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Gesture recognition by using Inertial Measurement Unit (IMU) sensors plays an important role in various Internet of Things (IOT) applications, e.g., smart home, intelligent medical system and so on. Traditional technologies usually utilize machine learning algorithms to train different gestures during the offline phase, then recognize the gesture during the online phase. However, such technologies cannot recognize these gestures without prior training. Even for the same gesture, with different gesture amplitude may result in unsuccessful recognition. Also if we change the person to perform the same gesture, the algorithms fails. In order to overcome these drawbacks, we propose an approach, which will be able to track the human body motion in realtime and also recognize complicated gestures. It utilizes the accelerometer information and proposes comprehensive localization algorithms for each deployed sensor attached on the human body. Then, it takes the correlation and limitation among body parts into account to recognize the gesture. Our experiments results show that, the successful recognition rate of our algorithm is 100%. Furthermore, any part of the human body can be well tracked, the tracking accuracy can reach 0.06m.

In this work, an internet of things (IoT) sensing and monitoring box has been developed. The proposed low-cost system is a portable device for smart buildings to measure vibrations, monitor, and control noise caused by the industrial machines. We will present an instrument and a method to measure the vibration and tilt of a mechanical system (air conditioner). The primary goal is to create a signal acquisition and monitoring system that is both userfriendly and affordable, while also delivering exceptional precision. The key concept is centered around acquiring and processing signals through the Raspberry Pi. We will use for the first time as an application, which does not exist before, a conversion method to control and monitor remotely the noise generated by the machines. Once the noise reaches a high value or the air conditioner is too much tilted, the system sends an alert in the form of an email. We will use the Python language to acquire and process the signal and send the alerts. The proposed approach is straightforward to implement, and the obtained results demonstrate a high level of accuracy that is consistent with the existing literature.

Study aim: As studies from shooting disciplines other than biathlon have observed associations between weapon accelerations and shooting performance, this study investigated whether accelerations of the rifle stock and aiming point (the point on the target where the rifle is aimed at) are associated with shooting performance, and differences in rifle and aiming point accelerations between the most and least accurate shots. Further, associations between rifle and aiming point accelerations were studied. Materials and methods: Shooting performance (HitDist, hit point distance from the center of the target) along with rifle and aiming point accelerations were measured from nine biathletes who performed 6×5 biathlon prone and standing shots. Results: In the prone posture, rifle or aiming point accelerations were neither associated with shooting performance nor with each other. In the standing posture, vertical rifle accelerations right before triggering were negatively associated with HitDist (r = –0.70, p < 0.05), whereas aiming point accelerations were not associated with HitDist. Horizontal rifle accelerations were positively associated with aiming point accelerations in standing (r = 0.74, p = 0.024), whereas vertical or resultant rifle accelerations did not demonstrate associations with aiming point accelerations. In both postures, rifle accelerations were of the same magnitude in the most and least accurate shots. Conclusion: Rifle and aiming point accelerations provide limited description of the technical level in biathlon shooting. Moreover, rifle accelerations alone do not appear to provide sufficient information to deduce the aiming point movements. Angular movement would likely be required for aiming point movement estimation.

Road surface irregularities not only has an impact on consistency of road, but also affects driver safety, automobile mechanical stability, and fuel consumption. To order to evaluate road roughness and spot potholes, many solutions have been suggested to remotely track the road surface quality. Some of these methods use an embedded mobile accelerometer to take a crowd sensing view to feel the surface of road. Although crowd sensing has various advantages such as ubiquity and less cost, it has some sensitivity to the problems generated by structures made by human, actions of the driver, and characteristics of the road surface which cannot be identified road anomalies. For this purpose, this paper suggests method of deep learning which helps us (a) to identify the different types of surface of road and (b) to differentiate potholes automatically from destabilizations in the crowd sensing-based application sense arising from speed bumps or driver behavior. This paper study and implement different deep learning frameworks in particular: convolutionary neural networks, LSTM networks, and computational structures for reservoirs. The tests were performed using knowledge from the real world, and the results revealed good precision in solving both of the problems.

The Russian breeding population of barnacle geese Branta leucopsis has shown a rapid increase in numbers since 1980, which has coincided with a southwest-wards breeding range expansion within the Russian Arctic. Here barnacle geese also started to occupy coastal and marsh land habitats, in which they were not know to nest on their traditional breeding grounds. While these changes have been well documented by studies and observations throughout the new breeding range of barnacle geese, observations are lacking from the traditional breeding grounds on Novaya Zemlya, as this area is remote and difficult to access. This is especially relevant given rapid climate warming in this area, which may impact local distribution and population size. We used GPS-tracking and behavioural biologging data from 46 individual barnacle geese captured on their wintering grounds to locate nest sites in the Russian Arctic and study nesting distribution in 2008–2010 and 2018–2020. Extrapolating from nest counts on Kolguev Island, we estimate the breeding population on Novaya Zemlya in 2018–2020 to range around 75,250 pairs although the confidence interval around this estimate was large. A comparison with the historical size of the barnacle goose population suggests an increase in the breeding population on Novaya Zemlya, corresponding with changes in other areas of the breeding range. Our results show that many barnacle geese on Novaya Zemlya currently nest on lowland tundra on Gusinaya Zemlya Peninsula. This region has been occupied by barnacle geese only since 1990 and appears to be mainly available for nesting in years with early spring. Tracking data are a valuable tool to increase our knowledge of remote locations, but counts of breeding individuals or nests are needed to further corroborate estimates of breeding populations based on tracking data.

Microelectromechanical systems accelerometers have opened new possibilities for vibration monitoring of a rotating machinery. They enable mounting accelerometers directly to the rotating component of the machine, e.g., shaft. This enables not only the measurement of a lateral vibration but also a torsional vibration of the machine. This increases the vibration data gathered from the machine by one measurement instrument. This article presents an on-shaft wireless universal measurement unit (UMU) with innovative combination of features, such as a high measurement range and easy mounting. The UMU has a two-sensor configuration where two accelerometers are mounted to the opposite sides of a shaft. This enables to utilize a novel signal processing method to separate torsional and lateral vibration from the data. The signal processing method combines and modifies methods presented in the published literature. The results presented in this article demonstrate that the UMU together with the presented signal processing method can measure frequencies of torsional and lateral vibration accurately. However, the amplitude comparison between the UMU and reference sensors cannot be done adequately because they are measuring either different components of the machine or different physical properties.

