Covid-19 crisis in Australia to understand the ongoing changes in the income distribution as well as the impact of policy responses. By semi-parametrically combining incomplete observed data from three different sources–the monthly Longitudinal Labour Force Survey, the Survey of Income and Housing and administrative payroll data–we estimate the impact of Covid-19 on the Australian income distribution and decompose its impact into the income shock effect and the policy effect between February and June 2020, covering the immediate periods before and after the initial Covid-19 outbreak. Our results suggest that, despite growth in unemployment, the Gini coefficient of equivalised household disposable income dropped by more than 0.02 points between February and June 2020. This reduction is due to the additional wage subsidies and welfare supports offered as part of the policy response, offsetting the increase in income inequality from the income shock effect. The results shows the effectiveness of temporary policy measures both in maintaining living standards and avoiding increases in income inequality. However, the heavy reliance on the support measures shown in the modelling raises the possibility that the changes in the income distribution may be reversed, or even that inequality and living standards could substantially worsen once the measures are withdrawn.This paper undertakes a near real-time analysis of the income distribution effects of the  Covid-19 pandemic and the associated government response led to a significant shift in the usual pattern of social and economic activities. Many governments worldwide imposed various measures in the hope of containing the outbreak. Both the pandemic and the policy measures have had wide-reaching and highly asymmetric implications for many aspects of life in most countries  impact in the early phase of the pandemic and the associated policy response. We propose a calibrated income distribution estimation based on: (i) actual data on changes in employment (both at the extensive and intensive margin) drawn from the up-to-date monthly Longitudinal Labour Force Survey (LLFS); (ii) administrative data on the changes in earnings at the industry and age group level drawn from the Weekly Payroll Jobs and Wages in Australia dataset; and (iii) the Survey of Income and Housing (SIH), which is a comprehensive biannual household income survey collected in 2017–18.As a comprehensive household survey is not available to capture the pre- and post-pandemic changes in the income distribution, we reconstruct the income distribution profiles by combining these three different datasets–the LLFS, the weekly payroll statistics and the SIH–that describe the evolution of the labour market in Australia. All datasets are collected by the Australian Bureau of Statistics (ABS).Covid-19 began to spread in Australia. The selection of the data period allows us to capture the effect of Covid-19 progression in Australia.The LLFS provides details on geography, demography , labour market participation  and transitions into and out of the employment of Australians , housing, labour force characteristics, education, income, assets and childcare -enabled payroll or accounting software, and which is provided to the Australian Taxation Office (ATO). Since July 2019, all employers in Australia have been covered by STP unless an exemption has been granted. This data includes changes in payroll jobs, changes in total wages paid and changes in average weekly wages per job  and the government policy response (part B):I is an outcome measure calculated from the entire income distribution , p is the government Covid-19 response policies and y is the distribution of gross market income. The subscript 0 refers to the pre-Covid baseline period, which is February 2020, and the subscript 1 refers to the periods after the initial spread of Covid-19, ranging between April and June 2020.Covid-19.We focus on the short-term change in income distribution given the data availability and the typical use case of nowcasting. We also assume that factors other than entclass1pt{minimaCovid-19 dominated the income shock between Feb 2020 and June 2020, we assume that most  changes in Part A can be attributed to Covid-19. Part B captures the policy changes and their effects on income distribution. As all changes in the policies are part of the government response to Covid-19, the effect of B is also a measure of the effectiveness of the policy response in stabilising the market income shock via welfare policies. It should be noted that Eq. ;employment characteristics ; andhousehold characteristics .To capture the changes in demographics and employment while retaining income information, we estimate a set of new weights for the latest SIH to mimic the demographic and employment patterns observed in the monthly LLFS, reflecting the changes in both intensive and extensive margins in the labour market. We follow the semi-parametric method developed by DiNardo et al.  to adjusAppendix Table In addition to the distributional adjustment, we also index wage income by official Average Weekly Earnings (AWE) data, and we index investment income by an assumed annual growth rate of 2.5% on top of the Consumer Price Index (CPI) until February 2020. Childcare expenses, which are used to estimate post-childcare disposable income, are also indexed using the CPI. This allows the nominal income to match with the policy parameters we use in the tax-benefit simulation.Covid-19 on the financial market, we use the default portfolio performance of the largest pension fund in Australia  and age group (7 groups). To reflect the volatile effect of Covid-induced economic shocks, the Australian government announced several temporary taxation and welfare benefit changes that directly affected employment and the income level of households, including a one-off payment for specific welfare recipients, increased unemployment and other benefits, temporary support for free childcare, and wage subsidies to employees via eligible employers.In response to the In March 2020, the government announced a one-off economic support payment of A$1,500 to nearly all recipients of specific welfare payments, including pensions, unemployment benefits and Family Tax Benefit (FTB). The majority of recipients received the first payment ($750) by mid-April, and the second payment ($750) was made starting from mid-July. Besides one-off payments, the government also introduced a temporary payment of $550 per fortnight, known as the Coronavirus Supplement, to eligible welfare payment recipients. The unemployment benefit was also doubled to $1,115.70 per fortnight and its eligibility criteria were relaxed, with recipients not required to fulfil their usual job searching obligations. Additionally, the taper rate, which determines the reduction of the payment when a partner’s income exceeds a threshold, was also temporarily changed, and asset testing was suspended for selected benefits  to assess the robustness of the results.Appendix Table As well as the unemployment rate results reported in Table In terms of income, wage and business income, on average, only experience minor fluctuations when wage subsidies are included in the reported income. Investment income dropped, as expected, due to market volatility. Income from government payments experienced the largest change over the period. It increased by more than a quarter on average due to the increased unemployment benefit as well as other welfare payments. As welfare payments are heavily means-tested in Australia, the gains in welfare payments are mostly absorbed by middle- and low-income households, as discussed in the later sections of this paper.Covid quintiles of equivalised household disposable income in dollar amounts, and shows the percentage change compared with the pre-Covid period, taking into account the observed changes in labour force participation. The market income change shows the extent of the Covid-19 shock as well as the impact of the government wage subsidy program (JobKeeper) on pre-tax earnings. As the government wage subsidy was distributed through the employer as part of the wage, or directly to the self-employed, the subsidy is included in market income. In the pre-Covid period, the estimates show that gross household incomes were mostly stable. In May 2020, gross market income increased for most quintiles, with an increase of over 20% for Q1. Q5 was the only exception, where we observe an initial decline of gross income in April but a gradual recovery since then. Table Covid period tends to be small, suggesting the initial policy response and economic shock are the dominant factors for the change in income.The increase in gross market incomes between April and June in the bottom quintiles can largely be attributed to the wage subsidy scheme, as shown by the estimates without policy responses, which is unsurprising given that the subsidy can sometimes exceed the usual wage for casual workers. In the lower bound estimates, where we assume that all