically independent older adults (65-91 yrs). All participants simultaneously wore one pedometer and six activity trackers: ActiGraph GT3X + Wrist and Hip, Omron Active Style Pro HJA-350IT, Panasonic Actimarker, TANITA EZ-064, Yamasa TH-300, and Yamasa AS-200 for seven days. A regression equation was also used to assess inter-device compatibility. RESULTS: When comparing wrist-worn ActiGraph to the six hip-worn activity trackers, the wrist-worn ActiGraph consistently recorded step counts over 4,000 steps higher than hip-worn activity trackers in both groups (range, 3000-5000 steps). Moreover, when comparing the ActiGraph worn on the wrist to that worn on the hip, the proportion was higher among older adults compared to younger ones (younger: 131%, older: 180%). The Actimarker recorded the highest average step counts among six hip-worn devices, with 8,569 ± 4,881 overall, 9,624 ± 5,177 for younger adults, and 7,890 ± 4,562 for older adults. The difference between the hip-worn ActiGraph and Active Style Pro was just about 70 steps/day overall. The correlation among all devices demonstrated a very high consistency, except for the wrist-worn ActiGraph (r = 0.874-0.978). CONCLUSIONS: Step counts recorded from seven selected consumer-based and research-grade activity trackers and one pedometer, except for the wrist-worn ActiGraph. showed a variation of approximately 1700 steps (range, 1265-2275 steps) steps for both groups, yet maintained a high correlation with each other. These findings will be valuable for researchers and clinicians as they compare step counts across different studies or representative surveys conducted globally.