Studies that objectively investigate patterns of everyday physical activity in relation to well-being and that use measures specific to older adults are scarce. This study aimed to explore objectively measured everyday physical activity and sedentary behavior in relation to a morale measure specifically constructed for older adults. A total of 77 persons (42 women, 35 men) aged 80 years or older (84.3 ± 3.8) wore an accelerometer device for at least 5 days. Morale was measured with the Philadelphia Geriatric Center Morale Scale (PGCMS). PGCMS scores were significantly positively associated with number of steps, time spent stepping, and time spent stepping at >75 steps per minute. Sedentary behavior did not associate with PGCMS. Promoting PA in the form of walking at any intensity–or even spending time in an upright position—and in any quantity may be important for morale, or vice versa, or the influence may be bidirectional. Validerad;2022;Nivå 2;2022-09-12 (hanlid)

Seismic responses of utility tunnel-soil system were studied via shaking table model tests with considerations of two kinds of double box utility tunnels: with and without joint connections. These two testing utility tunnel models were made of galvanized iron wire and micro-concrete, and the ground was simulated by the dry standard sand through layered tamping treatment. The utility tunnel-soil system was subjected to horizontal vibration in uniaxial direction perpendicular to the longitudinal direction of tunnel model. Via instrumentations of earth pressure gauges, accelerometers and strain gauges, the earth pressure response, acceleration response and bending moment response were measured. The testing results show that the joint connections in the utility tunnel along the longitudinal direction play an important role in determining the characteristic of earth pressure response and bending moment response, whereas the effect of joint connections on acceleration response is less significant. In addition, the partition wall exhibits the consistent acceleration response with the side-wall of double box utility tunnel model under seismic condition. Based on the testing results, it is suggested that the joint connection should be taken reasonably into consideration during design and construction for engineering practice.

Cow behavior recognition systems support the assessment of cows’ condition by providing their behavior information. Accelerometers are particularly suited for a non-invasive solution for the development of these monitoring systems. They are cheap and simple in setting up and providing high-performance recognition when using machine learning algorithms. The activity complexity of animals brings challenges in real context applications because different behaviors may have similar acceleration data. The reason is that they contain similar gestures, for example, feeding and standing. In our previous work, we proposed a cows’ behavior classifier based on leg-mounted acceleration data. The distinguishing of similar behaviors, such as feeding and standing, is limited by the data. This study presents a new efficient cow behavior recognition system based on combining leg-mounted and collar-mounted accelerometers. Significantly, the acceleration data from these two sensors were synchronized. Therefore, we can substantially expand the amount of information for classification purposes. Our approach identifies four cow behaviors: walking, feeding, lying, and standing. Random Forest algorithm with our extracted features (root mean square, standard deviation, and mean) and 16-second data window (a sample/second) offer excellent performance when identifying all concerning behaviors: feeding (0.914 accuracy, 0.884 sensitivity, 0.956 positive predictive value), lying (0.998, 0.996, 1), standing (0.88, 0.928, 0.842), and walking (0.998, 0.996, 0.998). These performances are better than other existing works, especially in our experiments with free-grazing cows.

The proliferation of smartphones equipped with various sensors created a large user base, which includes robots (smartphone controlled) as well. The high processor enabled smartphones are used as software robots to track hour-to-hour activities of users using on-board motion sensors. The magnetometer, accelerometer, and gyroscope are non-permission based motion sensors. The logs of these sensors create discriminative patterns to identify users’ movements. This leverages an adversary to cause privacy threats using these non-permission based sensors without user consent. Recent research works also highlighted the privacy threats due to these motion sensors in smartphones. Therefore, in this paper, we present a potential misuse of technology where smartphones can be hacked by PIN Inference attack endangering the physical and software robots’ controlled user activities. We perform experiments to collect motion sensors’ data and infer 4-digit PIN of a smartphone. Our strategy is to detect every key pressed one by one constituting a 4-digit PIN. Our inference results are promising and prove that an adversary can make use of sensors’ data to infiltrate user’s sensitive information from smartphones. Our proposed model infers 84% of the PINs correctly within 40 attempts when tested on 50 PINs. Given real Android devices with different users, we can identify the PIN of smartphones by training our machine learning based model. We also compare our approach with existing state-of-the-art approaches to show the efficacy of our attack model.

The use of side brushcutters exposes users to vibration and noise, which can cause damage to the operator, as they act as a stressor agent. Excessive vibration is responsible for countless disorders, including Raynaud’s syndrome. This study aimed to assess the effect of the vibrations of a side brush cutter on the human body. A side brush cutter was used with three cutting implements (double blade, triple blade and double nylon thread) applied to two crops (wheat and ryegrass). The vibration was measured through a three-dimensional accelerometer placed on the handgrips of the brush cutter, in compliance with the provisions of the ISO 5349-1 Standard. The analyzed variables were submitted to the Shapiro-Wilk and Bartlett tests. Due to the abnormality, the data were transformed by the Neperian logarithm and submitted to analysis of variance, and the means were compared by the Tukey test at 5% probability. As a result, the orthogonal y-axis presented a higher vibration level; the three-point blade showed the lowest vibration level compared to the other cutting implements for both crops; these vibration levels did not exceed the NHO10 recommendation. Hence, using the appropriate attachment for each operation may reduce the harm caused by vibration to the operator’s body. The use of side brushcutters exposes users to vibration and noise, which can cause damage to the operator, as they act as a stressor agent. Excessive vibration is responsible for countless disorders, including Raynaud’s syndrome. This study aimed to assess the effect of the vibrations of a side brush cutter on the human body. A side brush cutter was used with three cutting implements (double blade, triple blade and double nylon thread) applied to two crops (wheat and ryegrass). The vibration was measured through a three-dimensional accelerometer placed on the handgrips of the brush cutter, in compliance with the provisions of the ISO 5349-1 Standard. The analyzed variables were submitted to the Shapiro-Wilk and Bartlett tests. Due to the abnormality, the data were transformed by the Neperian logarithm and submitted to analysis of variance, and the means were compared by the Tukey test at 5% probability. As a result, the orthogonal y-axis presented a higher vibration level; the three-point blade showed the lowest vibration level compared to the other cutting implements for both crops; these vibration levels did not exceed the NHO10 recommendation. Hence, using the appropriate attachment for each operation may reduce the harm caused by vibration to the operator’s body.

In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. Such systems automatically detect critical fall events and immediately alert medical professionals or caregivers. To support long-term FD services, various power-saving strategies have been implemented. Among them, a reduced sampling rate is a common approach for an energy-efficient system in the real-world. However, the performance of FD systems is diminished owing to low-resolution (LR) accelerometer signals. To improve the detection accuracy with LR accelerometer signals, several technical challenges must be considered, including misalignment, mismatch of effective features, and the degradation effects. In this work, a deep-learning-based accelerometer signal enhancement (ASE) model is proposed to improve the detection performance of LR-FD systems. This proposed model reconstructs high-resolution (HR) signals from the LR signals by learning the relationship between the LR and HR signals. The results show that the FD system using support vector machine and the proposed ASE model at an extremely low sampling rate (sampling rate < 2 Hz) achieved 97.34% and 90.52% accuracies in the SisFall and FallAllD datasets, respectively, while those without ASE models only achieved 95.92% and 87.47% accuracies in the SisFall and FallAllD datasets, respectively. This study demonstrates that the ASE model helps the FD systems tackle the technical challenges of LR signals and achieve better detection performance.