jobs supported by the JobKeeper Payment and the welfare support schemes are gone, a significant decline in gross earning is seen throughout the distribution. In the upper bound estimates, where no JobKeeper-supported jobs are lost, the changes in gross income are generally less than a few percentage points. The actual impact of the wage subsidy on labour demand is likely somewhere between those two extreme estimates. The difference between the model estimates and the estimates without the policy change shows the substantial impact of the policy change, especially for households in Q1 and Q2. The variation between estimates for the post-Covid-19 contributes a maximum of 16–23% of the decline in gross income depending on the quintile, and the policy response contributes up to 44% of the pre-Covid gross income for households in Q1, and up to 29% for households in other quintiles. The net effect is reported in Table In line with the decomposition framework, as set out in the methodology section, we estimate the contributions from the income shock effect and the government policy response to the overall changes in income. Table Table Covid-19 outbreak would have been greater if there had been no policy response. Disposable income could have contracted by 3–4% for Q1 and by up to 10% for Q2 in the worst-case scenario. Due to the inability of the higher income groups to access means-tested welfare payments, Q3–Q5 would have suffered more in terms of percentage changes due to job losses, although they would still have been better off in absolute terms. Across all results, the variations over time between April and June tend to be small. As each month is estimated independently, the stability of the numeric results is a welcome sign, suggesting the robustness of the results derived from the semi-parametric approach.The impact of the Covid-19 and the policy response to changes in disposable income. Compared with the estimates of gross income shown in Table Covid-19 outbreak. Households in Q2 to Q5 would have experienced a greater reduction of income under a no policy response scenario. The income shock effect would account for up to 20% of the reduction in disposable income in the worst-case scenario. The policy response effect is a net positive for all quintiles, responsible for the increase in disposable incomes, and with Q1 households benefiting the most. The change in disposable income is dominated by the increased level of welfare payments for Q1 and the wage subsidy for Q2–Q5. Free childcare has only a limited impact, as it only affects the out-of-pocket portion of a service that is already heavily subsidised.Table Covid-19 income shock would have caused increases in gross income inequality ranging between 0.016 and 0.13 Gini points in April–June relative to February. The impact, however, is almost entirely negated by the policy effect due to the introduction of the wage subsidy. This finding is robust across both the extreme estimates, which provide the bounds of the estimates for the policy and the income effects.This section examines how the changes observed across the distribution are reflected in the changes in market income inequality  and disposable income inequality. The nowcast estimates in Table Covid-19 crisis was found by O’Donoghue et al. (Compared with the relatively stable gross market income inequality due to wage subsidies, disposable income inequality shows an apparent reduction. Compared with February 2020, when the Gini was around 0.33, the Gini was hovering around 0.31 in April, May and June. A drop of 0.02 Gini points in a few months is substantial given that the annual change in disposable income inequality measured by the Gini coefficient in Australia is usually smaller than 0.01 (Li et al., e et al.  in the UCovid-19 has had a significant impact on social and economic activities in Australia. Using the combined data from the monthly LLFS, administrative payroll information and the SIH, this paper proposes a method to reconstruct the income distribution semi-parametrically from incomplete data, and uses this method to estimate the impact of the Covid-19 outbreak and the policy response on the income distribution.Similar to other countries, Covid-19 increased unemployment, the wage subsidy initiative from the government was effective in increasing market incomes for households in the lowest and the second-lowest income quintile, with a stabilising effect for the families situated in the mid to high part of the income distribution. Inequality in gross market income was generally stable despite the significant shocks introduced by Covid-19. The relatively positive outcome of the market income changes is primarily due to the flat rate wage subsidy for part-time and long-term casual workers, who could receive an income exceeding their usual wage.While In terms of disposable income, we observe an increase in the living standards for most households except those in the wealthiest income quintile between February and June 2020, although the increase is most evident in the bottom quintile, showing a similar pattern to the change in gross income. The increase in the bottom quintile can be mostly attributed to the policy response, which also negates the income shock effects for other quintiles. Due to the significant rise in disposable income in the bottom quintile, Australia experienced a rapid drop in income inequality, reducing the Gini coefficient for equivalised disposable income from 0.33 to 0.31.Covid-19 outbreak was heavily dependent on the temporary measures. This raises the question of the impact on the income distribution once these policies are replaced or withdrawn. Future research may consider comparing the estimates when new household data become available to examine the longer-term impact of the shocks. Additionally, it could be interesting to further investigate the persistence and the heterogeneities of the income shocks.While our analyses do not imply the measures have sheltered the shock for every household in need, the overall change in the income distribution does suggest the measures are generally progressive, protecting those who are, on average, more vulnerable. Our results also suggest the change or the lack of change in the income distribution in the initial aftermath of the Covid data on behaviours of individuals and firms become available, the framework may also be used to estimate the impact of the withdrawal of the policies. Adopting this method in a cross-country context could also help to analyse whether the same tax-benefit policies may lead to similar results in different contexts, which could offer potential insights for both Covid-19 and general economic policy research.Our findings also shows the feasibility of the methodological framework with partially updated data in the case of Australia. This method can be applied to countries where data availability is more restricted, especially those with limited comprehensive surveys but with more frequent short surveys. If the post-


Cardiovascular disease continues to be one of the most prevalent medical conditions in modern society, especially among elderly citizens. As the leading cause of deaths worldwide, further improvements to the early detection and prevention of these cardiovascular diseases is of the utmost importance for reducing the death toll. In particular, the remote and continuous monitoring of vital signs such as electrocardiograms are critical for improving the detection rates and speed of abnormalities while improving accessibility for elderly individuals. In this paper, we consider the design and deployment characteristics of a remote patient monitoring system for arrhythmia detection in elderly individuals. Thus, we developed a scalable system architecture to support remote streaming of ECG signals at near real-time. Additionally, a two-phase classification scheme is proposed to improve the performance of existing ECG classification algorithms. A prototype of the system was deployed at the Sarawak General Hospital, remotely collecting data from 27 unique patients. Evaluations indicate that the two-phase classification scheme improves algorithm performance when applied to the MIT-BIH Arrhythmia Database and the remotely collected single-lead ECG recordings. Despite rapid development of clinical practices and technologies, cardiovascular diseases continue to be a common concern in modern society. It remains the primary cause of death globally; representing 31% of all deaths that occurred worldwide in 2016 [In particular, cardiovascular diseases are a major concern for elderly citizens; a growing demographic that is more susceptible to developing these diseases and more likely to succumb to them ,4. In MaThrough the use of electrocardiograms (ECG), medical staff are able to noninvasively monitor heart conditions based on electrical impulses generated by contractions of the heart muscles ,10. InteIn this paper, we design and implement a medical platform for remote monitoring and automated arrhythmia detection in elderly individuals. To address the scalability and reliability requirements when managing a large number of remote data streams, a modular approach is proposed to allow for horizontal scaling of individual functionalities within the system. For automated heart arrhythmia detection, a two-phase classification scheme is introduced to improve the classification performance of an existing algorithm. Verification of the proposed classification scheme is performed on ECG recordings from the MIT-BIH Arrhythmia Database and single-lead ECG recordings collected by the prototype system.The paper is organized as follows. Existing research works primarily explore the concept of remote monitoring through the deployment of wireless devices and wireless sensor networks ,16,17. KA key trend among current developments is the primary focus on inpatient monitoring. As a result, these works do not address the scalability and reliability challenges of truly remote monitoring. Certain works such as the TELECARE system proposed by Szydło et al.  attempt In terms of automated arrhythmia detection, various approaches have been proposed for ECG classification using algorithms and machine learning techniques. Chazal et al.  proposedRecent works have seen increased focus on deep learning techniques and artificial intelligence such as the use of neural networks. Chauhan et al.  utilize When reviewing the works covering ECG classification, no clear consensus is reached on the right methodology to employ during the data preparation phase. Many existing works do not adhere to the Association for the Advancement of Medical Instrumentation (AAMI) recommendations, resulting in performance results that are higher than expected. For example, the works proposed by Ye et al. and Song et al. have been shown to produce lower performance results when the data are prepared following AAMI recommendations . Due to Additionally, some works adhere to the AAMI recommendations but do not implement the interpatient scheme during data partitioning for the training and test datasets. When following the interpatient scheme, data from each patient can only appear in either the training dataset or test dataset to avoid learning bias. While the works proposed by Kiranyaz et al. and Qiu et al. adhere to the AAMI recommendations for excluding paced heartbeats and classifying heartbeats into five recommended heartbeat types, data from each patient are present in both the training and test datasets which can result in inflated performance results.A high-level overview of the system architecture and its primary components are demonstrated in Data within the system are primarily generated and consumed on the end user side. All requests to access or store data are routed through the reverse proxy. The reverse proxy acts as a gateway to the backend functionality, managing all incoming requests and performing load balancing when multiple instances of backend components are deployed. The web API is responsible for interpreting incoming requests and performing the appropriate actions in response. For example, the web API interacts with the database to perform data access and storage. Additionally, the web API interacts with the ECG classifier API in order to detect abnormal heart arrhythmias in collected ECG signals and generate alerts.The web API is designed around a module system. Each module provides all necessary components such as API routes and database mappings for a specific functionality. This approach allows for multiple deployments with varying feature sets as well as future extension through the inclusion of new modules. For example, new monitoring devices and the necessary functionality for handling its data can be added to the existing system, rather than requiring a complete rework of the system. The system includes two modules: (1) temperature monitoring, and (2) ECG monitoring. The web and mobile clients can provide access to all available modules or a subset as shown in The temperature and ECG monitoring modules provide functionality for remotely monitoring the body temperature, heart rate, respiratory rate, and electrocardiogram readings of a patient. The patient’s vital signs are measured using a VV200 temperature patch and VV330 ECG patch from VivaLNK. As demonstrated in Due to the high frequency of data, the mobile client acts as a buffer for incoming data using a local SQLite database. The data are transmitted to the web API in batches every 15 min by default, with varying frequencies depending on the immediate situation as indicated in When certain events such as abnormal heart rhythms occur, the system generates alerts in order to inform medical practitioners of a potential problem. The alert generation process uses a job queue to keep track of incoming data that has not been processed for abnormalities. New sections of data are marked automatically and added to the job queue. As shown in Each entry in the job queue and its associated marked section of data are loaded by the web API. For the detection of heart arrhythmias, the web API forwards the loaded ECG signals to the ECG classifier API for processing. After classification, the web API generates alerts for any abnormal heartbeats that are detected. These alerts are saved to the database for viewing by the web and mobile clients.In contrast to systems designed for on-site deployments, remote monitoring systems are not constrained by physical limitations and may support upwards of hundreds to thousands of monitoring devices that are continuously producing data. These devices may not be positioned in a fixed location and may transmit data at inconsistent timings, requiring the system to adapt to variable loads with random spikes of activity.As indicated in In the proposed system, the three major areas that affect overall scalability are the maximum number of concurrent requests, processing speed of these requests, as well as data access and storage capabilities. The two components responsible for handling all incoming requests are the web API and ECG classifier API. On the other hand, the database is the sole component that affects the data access and storage capabilities.For each of these components, a single instance of the software can only handle a limited amount of load. The maximum load that can be handled is dictated by the available system resources of the host server. In order to support a higher level of load with a single instance of the software, vertical scaling can be done by upgrading the server hardware. However, the single instance architecture requires expensive server upgrades and cannot scale indefinitely due to physical and hardware constraints.In order to address the limitations of vertical scaling, the proposed system is designed to also support horizontal scaling. Each component in the system can have one or more software instances running concurrently to service incoming requests. While horizontal scaling provides greater redundancy and flexibility, the scale of operations ultimately determines the overall cost and performance efficiency. At smaller scales, horizontal scaling is inefficient due to the increased overhead and complexity of managing multiple software instances. Instead, the system can initially be scaled vertically on a single server to avoid the time and monetary costs of managing multiple servers. Once the number of users exceeds the processing capabilities of the single server instance, horizontal scaling of individual components can be performed based on the specific usage patterns and bottlenecks encountered.A prototype setup was developed and deployed for use by the Sarawak General Hospital in Malaysia to evaluate the remote monitoring capabilities of the proposed system. The system was tested on a total of 27 unique patients, eight of which are stroke patients. The VV330 ECG patches from VivaLNK were used to monitor the heart rate, respiratory rate, and ECG signals of the patients for a period between 24 and 72 h. With the exception of the eight stroke patients, all other users were remotely monitored outside the hospital premises.Android phones were used as the mobile client due to their lower cost and wide availability. A mobile client application was developed using the Kotlin programming language, with native Android features such as SQLite being used to implement data caching. For communication with the ECG patches, a software development kit (SDK) created by VivaLNK was used to receive incoming data from the ECG patches over a Bluetooth connection. In order to facilitate the remote monitoring process, a web client was also developed using standard web tools such as the hypertext markup language (HTML), cascading style sheets (CSS), and the Javascript programming language (JS).For the backend, a single instance of each component is used as it was sufficient for the number of patients being monitored. The Nginx web server is used as a reverse proxy for the system. The web API and all associated modules are implemented in the Javascript programming language using the Node.js platform. For the ECG