Drowsiness detection is a crucial step for safe driving. A plethora of efforts has been invested on using pervasive sensor data (e.,g., video, physiology) empowered by machine learning to build an automatic drowsiness detection system. Nevertheless, most of the existing methods are based on complicated wearables (e.,g., electroencephalogram) or computer vision algorithms (e.,g., eye state analysis), which makes the relevant systems hardly applicable in the wild. Furthermore, data based on these methods are insufficient in nature due to limited simulation experiments. In this light, we propose a novel and easily implemented method based on full non-invasive multimodal machine learning analysis for the driver drowsiness detection task. The drowsiness level was estimated by self-reported questionnaire in pre-designed protocols. First, we consider involving environmental data (e.,g., temperature, humidity, illuminance, and further more), which can be regarded as complementary information for the human activity data recorded via accelerometers or actigraphs. Second, we demonstrate that the models trained by daily life data can still be efficient to make predictions for the subject performing in a simulator, which may benefit the future data collection methods. Finally, we make a comprehensive study on investigating different machine learning methods including classic `shallow' models and recent deep models. Experimental results show that, our proposed methods can reach 64.6% unweighted average recall for drowsiness detection in a subject-independent scenario.

Objective : The coronavirus disease-2019 (COVID-19) pandemic and national lockdowns took away opportunities for children to be physically active. This study aimed to determine the effect of the COVID-19 lockdown on accelerometer-assessed physical activity (PA) in children in Wales. Methods : 800 participants (8–18 years old), stratified by sex, age, and socio-economic status, wore Axivity AX3 accelerometers for 7 days in February 2021, during the lockdown, and in May 2021, while in school. Raw accelerometer data were processed in R-package GGIR, and cut-point data, average acceleration (AvAcc), intensity gradient (IG), and MX metrics were extracted. Linear mixed models were used to assess the influence of time-point, sex, age, and SES on PA. Results : During lockdown, moderate-to-vigorous PA (MVPA) was 38.4 ± 24.3 min/day; sedentary time was 849.4 ± 196.6 min/day. PA levels increased significantly upon return to school (all variables p < 0.001). While there were no sex differences during lockdown (p = 0.233), girls engaged in significantly less MVPA than boys once back in school (p < 0.001). Furthermore, boys had more favorable intensity profiles than girls (IG: p < 0.001), regardless of time-point. PA levels decreased with age at both time-points; upper secondary school (USS) girls were the least active group, with an average M30 of 195.2 mg (while in school). Conclusion : The lockdown affected boys more than girls, as reflected by the disappearance of the typical sex difference in PA levels during lockdown, although these were re-established on return to school. USS (especially girls) might need specific COVID-recovery intervention., Graphical Abstract Image, graphical abstract

Tracking ground support equipment (GSE) in a high accuracy manner is crucial for both airport safety and optimal management of airport assets but the related researches and products are scarce. Tracking a multi-carriage logistics train is obviously most challenging compared with other single-carriage GSE. In this paper, we design a real-time on-board positioning and heading system (ROPHS) to obtain the real-time status of a multi-carriage logistics train which consists of a powered leading vehicle and one or several non-powered trailing vehicles. The status includes: (a) the accurate positions and velocities of any points on this train, and (b) the alterable number and linking sequence of trailing vehicles at any time of a trip. Technically, the hardware of the system relies on real-time kinematic (RTK) to obtain geolocation of the leading vehicle, and on gyroscopes, magnetometers and accelerometers to obtain headings of all vehicles. A geometry based recurrence algorithm is afterwards presented to calculate the positions of any trailing vehicles. In the end, the multiple model based tracking algorithm is proposed to compute the precision-improved locations and real-time velocities of the whole train. Different from existing traditional GPS or RFID based positioning techniques, the proposed system can reach centimeter-level accuracy, which enables collisions detection, especially those latent collisions that cannot be easily monitored or foreseen by crews' visual inspection.

Background: Although digital mobility outcomes (DMOs) can be readily calculated from real-world data collected with wearable devices (WD) and ad-hoc algorithms, technical validation is still required. The aim of this paper is to comparatively assess and validate DMOs estimated using real-world gait data from six different cohorts, focusing on gait sequence detection (GSD), foot initial contact detection (ICD), cadence (CAD) and stride length (SL) estimates. Methods: Twenty healthy older adults, 20 people with Parkinson’s disease, 20 with multiple sclerosis, 19 with proximal femoral fracture (PFF), 17 with chronic obstructive pulmonary disease and 12 with congestive heart failure were monitored for 2.5 hours in the real-world, using a WD worn on the lower back. A reference system combining inertial modules with distance sensors and pressure insoles was used for comparison of DMOs from the WD. We assessed and validated three algorithms for GSD, four for ICD, three for CAD and four for SL by concurrently comparing their performances (e.g., accuracy, specificity, sensitivity, absolute and relative errors). Additionally, the effects of walking bout (WB) speed and duration on algorithm performance were investigated. Results: We identified two cohort-specific top performing algorithms for GSD and CAD, and a single best for ICD and SL. GSD best algorithms showed good performances (sensitivity > 0.73, positive predictive values > 0.75, specificity > 0.95, accuracy > 0.94). ICD and CAD algorithms presented excellent results, with sensitivity > 0.79, positive predictive values > 0.89 and relative errors < 11% for ICD and < 8.5% for CAD. The best identified SL algorithm showed lower performances than other DMOs (absolute error < 0.21 m). Lower performances across all DMOs were found for the cohort with most severe gait impairments (PFF). Algorithms’ performances were lower for short WBs; slower gait speeds (Conclusions: Overall, the identified algorithms enabled a robust estimation of key DMOs. Our findingsshowed that the choice of algorithm for estimation of GSD and CAD DMOs should be cohort-specific (e.g., slow walkers and with gait impairments). Short WB length and slow walking speed worsened algorithms’ performances. Trial registration: ISRCTN – 12246987.

Background Early puberty timing is associated with adverse health outcomes. We aimed to examine prospective associations between objectively measured physical activity and puberty timing in boys and girls. Methods In the UK Millennium Cohort Study, physical activity volume and intensities at 7 years were measured using accelerometers. Status of several pubertal traits and age at menarche were reported at 11, 14 and 17 years. Age at menarche in girls was categorized into tertiles. Other puberty traits were categorized into earlier or later than the median ages calculated from probit models, separately in boys and girls. Multivariable regression models, with adjustment for maternal and child characteristics including body mass index (BMI) at age 7 years as potential confounders, were performed to test the associations of total daily activity counts and fractions of activity counts across intensities (in compositional models) with puberty timing, separately in boys (n = 2531) and girls (n = 3079). Results Higher total daily activity counts were associated with lower risks for earlier (vs later) growth spurt, body hair growth, skin changes and menarche in girls, and more weakly with lower risks for earlier skin changes and voice breaking in boys (odds ratios = 0.80–0.87 per 100 000 counts/day). These associations persisted on additional adjustment for BMI at 11 years as a potential mediator. No association with puberty timing was seen for any physical activity intensity (light, moderate or vigorous). Conclusions More physical activity regardless of intensity may contribute to the avoidance of earlier puberty timing, independently of BMI, particularly in girls.

In the integrity of the conservation law, the amount of motion, A mathematical model of signal propagation during vibration diagnostics has been constructed. In the beginning, the problem of signal propagation was investigated, which was reduced to the problem of will propagation was solved. Analyzing the experimental results it was found that it is necessary to take into account the damped nature of the signals. For this purpose, a mathematical model has been developed that allows solving the problem of the propagation of damped signals. Comparative analysis allows us to conclude that the constructed model is adequate.

Driving is an integral component of many operational systems, and any small improvement in driving quality can have a significant effect on accidents, traffic, pollution, and the economy in general. However, making improvements is challenging given the complexity and multidimensionality of driving as a task. In this paper, we investigate the effectiveness of nudging to improve driving performance. In particular, we leverage a smartphone application launched by our industry partners to send three types of nudges through notifications to drivers, indicating how they performed on the current trip with respect to their personal best, personal average, and latest driving performance. We measure the resulting driving performance using telematics technology (i.e., real-time sensor data from an accelerometer, Global Positioning System (GPS), and gyroscope in a mobile device). Compared with the “no-nudge” control group, we find that personal best and personal average nudges improve driving performance by approximately 18% standard deviations of the performance scores calculated by the application. In addition, these nudges improve interaccident times (by nearly 1.8 years) and driving performance consistency, as measured by the standard deviation of the performance score. Noting that driving abilities and feedback seeking may vary across individuals, we adopt a generalized random forest approach, which shows that high-performing drivers who are not frequent feedback seekers benefit the most from personal best nudges, whereas low-performing drivers who are also frequent feedback seekers benefit the most from the personal average nudges. Finally, we investigate the potential mechanism behind the results by conducting an online experiment in a nondriving context. The experiment shows that the performance improvements are directly driven by the changes in participants’ effort in response to different nudges and that our key findings are robust in alternative (nondriving) settings. Our analysis further shows that nudges are effective when the variability in reference points is low, which explains why the personal best and personal average nudges are effective, whereas the last score nudge is not. This paper was accepted by Vishal Gaur, operations management.

This paper introduces a newly developed multi-sensor data fusion for the milling chatter detection with a cheap and easy implementation compared with traditional chatter detection schemes. The proposed multi-sensor data fusion utilizes microphone and accelerometer sensors to measure the occurrence of chatter during the milling process. It has the advantageous over the dynamometer in terms of easy installation and low cost. In this paper, the wavelet packet decomposition is adopted to analyze both measured sound and vibration signals. However, the parameters of the wavelet packet decomposition require fine-tuning to provide good performance. Hence the result of the developed scheme has been improved by optimizing the selection of the wavelet packet decomposition parameters including the mother wavelet and the decomposition level based on the kurtosis and crest factors. Furthermore, the important chatter features are selected using the recursive feature elimination method, and its performance is compared with metaheuristic algorithms. Finally, several machine learning techniques have been adopted to classify the cutting stabilities based on the selected features. The results confirm that the proposed multi-sensor data fusion scheme can provide an effective chatter detection under industrial conditions, and it has higher accuracy than the traditional schemes.

Current methods of evaluating essential tremor (ET) either rely on subjective ratings or use limited tremor metrics (i.e., severity/amplitude and frequency). In this study, we explored performance metrics from Fitts law tasks that replicate and expand existing tremor metrics, to enable low-cost, home-based tremor quantification and analyze the cursor movements of individuals using a 3D mouse while performing a collection of drawing tasks. We analyzed the 3D mouse cursor movements of 11 patients with ET and three controls, on three computer-based tasksa spiral navigation (SPN) task, a rectangular track navigation (RTN) task, and multi-directional tapping/clicking (MDT)with several performance metrics (i.e., outside area (OA), throughput (TP in Fitts law), path efficiency (PE), and completion time (CT)). Using an accelerometer and scores from the Essential Tremor Rating Assessment Scale (TETRAS), we correlated the proposed performance metrics with the baseline tremor metrics and found that the OA of the SPN and RTN tasks were strongly correlated with baseline tremor severity (R2=0.57 and R2=0.83). We also found that the TP in the MDT tasks were strongly correlated with tremor frequency (R2=0.70). In addition, as the OA of the SPN and RTN tasks was correlated with tremor severity and frequency, it may represent an independent metric that increases the dimensionality of the characterization of an individuals tremor. Thus, this pilot study of the analysis of those with ET-associated tremor performing Fitts law tasks demonstrates the feasibility of introducing a new tremor metric that can be expanded for repeatable multi-dimensional data analyses.

Wearable activity recognition can collate the type, intensity, and duration of each child's physical activity profile, which is important for exploring underlying adolescent health mechanisms. Traditional machine-learning-based approaches require large labeled data sets; however, child activity data sets are typically small and insufficient. Thus, we proposed a transfer learning approach that adapts adult-domain data to train a high-fidelity, subject-independent model for child activity recognition. Twenty children and twenty adults wore an accelerometer wristband while performing walking, running, sitting, and rope skipping activities. Activity classification accuracy was determined via the traditional machine learning approach without transfer learning and with the proposed subject-independent transfer learning approach. Results showed that transfer learning increased classification accuracy to 91.4% as compared to 80.6% without transfer learning. These results suggest that subject-independent transfer learning can improve accuracy and potentially reduce the size of the required child data sets to enable physical activity monitoring systems to be adopted more widely, quickly, and economically for children and provide deeper insights into injury prevention and health promotion strategies.

A work accident on a technician causes injury. When doing work, even though the technician is equipped with work safety, there are still factors that can cause accidents intentionally or unintentionally. The development of technology is now more advanced. Now the industrial revolution has reached the era of 4.0. Its progress is accompanied by information technology that is growing very rapidly. Many internets of things devices can already be used in everyday life. In today's life, the use of IoT devices has become a habit of today's modern society. For example, to travel using public transportation, there are many mobile apps that are connected to transportation providers. Including trade, the number of marketplaces has caused the growth of smartphones to be very fast. Smartphones are now equipped with many sensors, ranging from GPS sensors, wi-fi sensors, temperature sensors, and various sensors. Often the technicians in charge of climbing towers or heights are never caught by management or project leaders. Even though this causes losses both losses for technicians and losses for management. Therefore, how to monitor the technicians to be careful in carrying out their duties. Android smartphone devices have many sensors. Sensors in Android are used as a fall detection tool. By using Android sensors, the use of Internet of Thing sensors on smartphones will produce very useful monitoring tools. The use of Flutter framework as a medium for utilizing the accelerometer sensor and gyroscope sensor as a fall detection tool. This study aims to create a prototype of a fall detection application system.