classifier API, the Python programming language is used as the selected ECG classification algorithm was originally developed in the same language. Finally, all data access and storage in the system is handled by a PostgreSQL database with a time-series extension, providing the structure of a relational database and the flexibility of a non-relational database.For the automatic classification of single-lead ECG recordings, the classification algorithm proposed by Mahajan et al.  was chosThe ECG classification was performed using a random forest classifier. The classifier consisted of an ensemble of 220 decision trees used to categorize four classes of ECG rhythms: normal, atrial fibrillation (AF), other AF, and noisy. Each decision split of the random forest classifier operates on a random subset of features, increasing the distinctiveness of outputs among the decision trees.The MIT-BIH ECG Arrhythmia Database  was usedThe training and test datasets were derived following the interpatient scheme to avoid bias in the classification performance. Under this scheme, the recordings were divided between the two datasets such that heartbeats from one patient were not used simultaneously for both the training and testing of the ECG classifier. Thus, the training dataset was composed of heartbeats from recordings 101, 106, 108, 109, 112, 114, 115, 116, 118, 119, 122, 124, 201, 203, 205, 207, 208, 209, 215, 220, 223, and 230. Meanwhile, the test dataset was composed of heartbeats from recordings 100, 103, 105, 111, 113, 117, 121, 123, 200, 202, 210, 212, 213, 214, 219, 221, 222, 228, 231, 232, 233, and 234.The PhysioNet Computing in Cardiology Challenge 2017  was usedAs with the MIT-BIH records, the challenge data were divided such that each recording was only used once in either the training dataset or the test dataset. Starting from the first recording labelled A00022, each subsequent record was assigned to either the training or test dataset in an alternating fashion. Under this scheme, record A00022 was first assigned to the training dataset, followed by record A00034 being assigned to the test dataset, and so forth. Initially, all ECG signals from the PhysioNet challenge data were upsampled from 300 Hz to 360 Hz using the Fourier resampling technique in order to match the ECG signals in the MIT-BIH database. Next, two median filters were applied to all ECG signals with a width of 200 ms and 600 ms in order to remove baseline wander and reduce signal spikes caused by noise. After filtering, amplitude normalization was performed on the resultant signals by rescaling to fit within a range between 0 and 1. The normalized signal eliminated potential biases from differences in the amplitude between different ECG signals.After signal preprocessing, each successive PQRST wave present in the ECG signal was extracted as an individual heartbeat as demonstrated in Identifying abnormal heartbeats and their specific types from an ECG recording is a complex task. As demonstrated in existing studies, classification performance is inconsistent and varies based on the specific type of heartbeat that is being classified. In addition, certain applications such as anomaly detection and alert systems may only require a distinction between normal and abnormal heartbeats without requiring the specific abnormality type. In these scenarios, the additional computation performed when identifying the specific heartbeat abnormality type is unnecessary.Therefore, the two-phase classification scheme was proposed with the objective of developing a technique for improving the performance of existing ECG classification algorithms. The classification scheme was designed with the intention of being algorithm-agnostic, allowing it to be replicated for other ECG classification algorithms besides the one discussed in this paper. Additionally, this approach allows for the combination of different classification algorithms that are specialized for the classification problem faced at each of the two stages.Decomposing the classification process into two distinct stages provides several tangible benefits. The classification complexity at each of the two stages is reduced compared to the original approach of only classifying once. Each stage of the classification process places greater emphasis on addressing a specific problem. The first stage is focused on determining whether a given heartbeat is considered normal or abnormal, while the second stage focuses on identifying the specific abnormality type for heartbeats that are not categorized as normal.The two-phase classification scheme was designed based on the classification approach commonly employed by cardiologists. Firstly, the baseline characteristics of a normal heartbeat is established for a given patient. Once the baseline is established, abnormal heartbeats that are present in the ECG signal are identified as heartbeats that deviate from the norm. Finally, the abnormal heartbeats are classified into specific categories based on their characteristics.In order to perform the two-phase classification, a composite model consisting of two separately trained models is used. In order to assess the classifier performance during training and testing, three metrics are employed: accuracy, sensitivity, and specificity. When evaluating the performance of the heartbeat classifier, the overall results for each of the outlined performance metrics is calculated. Additionally, each performance metric is also calculated on a per-class basis for each heartbeat classification label.The terms TP, TN, FP, and FN denote true positive, true negative, false positive, and false negative respectively. True positive and true negative represent the number of samples that are accurately classified as belonging or not belonging to a given class. Conversely, false positive represents the number of samples that are misclassified as a given class that they do not belong to. Finally, false negative represents the number of samples that belong to a given class but are misclassified as a different class.Equation (1) denotes the accuracy metric, measuring the total number of heartbeats that are correctly classified across all classes. Equation (2) denotes the sensitivity metric, measuring the proportion of heartbeats that are correctly identified as positive for a given class. Equation (3) denotes the specificity metric, measuring the proportion of heartbeats that are correctly identified as negative for a given class.Initially, the random forest classification model based on the selected ECG algorithm is developed using the training dataset. An initial population of 10 individuals is randomly generated. Each individual in the population consists of an ensemble of bagged decision trees with an estimated 220 learners. All training of the initialized random forest model is performed exclusively on the training dataset to avoid introducing biases and discrepancies in the final classification performance. Once the model is fully trained, its classification performance against unseen data is evaluated using the test dataset. The accuracy, sensitivity, and specificity metrics are calculated in order to benchmark the final performance of the model.After the baseline evaluation is performed, a two-phase classification scheme is applied to the same ECG classification algorithm. The proposed method is derived based on the approach typically employed by cardiologists when identifying abnormalities in ECG recordings. For the two-phase classification scheme, modified datasets are created for the training and evaluation of a composite model. Finally, the results of the baseline evaluation and the composite model are compared to determine whether there are measurable improvements in the evaluation metrics. Evaluation is also performed against a single-lead ECG dataset collected by the prototype system. The single-lead ECG signals were prepared following the outlined data preparation methodology, with the heartbeats being hand-labelled by a certified cardiologist from Sarawak General Hospital (SGH).In order to validate the benefits of horizontal scaling, test deployments were performed with a dynamic number of web API instances. The web API was chosen for evaluation as it performs the primary processing and data management tasks within the system. Additionally, the performance characteristics of the employed database software when sharding horizontally have been thoroughly tested and documented. A total of eight separate deployment configurations were used for testing. Each deployment had a fixed number of web API instances ranging between 1 to 8 instances. In order to simulate realistic cloud deployment scenarios, each web API instance was isolated using Linux containers allocated with 1 dedicated CPU and 512MB of system memory. The reverse proxy distributes incoming requests evenly between the running instances. A HTTP benchmarking tool was used to stress test the deployments and collect performance metrics.The horizontal scaling was evaluated using three performance metrics: requests per second, latency, and errors. The requests per second metric refers to the number of HTTP requests that were successfully processed by the system. Latency was computed as the time from the sending of the first byte of the request to the time the complete response was received. Finally, the errors were computed as the number of requests that receive an erroneous response or requests that were not processed due to overloading of the system by the benchmarking tool.Initial usability evaluation of the prototype system was performed at the Sarawak General Hospital in Malaysia. In total, the system was used on 27 unique patients to identify the effects of using a remote monitoring system. Each patient was outfitted with a VV330 ECG patch for continuous monitoring over a period between 24 to 72 h.The application of wireless ECG patches enhanced patient compliance to prolonged cardiac monitoring in several ways. The continuous monitoring mechanism reduced the burdens typically associated with patient-initiated intermittent monitoring strategies using conventional ECG, Holter and novel mobile devices. Examples of these burdens include failure to adhere to ECG monitoring intervals and improper usage of the ECG monitoring devices.Additionally, the wireless and waterproof features of the ECG patches resulted in tolerable comfort levels during the prolonged monitoring periods above 48 h. As the device is small and lightweight, patients were able to shower throughout the monitoring period. The absence of wired skin electrodes also reduced motion artefacts during ambulation and other daily activities.For the baseline evaluation, the classification algorithm proposed by Mahajan et al.  was usedFrom the confusion matrix, two extremes in terms of predictive performance are demonstrated. The N, V, and Q labels are shown to have a high percentage of their samples that are correctly classified by the model. The percentage of correctly classified samples for the N, V, and Q labels are 88.9%, 89%, and 97.7% respectively. On the other hand, the model is shown to perform poorly when classifying samples that belong to the S and F labels. For the S and F labels, the percentage of correctly classified samples are 0% and 3.6% respectively.The model exhibited a specific characteristic when classifying samples that belong to the S and F labels. When examining the classification results for these labels, it is clearly shown that the majority of the samples belonging to these labels were misclassified as the N label. A total of 95.1% of the samples belonging to the S label were misclassified as the N label, while 77.8% of the samples belonging to the F label were misclassified as the N label. As for the remaining samples under the S and F labels, the majority of the misclassification occurs when the samples were mislabeled as the V label.Generally, the performance metrics of the model paint a positive picture as the model achieved an accuracy score of 89.481% and a specificity score of 92.322%. However, the poor performance of the model when distinguishing heartbeats under the S and F labels resulted in a sensitivity score of 55.827%. The results from both Both model A and model B are trained and tested using all available data within their respective training datasets. Model A was trained on a total of 17,190 heartbeats while model B was trained on a total of 8148 heartbeats. For the evaluation of the two models after training, a total of 17,033 heartbeats were used for testing model A while a total of 8268 heartbeats were used for testing model B.The classification results for model B on the test dataset are summarized in After both model A and model B were trained, the two model instances were combined in order to form the composite model. The performance of the composite model was evaluated using the same dataset as the baseline evaluation, containing a total of 12,648 heartbeats. The heartbeat classification results on the test dataset are presented in Referencing the confusion matrix, the model is shown to have better predictive performance when classifying heartbeats that belong to specific labels. The model was able to classify a high percentage of the samples that belong to the N, V, and Q labels. Heartbeat samples under these labels were correctly classified at a rate of 89.8%, 82.5%, and 91.9% respectively.In comparison, the model performed poorly when classifying samples that belong to the S and F label. For the S label, the model was only able to correctly classify 45.6% of the heartbeat samples. The majority of the remaining samples under the S label were misclassified as the N label. A similar trend is apparent when examining the F label, as the model was unable to correctly classify any of the samples correctly and the majority of the misclassified samples were predicted to be under the N label.During the baseline evaluation, a key trend can be identified from the classification results. When adapted to work on the MIT-BIH Arrhythmia Database, the selected ECG classification algorithm demonstrated polarizing performance depending on the type of heartbeat being classified. The baseline model performed well when classifying heartbeats that belong to the N, V, and Q labels. However, the model was shown to be unable to correctly identify heartbeats that belong to the S and F labels.The same trend was present to a lesser degree when examining the classification results of the composite model. The composite model also performed well when classifying heartbeats under the N, V, and Q labels. Additionally, the composite model was also unable to identify heartbeats that belong to the F label. The key distinction was the marked improvement in classification performance for heartbeats under the S label, resulting in greater overall metrics as shown in The preparation process of the composite model provides greater insights into the predictive capabilities of the selected ECG algorithm. The algorithm is found to perform well when classifying abnormal heartbeat types, but faced difficulties in distinguishing between normal and abnormal heartbeats. In In contrast, model A, which was trained specifically for distinguishing between normal and abnormal heartbeats, was only able to achieve lower classification performance results as shown in In order to validate the classification performance against single-lead ECG signals, an additional dataset is prepared using the data collected by the prototype system at the Sarawak General Hospital in Malaysia. A total of eight stroke patients were monitored remotely using VV330 ECG patches from VivaLNK for a period between 24 and 72 h. From the collected ECG recordings, a subset containing 5 h of ECG strips was extracted and the heartbeats were hand-labelled by a certified cardiologist.The extracted ECG signals were preprocessed following the previously established data preparation process. As the ECG patches record the ECG signals at 128 Hz, the signals were first upsampled from 128 Hz to 360 Hz using the Fourier resampling technique. Two median filters of 200 ms and 600 ms width were used to remove baseline wander from the upsampled signals, followed by amplitude normalization to a range between 0 and 1. Finally, the heartbeats were segmented using a window centered around the R-peaks with a size of 600 ms. The classification results for the baseline model and composite model are shown in In comparison, the composite model demonstrates better performance when classifying heartbeats belonging to the N and V labels. When comparing the two models directly, the composite model generally demonstrates a higher performance across all metrics. Exceptional cases where the baseline model had higher performance results were the accuracy and specificity for the F label, as well as the specificity for the N label. When evaluating the overall metrics, the composite model is shown to be the better classifier as it outperformed the baseline model across all metrics.To summarize, this research has successfully proposed and evaluated a scalable remote patient monitoring platform with automatic heart arrhythmia detection. A modular approach was employed for both the high-level architecture and the software design, allowing for greater horizontal scaling as the number of data streams increases. A prototype was developed and deployed for use by the Sarawak General Hospital in Malaysia, with a total of 27 unique patients being remotely monitored between 24 and 72 h.For automated heart arrhythmia detection, a two-phase classification scheme was proposed in order to improve the classification performance of existing algorithms. An existing random forest classifier algorithm was retrained and reevaluated based on the proposed methodology. Evaluation of the experimental results indicate that the composite model from the two-phase classification scheme had a higher overall classification performance when compared to the original algorithm. The same results were reflected when classifying single-lead ECG recordings collected by the prototype system.


Background. Adolescent idiopathic scoliosis (AIS) is included into the category of pathologies that could affect postural control. Rarely AIS shows symptoms but often compromises the normal positioning of the head, trunk and, more generally, of the limbs in the space. We used a stabilometric platform to evaluate the motor control outcomes during a self-elongation in girls with AIS. Methods. In 10 girls with AIS, we evaluated the center of pressure (COP) modifications on a baropodometric platform in a standing position and after a self-elongation correction. Results. All the outcomes (except the eccentricity) showed an increasement during the self-elongation exercise even if the significant differences were not found. Conclusion. SE correction contributes to ameliorate the COP symmetry with a better repercussion on the balance management. This highlights the importance of repetitions during all activities of daily life. Scoliosis is a three-dimensional spine deviation that is clinically recognized when the deviation on the coronal plane exceeds 10° Cobb. Scoliosis is 20% secondary to another pathology such as cerebral palsy or muscular dystrophy and injuries or infections of the spine, and 80% idiopathic, that means of unknown etiology [Rarely is it symptomatic  but in sThe most used method to evaluate the postural control is the assessment of center of pressure (COP) ,15,16,17The use of a stabilometric platforms  ,23,24 coIndeed, self-elongation (SE), as suggested by many rehabilitation methods, can be defined as a self-correction exercise with an active recruitment of the trunk muscles aimed to temporarily re-arrange the vertebrae in a straight position. Often, self-correction is recommended to patients as a routine exercise during daily life activities ,25. In pThus, the aim of this study was the evaluation of the changes of COP during specific SE exercise to verify if the correction of the alignment of the spine had a secondary benefit correlated to body balance arrangement.A convenient sample of 10 girls (age 13–15 years) with a diagnosis of AIS were freely recruited during the weekly routine rehabilitation session. All patients had a diagnosis of primary thorax scoliosis defined as right convex and a frequency of clinic gym visitation of twice weekly. They were under physiotherapist control from 28 months (on average).Anthropometric measurements were performed as described elsewhere . Weight The Risser sign  is deterParental consents were obtained before participation in the study, completing a secondary anamnesis to verify other impairments or psychological transitory stress that could affect the compliance of the subjects.All procedures were in accordance with the Declaration of Helsinki (1975) as revised in 2008 and were preventively approved by the clinic ethic committee .®, Sensormedica, Roma, Italy). Dimensions were 120 × 50 cm and the use of a sampling frequency up to 400 Hz guaranteed a high level of accuracy during the stance phase  according to the scholastic obligations of the students; in the same clinical setting with a constant temperature of 21 centigrade. All AIS subjects stood barefooted over the stabilometric platform: Subjects were asked to keep their feet in contact with the platform, hang their arms along the trunk; look straight ahead toward a mirror 3 m far to avoid movement response . In partSE exercise is performed with the auto-correction of the trunk-alignment and spinal deforment with a specific focus on postural balance and proprioception. In particular, the general routine of exercise was composed by four to six exercises: upwards spine elongation; major curve translation on the frontal plane, towards the side of the concavity; vertebral rotation on the horizontal plane; thoracic kyphotization (when necessary); lordosization of the lumbar spine (when necessary). All the subjects performed four exercises maintaining the correct position for at least 15 s with a short rest time (up to 7 s) between exercises. The last two exercises were performed only if deemed necessary from the physiotherapist. All the SE sessions were conducted by the same examiner in the same laboratory environment to minimize any noise or other disturbances. 2) and the speed of the COP displacement . In particular, the eccentricity value of the ellipse (e) was extracted as an index of the symmetry of the displacement of the COP. This value defines the shape skewness of an ellipse considering the length of the two axes. This value ranges between 0 and 1; whereas if the index corresponds to zero the ellipse is a circle while in case of e = 1 the ellipse corresponds to a line.The software provided the maximum display of the COP (mm) along the X  and Y (antero-posterior direction) axis (sway-X and Sway-Y) such as the total area  data were matched and compared with a non-parametric approach through Wilcoxon signed-rank test. The significance was set at a p-value less than 0.05 and eventually the Effect Size (ES) used to verify the biological consistence of the differences. Statistical analyses were performed using The Jamovi project (2021): COP sway outcomes are described in In the last years, the improvement of technologies such as baropodometric platforms helped physiotherapists to ameliorate the evaluation and then the treatment of pathologies that could affect postural control and balance. In particular, the aim of this study was to assess COP sway in adolescents with AIS during SE correction, because AIS subjects’ postural sway is lower than healthy subjects and become wider when performing dynamic adaptations ,30,31.Our results showed a small increment in Sway-X, Sway-Y, Sway Area and Speed Sway that could be interpreted as a negative outcome from an early evaluation. Instead, it is necessary to consider all the modalities of movement as a confounder for COP arrangement. For example, Pasha et al.  found, ie index , whereas the oscillation in medio-lateral and antero-posterior direction become similar thanks to a more rounded Sway Area. Our results underlined that the postural control in adolescents with AIS tends to modify through SE exercises and the subsequent body arrangement. In fact, proprioceptive and visual information changed with a shift to a new asset. These changes in torso asset and in head-neck position affected COP sway, which tends to equalize the two axes.Considering these assumptions, the sway trend could be considered as a modification of the motor control in patients during SE performance; complex and non-immediate adaptations seem to be promising to enhance postural control . In thisWe are conscious that this study had some limitations. First, the small sample of recruited subjects did not allow us to perform a more in-depth analysis. Nevertheless, participants were all young girls with similar deviation contributing to homogenize the data and to exclude bias caused by gender differences. Finally, the novelty of this work does not only consist in the evaluation of the scoliotic curve correction level but mainly in measuring the variations of the COP  displacement during a physical correction.In conclusion, we stated that SE correction could contribute to ameliorate the COP symmetry with a possible better repercussion on balance management. This highlights the importance of the implementation of postural control obtained by SE correction during all activities of daily life.