Identifying vehicle maneuvers in the context of Connected Vehicles (CV) system brings huge potentials to enhance traffic safety. However, this process requires various advanced sensors, which are either available for luxury vehicles or expensive to install. Differently, smartphone is a more feasible choice with high penetration rate and various built-in sensors. Among the existing studies of applying the smartphone to detect vehicle maneuvers, most treated the detection as a classification problem without considering the real-world application. For example, the smartphone was fixed. Too many descriptive features were generated from the sensor data. To alleviate these problems, this paper developed a vehicle maneuvers detection system using a common smartphone with GPS, gyroscope, accelerometer, and magnetometer sensors. We first released the constraints on the smartphone's position through a coordination system reorientation method. Then, simply filtered sensor data were directly used. A stacked-LSTM model was built to detect the vehicle maneuvers considering the time-dependency of the sensor data. This paper compared the performance of the proposed system with previous studies and various machine learning methods, including LightGBM, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest. Extensive experimental results indicated that the proposed system accurately detected different vehicle maneuvers with an average F1-score of 0.98, precision of 0.97, and recall of 0.98, which outperformed the counterparts. Moreover, the model can be easily transferred to different drivers and locations. The system is robust and suitable for the real-time application as it requires simple processing of smartphone sensor data.

A recent survey of Europe’s highway infrastructure has concluded that almost half of Europe’s bridges are nearing the end of their design live. Work in the wider Structural Health Monitoring sector is aiming to develop reliable and cost-effective methods for verifying condition, remaining service life and safety of ageing structures. Most bridge condition assessment methods are based on deflection, acceleration or strain measurements. This paper looks at the possibility of using rotation measurements as a main parameter to identify damage. This study looks at numerical analyses of a moving point load on a one-dimensional bridge model to provide the theoretical basis of the proposed damage detection method. It is shown that when local damage occurs, even when it is remote from a sensor location, it results in an increase in the magnitude of rotation measurements. This study looks at how best to exploit this fact for damage detection. A number of damage scenarios, sensor locations, and load arrangements are investigated in this study and their influence on the ability of the algorithm to detect damage are reported.

Background Health benefits of physical activity are very well acknowledged but the role of both occupational physical activity (OPA) and leisure time physical activity (LTPA) in recovery after work is not thoroughly understood. The purpose of this study was to investigate the association between accelerometer-measured OPA and LTPA and the need for recovery after work (NFR) in early childhood education and care (ECEC) professionals. Methods The study participants were 217 female ECEC professionals aged 17–64. Physical activity was recorded with a three-axis accelerometer (ActiGraph GT9X Link, ActiGraph, USA) for seven consecutive days. Separate analyses were conducted for both OPA and LTPA and reported as hours/day based on different intensity levels (light, moderate, vigorous, very vigorous). The NFR was measured with the Need For Recovery (NFR) scale (0%–100%). Results Participants’ average physical activity for both OPA and LTPA was about 4 h/day, and the mean NFR score was 38.4%. OPA was significantly associated with the NFR but not with LTPA. The relationship remained significant after adjustments for age, body mass index, work ability, mental health status, and sleep difficulties (p Conclusion According to this study, the OPA level is related to the level of the NFR in female ECEC professionals. Based on the results, it seems that LTPA has no relevance to the NFR. Results suggest that long-lasting OPA, even without strenuous physical activity at work, may predispose individuals to a high NFR.

Introduction Physical activity is important for children’s health and well-being. Supportiveness for physical activity of home and neighborhood environments may affect children’s PA, but most studies are cross-sectional. We examined environmental predictors of change in children’s physical activity over two years. Methods Data were from the longitudinal, observational cohort study, ‘Neighborhood Impact on Kids’. Participants were children (initially aged 6–12 years) and their parent/caregiver (n = 727 dyads) living in neighborhoods throughout San Diego County, California and King County (Seattle area), Washington, USA. Children’s moderate-to-vigorous physical activity (MVPA) was measured using accelerometers at T1 (Time 1 or baseline, 2007–2009) and T2, the two-year follow-up. At T1, parents survey-reported on physical activity (PA) equipment at home and demographics. Neighborhood environment was measured using spatial data in Geographic Information Systems (intersection density; park availability) and in-person audits (informal play space near home; park-based PA facilities; land use; support for walking/cycling). Generalized additive mixed models estimated total effects, then direct effects, of environmental attributes on MVPA at T1. Two-way moderating effects of child’s sex and age were examined at T1. To examine associations of environmental exposures with changes in MVPA, we estimated interaction effects of environmental attributes on the association between time and MVPA. Results On average, children accumulated 146 min/day (standard deviation or SD = 53) of MVPA at T1, and 113 (SD = 58) min/day at T2. There were no significant total or direct effects of environmental attributes on MVPA at T1, and no significant two-way interaction effects of child’s age and sex for T1 MVPA. Having informal play spaces proximal to home with more amenities was associated with less MVPA decline from T1 to T2. Higher residential density, higher land use mix, and higher number of PA facilities in nearby parks were unexpectedly associated with greater MVPA decline. Conclusion Higher quality informal play spaces close to home may help offset declines in MVPA during middle childhood, as they may promote unstructured active play with opportunities for parental or neighbor surveillance. Unexpectedly, environmental factors consistent with higher walkability were associated with greater declines in children’s MVPA. As physical activity differs across the lifespan, so may environmental factors that facilitate it.

These are experimental time series from an analog electronic circuit realizing a Rössler system, externally driven by physical recordings from an accelerometer monitoring the movements of cattle, taken from a pre-existing dataset (see below). They are provided to support the replication of the results reported in the associated publication, as well as any further public-domain academic research in the related fields, in compliance with the specified license terms and all applicable legal clauses. The following reference must be cited when using these data: Minati L, Li C, Bartels J, Chakraborty P, Li Z, Yoshimura N, Frasca M, Ito H, Accelerometer time series augmentation through externally driving a non-linear dynamical system, Chaos, Solitons and Fractals 168 (2023) 113100, DOI 10.1016/j.chaos.2023.113100. The present dataset only provides the analog electronic circuit outputs, and neither the accelerometer signals that were input to it nor the corresponding behavioral labels. Therefore, it cannot be used in isolation. Those essential elements need to be drawn from the following dataset: Ito H, Takeda K, Tokgoz KK, Minati L, Fukawa M, Li C, Bartels J, Rachi I, Sihan A, Japanese Black Beef Cow Behavior Classification Dataset, Available at https://zenodo.org/record/5849025. Usage is thus subject to citing and acknowledging this source, crediting its authors, abiding to its specific license terms and conditions, and citing the references indicated therein. See legend file for specifics, x10 up-sampling required. This work was supported in part by JSPS KAKENHI Grant Number 19H02191, and in part by JST-SPRING Grant Number JPMJSP2106. The authors are grateful to all the Research Institute for the Earth Inclusive Sensing members, and especially K. Takeda, K.K. Tokgoz, and M. Fukawa for their excellent support in establishing the publicly-available dataset used also in this study, and thank A. Tharayil Narayanan for useful discussions. L.M. developed the analog circuit and other concepts in collaboration with M.F. when based in Trento, Italy, later applying them to this study and performing the experiments and writing the manuscript while in Tokyo, Japan.