Strain data of structural health monitoring is a prospective to be made full use of, because it reflects the stress peak and fatigue, especially sensitive to local stress redistribution, which is the probably damage in the vicinity of the sensor. For decoupling structural damage and masking effects caused by operational conditions to eliminate the adverse impacts on strain-based damage detection, small time-scale structural events, i.e., the short-term dynamic strain responses, are analyzed in this paper by employing unsupervised modeling. A two-step approach to successively processing the raw strain monitoring data in the sliding time window is presented, consisting of the wavelet-based initial feature extraction step and the decoupling step to draw damage indicators. The principal component analysis and a low-rank property-based subspace projection method are adopted as two alternative decoupling methodologies. The approach’s feasibility and robustness are substantiated by analyzing the strain monitoring data from a customized truss experiment to successfully remove the masking effects of operating loads and identify local damages even concerning accommodating situations of missing data and limited measuring points. This work also sheds light on the merit of a low-rank property to separate structural damages from masking effects by comparing the performances of the two optional decoupling methods of the distinct rationales. Structural health monitoring (SHM) that consists of multidisciplinary technologies such as sensor, data processing, computer modeling, and mechanics inverse analysis can be responsible for the aging of infrastructures with the advantage of reducing the cost of the visual-based inspection and raising the efficiency of safety assessments. For the past two decades, there has been a rapid rise in the use of the civil SHM system ,2. The pIn fact, SHM data interpretation to reveal the structure’s health state has received much attention over the last two decades, though most studies only used limited data. These data interpretation strategies could be roughly divided into two families: model-based and data-driven strategies ,8. The fThe inevitable damage accumulation in the in-service infrastructure firstly gives rise to the local performance degradation of the structural components/system. Since strain monitoring data from SHM activities can directly relate to the stress redistribution in the vicinity of the strain sensor, such as damage initiation and development, strain-based damage detection approaches are recognized as great importance to structural health diagnosis ,22,23,24Conventionally, supervised models can be constructed by the regression between environmental parameters and structural damage-sensitive features to reduce the adverse impacts of these time-varying conditions in a structural health diagnosis. Much research considers the corresponding physical principle as a black-box model and assumes that all its parameters can be determinate from the training data, such as utilizing the measured temperatures and quasi-static strains in a long-term period ,31. NeveAn alternative data-driven strategy uses environmental effects as latent variables by employing unsupervised multivariate statistical tools. Following the orthogonal projection methodology, such as the principal component analysis (PCA), the new multivariate data in the hyper-plane projection space could be split into two distinct ingredients to represent the effects of dominant environmental factors and noises or anomalies, respectively ,37. Yan In this paper, an idea that offers data interpretation in a distinct time scale is employed. Short-term dynamic strain response data dominantly rendered by the operating loads are applied to the analysis. Due to the fact that the frequency of environmental conditions  is considerably lower than the frequency of structural vibration under operating conditions , the analyzed signals for a shorter temporal length no longer depend on the temperature-dominated correlation ,43,44. MIn this context, there is a two-step approach brought forward in this work to processing raw strain monitoring data only under operating loads for damage detection: in the first step, since relatively high-frequency dynamic strains increase the amount of data, the wavelet analysis tool is first used to process the strain responses to achieve initial feature extraction; in the next step, two data-driven methodologies, PCA and another one denominated as the low-rank subspace projection are presented and applied in this work, respectively, for decoupling the effects on the strain-based feature data of the operating loads and anomalies , thereby getting corresponding local and global DI values. On these bases, an output-only damage detection strategy is proposed, consisting of data collection, anomaly detection and the above two key steps and executes these procedures efficiently and successively in the sliding time window.To validate the damage detection strategy developed, we customize a steel truss model and its reaction frame system for continuous random excitations that can simulate time-varying operating loads, thus acquiring several independent vibration data to form the raw strain monitoring data set analyzed. The proposed two-step approach is carried out to obtain the global DI values and their outlier analysis results in each sliding time window and to exhibit the local DI values once the damage alert is raised. The results show that the strategy can detect the damage presence in the truss structure and the evolution from one to another damage state in time. The effects of damage localization from the two alternative decoupling methods are fully compared in the experiment. Moreover, the performance of the damage detection strategy is further evaluated concerning the cases of missing data and limited sensor deployments.The two-step approach for data interpretation refers to Steps 1 and 2 shown in The wavelet theory provides a time–frequency analysis tool for both stationary and nonstationary signals. One of the main goals of wavelet analysis is to extract useful information from raw data, such as signal features and image edges. Based on wavelet multiscale decomposition, one vibration signal can be characterized as many sequences with different levels and frequencies without losing any components. For example, bridge strain monitoring data can be conveniently decomposed into approximate and detailed components , separatith sensor channel, namely jth decomposition level in terms of the wavelet packet. A segment of strain sequence at the To provide a way of characterizing dynamics in the vicinity of sensor jth decomposition level, we can extract the energy feature of the chosen sub-frequency band sequence Consequently, at the The WPES defined in this study involves a segment of strain sequence, and it is associated with both operating loads and structural intrinsic characteristic. Therefore, the feature extraction in this step only plays a role in data reduction and noise reduction but not damage alert, thus called the initial feature extraction  and The corresponding global indicator is the Euclidean norm of This section presents another data-driven methodology that both the PCA and the low-rank property of the data structure ,17 can uith or kth sensor.ith strain sensor. The matrix Usually, several strain observations are obtained, and the WPES feature data can be extracted. Accordingly, we list the corresponding projection data in the second principal subspace, i.e., We define the expectation of The corresponding global indicator is defined as the Euclidean norm of the vector consisting of As outlined in It is not easy to directly measure operational loads such as traffic, train-bridge-coupled excitation, or complex current fields. Therefore, only the dynamic components in strain monitoring data are imported in chronological order in the data acquisition module. The imported data should be short-term, i.e., second-order, but not necessarily continuous, and could be obtained through multi-resolution analysis of the raw strain monitoring data . We emplCompute the WPES features according to Equations (1) and (2) with the Daubechies wavelet at appropriate scales. The Daubechies wavelet is often chosen due to its discrete wavelet