Background This primary analysis evaluated the “PREVenting the impairment of primary Osteoarthritis by high-impact long-term Physical exercise regimen—Psychological Adherence Program” (PrevOP-PAP), designed to support patients with osteoarthritis of the knee (OAK) to engage in regular moderate-to-vigorous physical activity (MVPA) to reduce OAK symptoms (WOMAC scores). Theory-based on the health action process approach (HAPA), the intervention targeted volitional precursors of MVPA change: action and coping planning, maintenance and recovery self-efficacy, action control, and social network formation. We hypothesized that compared to an active control condition, increases in MVPA at the end of the 12-month intervention would translate into lower WOMAC scores at 24 months in the intervention condition. Methods Participants with radiographically verified moderate OAK (N = 241; 62.66% female; M(SD) = 65.60(7.61) years) were randomly assigned to the intervention (51%) or the active control condition. WOMAC scores (24 months) were the primary -, accelerometer-assessed MVPA (12 months) the key secondary outcomes. The PrevOP-PAP was a 12-month intervention with computer-assisted face-to-face and phone-based sessions designed to increase HAPA-proposed volitional precursors of MVPA change (up to 24 months; secondary outcomes). Intent-to-treat analyses included multiple regression and manifest path models. Results MVPA (12 months) did not mediate effects of the PrevOP-PAP on WOMAC scores (24 months). Compared to the active control condition, WOMAC scores (24 months) were lower in the intervention condition, but this effect did not remain stable in sensitivity analyses (b(SE) = -8.41(4.66), 95%-CI [-17.53; 0.71]). However, exploratory analyses revealed significantly stronger reductions in WOMAC-pain (24 months) in the intervention condition (b(SE) = -2.99(1.18), 95%-CI [-5.36; -0.63]). Groups did not differ in MVPA at 12 months (b(SE) = -3.78(3.42), 95%-CI [-10.80; 2.58]). Of the proposed precursors of MVPA change, action planning was higher in the intervention than in the control condition (24 months; b(SE) = 0.64(0.26), 95%-CI [0.14; 1.15]). Conclusions Compared to an active control condition, the PrevOP-PAP did not produce reliable effects on WOMAC scores and none on preceding MVPA. Of the HAPA-proposed volitional precursors, only action planning was sustainably increased. Future interventions should use m-health applications to digitally support long-term changes in proposed volitional precursors of MVPA change. Trial registration German Clinical Trials Register; https://drks.de/search/de/trial/DRKS00009677; also available at http://apps.who.int/trialsearch/; registration number: DRKS00009677; date of registration: 26/01/2016.

Systematic review of the responsiveness or sensitivity to change of accelerometers for measuring physical activity, sleep, and/or sedentary behavior.

This study presents a method for predicting location classes of a room such as a kitchen, and restroom, where a user is located by discovering location-specific sensor data motifs in sensor data observed by user’s sensor devices, such as smartwatch, without requiring labeled training data collected in a target environment. For example, we can observe similar waveforms corresponding to kitchen knife chopping actions using body-worn accelerometers in kitchens and can also observe similar sound features by active sound probing in bathrooms because of their water-resistant walls. This indicates that such location-specific sensor data motifs can be inherent information for location class prediction in almost every environment. This study proposes a novel method that automatically detects location-specific motifs from time series sensor data by calculating a score that represents the “location specificity” of each motif in a time series. Previous studies on location class prediction assume that location-specific sensor data are always observed in a room or use handcrafted rules and templates to detect location-specific sensor data resulting in difficulties in applying them to several realistic environments. In contrast, our method, named IndoLabel, can automatically discover short sensor data motifs, specific to a location class, and can automatically build an environment-independent location classifier without requiring handcrafted rules and templates. The proposed method was evaluated in real house environments using leave-one-environment-out cross-validation and achieved a state-of-the-art performance although labeled training data in the target environment was unavailable.

Pedestrian positioning with wearable devices is a significant application of attitude tracking. It tracks the attitude (with heading angle being the most important part) of the device in real time and provides positioning services for users based on the information of step length provided by Pedestrian Dead-Reckon (PDR), which is a cheap and efficient positioning method at present. However, amid a train of positioning methods, the joint estimate of tracking is given by a train of methods based on the direction of gravity and the earths magnetic field direction. Considering the measurement of gravity that the gravity accelerometer is exposed to heavy noise due to the complex movement of human body during walking with uniform swing arm posture and forward speed, this paper proposed a novel estimate method based on the Kalman filter with multi-state constraints and the usage of low-cost sensors, which fulfills the estimation with the sequential observation of magnetic field. Compared with other related work, this method proposed in this paper eliminates the dependence on gravity direction, avoiding the influence of heavy noise caused by additional linear acceleration in motion state, and reduces the influence of insufficient observation when using magnetic field observation alone. The performance of the proposed method is evaluated by real-world experimentation results.

Made available in DSpace on 2022-04-28T19:51:49Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-01-01 Physical activity has been linked to higher cardiac autonomic modulation. However, it is not clear in the literature which type of intensity of physical activity (sedentary, light, moderate, or vigorous) could be related to better cardiac autonomic modulation and whether this relationship is similar in men and women. The aim of this study was to analyze the relationship between cardiac autonomic modulation and the different intensities of physical activity in men and women. The sample included 100 men and 131 women. Cardiac autonomic modulation was obtained by heart rate variability (SDNN, RMSSD, LF un, HF un, SD1, and SD2). Time spent in different intensities (sedentary, light, moderate, and vigorous intensity physical activity) was measured using a waist-worn accelerometer for seven days. Linear regression was used to verify the relationship between physical activity and cardiac autonomic modulation, considering the adjustment for age, ethnicity, and socioeconomic level. Among men, a significant relationship of moderate intensity was observed with SDNN (β = 2.442; p = 0.021) and SD2 (β = 2.936, p = 0.028), and of vigorous intensity with SDNN (β = 12.826, p = 0.028), RMSSD (β = 16.370, p = 0.018), and SD1 (β = 12.344, p = 0.025) indices. Among women, light-intensity physical activity was significantly related with LFnu (β =.655, p = 0.005) and HFnu (β = -.590, p = 0.010). Moderate and vigorous physical activity was associated with better cardiac autonomic modulation in men. Light physical activity was associated with lower sympathetic modulation in women. School of Technology and Sciences São Paulo State University (Unesp) Centro de Pesquisa Clínica e Epidemiológica Hospital Universitário Universidade de São Paulo (USP) Research Center on Physical Activity Health and Leisure (CIAFEL) Faculty of Sport University of Porto School of Human Movement and Nutrition Sciences The University of Queensland Department of Physical Education Universidade Nove de Julho (UNINOVE) School of Technology and Sciences São Paulo State University (Unesp)

The last decade has witnessed a growing demand for precise positioning in many applications, including autonomous car navigation. The safety features in autonomous driving and Advanced Driver Assistance Systems (ADAS) require lane-level positioning accuracy. Such accuracy can be obtained from the Global Navigation Satellite Systems (GNSS) through either differential techniques or Precise Point Positioning (PPP). PPP is currently favored over differential GNSS because it provides a global solution without the need for local reference stations. Nevertheless, employing PPP for land vehicles would be challenging due to frequent signal degradation and blockage. Integrating PPP with an Inertial Navigation System (INS) can solve the solution continuity problem; however, the INS solution drifts over time, resulting in losing the desired accuracy. Implementing a reliable PPP/INS system that can preserve the required accuracy is not trivial, especially with financial and computational cost constraints. This article proposes the integration of PPP with the Reduced Inertial Sensor System (RISS) for lane-level car navigation. The high-precision needed in lane-level positioning can be achieved by integrating PPP with high-end INS. Since high-end INS are expensive, this work proposes the use of RISS instead of the traditional INS. RISS uses only one gyroscope and two accelerometers, which can save more than half the high-end INS cost. The proposed PPP/RISS system was tested through three road tests that included highway driving under several overpasses. The system was able to maintain horizontal position errors of less than 50 cm.

Smoking cessation is a significant challenge for many people addicted to cigarettes and tobacco. Mobile health-related research into smoking cessation is primarily focused on mobile phone data collection either using self-reporting or sensor monitoring techniques. In the past 5 years with the increased popularity of smartwatch devices, research has been conducted to predict smoking movements associated with smoking behaviors based on accelerometer data analyzed from the internal sensors in a user’s smartwatch. Previous smoking detection methods focused on classifying current user smoking behavior. For many users who are trying to quit smoking, this form of detection may be insufficient as the user has already relapsed. In this paper, we present a smoking cessation system utilizing a smartwatch and finger sensor that is capable of detecting pre-smoking activities to discourage users from future smoking behavior. Pre-smoking activities include grabbing a pack of cigarettes or lighting a cigarette and these activities are often immediately succeeded by smoking. Therefore, through accurate detection of pre-smoking activities, we can alert the user before they have relapsed. Our smoking cessation system combines data from a smartwatch for gross accelerometer and gyroscope information and a wearable finger sensor for detailed finger bend-angle information. We compare the results of a smartwatch-only system with a combined smartwatch and finger sensor system to illustrate the accuracy of each system. The combined smartwatch and finger sensor system performed at an 80.6% accuracy for the classification of pre-smoking activities compared to 47.0% accuracy of the smartwatch-only system.

The paper presents a research of angular orientation based on a Microelectromechanical System (MEMS) accelerometer by using machine learning (ML) and deep learning (DL) model with architectures of deep neural networks (DNN). In the industrial environment, Artificial Intelligence (AI) plays a crucial role in automation which is a potential solution for better performance of inclinometer. This research was carried out to apply this intelligent model on the Inertial Measurement Unit (IMU) to accomplish the angular position. The experiment shows that the ML model correctly learns the relationship between acceleration and tracking angles via polynomial regression with an R-square of 0.99. The employed DL model with 4 hidden layers of 10 neurons achieves an accuracy of 99.99 \% and almost non-error performance. The acceleration acquisitions were obtained from MEMS accelerometer LSM9DS1 at a frequency of 50 Hz via microcontroller STM32F401RE. The ML and DNN model were designed based on platform Tensorflow with high processing accuracy. The Pan-Tilt Unit was used as the angle reference for static and dynamic tests. The traditional technique is used for comparison as well as verification of the proposed models. DL model has better precision over the ML model due to its high structure level with updating weight and error optimization from the neural network structure. Meanwhile, MC shows more stable results in dynamic circumstances.

This study tested the concurrent and construct validity of smartphone accelerometry measurement of sit-to-stand (STS) performance in individuals aged 65–89 years. Normal and fast STS times were recorded by smartphone accelerometer, force plate, and video motion systems concurrently, and isokinetic knee extension power and STS whole-body power were obtained. Normal and fast speed STS times from a smartphone accelerometer agreed closely with force plate and motion system methods (mean difference = 0.04 s). Normal and fast STS times were inversely related to isokinetic knee extension power (r = −.93, p r = −.82, p r = −.76, p r = −.70, p

Accurate orientation estimation using low-cost inertial and magnetic sensors is important. However, since the magnetometer measurements affect pitch and roll estimates in addition to yaw determination, the resultant accuracy reduction under magnetic disturbances is adverse for special occasions where high-precision pitch and roll estimates are essential. This article presents a double-extended Kalman filter (DEKF) orientation estimator with unit quaternion as output, which decouples magnetometer effects on pitch and roll estimates by decomposing the orientation into tilt quaternion and heading quaternion. The tilt quaternion, including pitch and roll information, is estimated by the first EKF where accelerometer and gyroscope measurements are processed and the inaccurate heading information is discarded. The heading quaternion, solely describing the yaw orientation, is determined by the second EKF, which fuses the vertical component of angular rate measurements and the horizontal component of magnetic measurements. True quaternion is denoted by the multiplicative of tilt and heading quaternions. The separate property of DEKF limits the influences of magnetic disturbances on yaw estimation, making the pitch and roll angles immune to them. Experiment results show that the proposed method solves the coupled disturbance problem without losing accuracy.

Balance ability is important for preschoolers' motor and physical development. Portable accelerometers can provide resolution tests and identification of preschoolers with balance defects.Despite previous studies on the balance measures of accelerometer tests, there is a lack of complete analyses for preschoolers aged 4-5 years. In this study, we aim to verify the reliability of measuring the static balance of preschoolers in this age range based on inertia sensors for the acceleration and angular velocity moduli.Thirty children wore an inertial sensor in the 5th lumbar vertebra and completed four tests, i.e., standing on a firm surface and on a foam surface with open and closed eyes. The standard deviation of the acceleration modulus and root mean square of the angular velocity modulus were calculated. The analysis was based on the intraclass correlation coefficient (ICC) to determine the internal consistency and feasibility.The ICC of the acceleration modulus was between 0.597 and 0.683 (P 0.01), and the test-retest reliability was medium. The ICC of the angular velocity modulus was between 0.683 and 0.812 (P 0.01, P 0.001), and the test-retest reliability was medium to good. The standard error of measurement (SEM) of the acceleration modulus was between 0.001591 and 0.007248 (g), and the SEM% was between 21.24% and 34.12%. The angular velocity modulus SEM values ranged from 1.296 to 3.441 (deg/s), and the SEM% ranged from 25.17% to 33.26%. The difference between the two tests was evenly distributed on both sides of the mean value, and the difference between the test results was within the consistency limit.Inertial sensors can be used to evaluate the static balance ability of preschoolers aged 4-5 years. Further, the angular velocity modulus is more reliable than the acceleration modulus.

GRACE and GRACE-FO require the reduction of non-gravitational accelerations, through an onboard accelerometer, to accurately estimate variations in the Earth’s gravitational field. Accuracy in the accelerometer measurements is vital to this mass change recovery and significant errors will reduce the quality of the recovered science data. This paper describes our current characterization of error sources in the GRACE-FO accelerometers and the error mitigation strategies used in the generation of the JPL/CSR GRACE-FO Version 04 accelerometer data for public release. Our current error characterization model describes flicker noise on readings of one accelerometer plate pair on GRACE-D, impulse response limitations on both accelerometers, nonphysical once per revolution variations in the roll angular acceleration measurements on both accelerometers, significant correlated errors on all axes on GRACE-D, nominal thermal bias dependencies on both accelerometers, and expected nominal inter-measurement aliasing (cross-talk - expected as heritage from GRACE). Attitude reconstruction strategies incorporating star camera, inertial measurement unit, and magnetorquer measurements visibly improve angular acceleration recovery independent of the accelerometer. Error mitigation strategies for the linear acceleration measurements, associated with the calibrated accelerometer product provided in the version 04 release, dramatically improve estimates of the Earth’s gravity field. While many of the error characterizations described here are not necessary for constructing the accelerometer transplant from GRACE-C to GRACE-D, as is currently done in the Version 04 release, the error characterizations described here lay the groundwork for future use of calibrated/corrected GRACE-D accelerometer data.

This paper reviews the development of reliability design technology as well as challenges encountered thus far regarding control systems for long march launch vehicles (LMLVs). Along with the high ...

Background We examined associations of total duration and pattern of accumulation of objectively measured sedentary behavior (SB) with incident cardiovascular disease (CVD) and all-cause mortality among older adults. Methods Total sedentary time and 8 sedentary accumulation pattern metrics were extracted from accelerometer data of 3 991 Whitehall II study participants aged 60–83 years in 2012–2013. Incident CVD and all-cause mortality were ascertained up to March 2019. Results Two hundred and ninety-nine CVD cases and 260 deaths were recorded over a mean (standard deviation [SD]) follow-up of 6.2 (1.3) and 6.4 (0.8) years, respectively. Adjusting for sociodemographic and behavioral factors, 1-SD (100.2 minutes) increase in total sedentary time was associated with 20% higher CVD risk (hazard ratio [95% confidence interval]: 1.20 [1.05–1.37]). More fragmented SB was associated with reduced CVD risk (eg, 0.86 [0.76–0.97] for 1-SD [6.2] increase in breaks per sedentary hour). Associations were not evident once health-related factors and moderate-to-vigorous physical activity (MVPA) were considered. For all-cause mortality, associations with more fragmented SB (eg, 0.73 [0.59–0.91] for breaks per sedentary hour) were found only among the youngest older group ( Conclusions In this study, no associations of total sedentary time and sedentary accumulation patterns with incident CVD and all-cause mortality were found in the total sample once MVPA was considered. Our findings of reduced mortality risk with less total and more fragmented SB independent from MVPA among individuals

The rapid development of Information-Centric Wireless Sensor Network (ICWSN) has solved the challenges of information transmission and processing caused by the accelerated growth of wearable devices and the wide deployment of the Internet of Things (IoT) recently. The privacy security is also a growing problem. The existing works use earphones, covert and user-friendly wearable devices, for user authentication. However, some of the earphone-based authentication solutions need to customize special earphones, which are not universal. Other solutions use microphones and speakers of earphones for authentication, which are susceptible to changes in the auricle’s internal environment, resulting in a decline in performance. To solve this problem, a new authentication solution based on the existing commercial earphones is proposed to authenticate a user by tapping on the earphone rhythmically. This rhythmic tap behaviour causes a change of the signal waveform of the built-in accelerometer in the earphone. Based on this, we design a pipeline to authenticate the user’s identity. We first design an event detection algorithm to segment the tap signal accurately. Then we use the global features calculated based on event detection algorithm and local features extracted from the Convolutional Neural Network (CNN) for building an authentication model using the Naive Bayes (NB) classifier. Finally, 20 users are recruited to evaluate the experiment, and the recognition accuracy reaches 98%. Moreover, we extend the experiment to prove that it has a good performance against the different attacks and is robust in different scenarios.

In the era of internet of things (IoT), sensor plays a more vital role and its quality has a great impact on final measurement. From the viewpoint of final measurement, there are two kinds of effects: bias-only measurement and error-growth measurement. The bias-only measurement can be directly read from a sensing device, and each measurement is only distorted by the inherited bias error of the sensor, e.g., measuring temperature by a thermometer sensor. On the other hand, the error-growth measurement cannot be read directly but be calculated by combining multiple former sampling data, therefore multiple bias errors are accumulated in the measurement. For instance, in inertial navigation, the raw data sampled from an inertial measurement unit (IMU) are converted into a trajectory by multiple integral operations, so the errors of new sampling data are continuously added into the trajectory, and the deviation of the traced trajectory will reach an unacceptable level over time. Clearly, a good IMU trajectory strategy is the one with a less error-accumulation effect. Unfortunately, the analysis of error-growth effect remains not well studied, which motivates this work. This letter first proposes a theoretical error-growth effect analysis framework. Next, we use it to analyze three typical inertial methods, namely single-IMU method, gyro-free-IMU (GF-IMU) method, and -free accelerometer pair (OFAP) method. Finally, the theoretical derivations of the three inertial methods are proved by simulation results.

Background:This research aimed to determine whether patterns of temporal changes in activity levels can indicate postoperative complications following hepatectomy., Methods:Between December 2016 and December 2019, 147 patients wore an accelerometer to measure their physical activity levels after hepatectomy until postoperative day 7. Patterns of changes in activity levels were categorized as follows: upward slope type (n = 88), wherein activity levels gradually increased; bell curve type (n = 13), wherein activity levels initially increased but subsequently decreased; and flat type (n = 46), wherein there was no apparent increase in activity levels. Patient characteristics and postoperative complications were compared for each group., Results:Postoperative complications occurred in 4.5% of patients in the upward slope group, in 76.9% in the bell curve group, and in 65.2% in the flat group (p < 0.001). Surgical site infections (SSI), refractory pleural effusion, and ascites were more common in the bell curve group, while pneumonia was only observed in the flat group., Conclusion:SSI, pleural effusion, and ascites should be considered when previously increasing activity levels decline during the postoperative period. In addition, there is a high risk of SSI and pneumonia when activity levels do not increase at all after surgery.

Background: Commercially available wrist-mounted exercise monitors may offer objective data on disease and recovery. This study is the first to evaluate the potential of such devices in the assessment of frozen shoulder and the effects of treatment. Methods: Twenty-one patients with isolated, unilateral frozen shoulder wore a wrist-mounted accelerometer (Fitbit Fire II, Fitbit Inc. 2007, California, USA) on each wrist for two separate seven-day periods, one week before and six months after treatment. The monitors produced an activity count for each 24-hour period, accounting for all movements of the upper limb. Three values were calculated for each time period: (1) the mean activity count for each limb, (2) the total activity count for both limbs, and (3) an activity count ratio calculated by dividing the activity of the frozen limb by the unaffected limb. Constant score, American Shoulder and Elbow Surgeons, visual analog scale–pain, and range of movement were recorded before and after treatment. Results: Mean activity counts were significantly lower in the frozen shoulder limb than those in the unaffected limb over the initial seven-day period (6066 vs. 7516; P = .04). The activity count ratio significantly improved after treatment (0.83 vs. 096; p 0.01), whereas the mean total activity count remained similar before and after treatment (14915 vs. 12371; P = .18), demonstrating that activity transferred from the unaffected limb back to the previously frozen limb. Range of movement (P