counterparts used when embedding the algorithm at the sensor level.Obtain the global and local DI values for decoupling of feature data according to the procedures introduced in The subsequent two key steps are implemented in each sliding time window. The window is updated iteratively through each moving with For the purpose of damage detection, an outlier analysis is performed to classify between the undamaged and damaged states after the global DI values are obtained. Outlier analysis usually encompasses comparing global DI values to a set of control limits that are defined by the threshold based on the reference/baseline data obtained from the undamaged state. In general, the threshold is set to three times of standard deviation of the baseline data that should be determined in advance. Any future observations of global DI values outside the threshold , as shown in Additionally, what we know about the difference between sensor temperature compensation and structural thermal effect is great in terms of the data processing in situ, although environmental temperature variations cause both. The former is because the temperature affects the central wavelength of the optical fiber sensor probe, which is an adverse impact that must be eliminated or can be easily eliminated. The latter is one of the problems to be solved urgently in SHM field, which leads to a perceptible quasi-static response of the structural component and should be accounted for to avoid masking the effects caused by structural damage. Since the time domain response of each excitation is short and the excitation starts from the zero equilibrium, there is no need to perform temperature compensation of the sensor. In addition, it was stated in the Introduction, existing studies have shown that the thermal effect has no significant impact on the vibration responses on a small timescale.The damage was approximately simulated by a decrease in the cross-sectional dimensions of an element. As shown in As illustrated in Although the experimental data set has been displayed, we can implement the damage detection strategy introduced in i, but for the sake of space, only ss1 and ss15 are shown. The maximum energy rule is applied for sub-band sequences to determine the extracted sub-frequency band and avoid noisy (high-frequency) ones. We have selected the fourth sub-frequency band, then used it to derive the initial feature WPES based on Equation (2).The first 200 sampling points in the first sliding time window of all the strain sequences have been used as examples, as shown in Subsequently, the sliding time window is pushed forward by 200 sampling points, and the next WPES value is extracted, the corresponding WEPS data set is updated, and so on. Sampled data can be analyzed in real-time and output the detection results. A total of 330 initial features were extracted from each strain channel in the experiment, and each obtained WPES value corresponds to the time interval of one second. We can see from the WPES values of Given that the introduced damage presences in the previous section, we have set the damage scenarios of the experimental truss with undamaged of 0–110 s, 1st DS of 111–220 s, and 2nd DS of 221–330 s. However, the WPES data set is typical of time-varying load-dominated response features, thus masking the effects caused by structural damage.The decoupling step is executed instantly after the initial feature extraction step to calculate the DI and prepare the anomaly detection. A relatively small data set, which is the WPES matrix (30 × 15) herein, is available in each sliding time window, entailing less computational cost and more timely capture of the rapid change. There are two alternative ways to deal with this initial feature matrix. SVD is performed to get the first eigenvector and then to calculate the global DI based on Equation (7); the initial feature data are projected to their second principal subspace in pairs to form the LSP vector according to Equations (8) and (9) and, then, to calculate the global DI based on Equation (11). No matter which decoupling method is used, the global DI values obtained have experienced two large spikes synchronized with the damage initiations . These can also be observed in detail in the subfigure below. Both methods are able to use the In addition, we found in For a clear visual comparison of the performance of local damage identification by initial WPES only, WPES-based PCA, and WPES-based LSP, we list the DI values of all elements at the moments of damage initiations  in It is evident from Furthermore, the clarity is presented to characterize the performance of the proposed DI for conveniently and accurately identifying local damage see . Note thAs a result, the outcomes of this experimental case study can be summarized as follows: First of all, the proposed output-only two-step approach processes the raw strain monitoring data efficiently from the vibration test to detect the elemental damages with the successful removal of the masking effects of operating loads, as shown in However, although the proposed approach has drawn the global DI for damage alert and the local DI for identifying local damages, both of which can facilitate online diagnosis of structural condition employing unsupervised modeling, the performance of these indicators for indicating damage quantification has not yet been discussed in this experiment. The rods were replaced with weak uniform ones to simulate local damage in the experimental truss. Different degrees of damage are difficult to simulate in this study in practice. If targeting damage quantification, the numerical simulation based on a refined finite element model and appropriate supervised machine learning tools is necessary, which is too vital for further nuanced structural condition assessment.A sliding time window is employed to calculate the global and local DI values successively in this paper. The raw strain monitoring data could be picked up in an equal interval according to Equation (12) when an appropriate time window is determined in advance. In the previous researches ,40, the Four window sizes  used as an example are chosen at a specific interval and for a smaller length than the baseline data. The global DI values obtained by the WPES-based PCA and LSP methodologies are displayed in Even though the raw strain monitoring data were utilized to substantiate the proposed damage detection strategy, the data set in this study might be restricted by a series of single loadings in the experiment. In addition, SHM with extensive data on the actual operation structures has very little public data-sharing, and especially the damage detection needs to reproduce the damage on operation structures to validate the algorithm, which is difficult to achieve at this stage. However, there are increasing cases of using strain sensors to monitor vulnerable parts of bridges ,50,51 orIn the SHM field, the issue of incomplete and erroneous monitoring data is widespread because of reasons such as inappropriate sensor deployment, sensor failure or replacement, poor quality of data transmission or power supply, and structural maintenance. Inside, the missing data issue may impede damage detection in time and lead to false analysis. There are currently two ways to deal with the missing data issue. One is the effective data cleansing or named augmentation algorithm that makes SHM data more reliable for online monitoring . AnotherTo simulate the case of continuous data missing, we chose a block of strain monitoring data and replaced it with null to represent missing values. entified . Note thAs the focus of the study was on the approach to processing the strain monitoring data, there is some likelihood that the strain-based DI could generally detect the local damages in the vicinity of the deployed strain sensors but not offer a distributed damage localization of a relatively large-scale structure. In this example, if the damage occurs outside the installed area, the proposed approach is also understandable to carry out structural damage detection using only a few measuring points rather than localizing the coordinate position of damage. We assumed that only a substructure is monitored  in the eAs the damage detection results demonstrate in This paper presents a damage detection strategy based on data-driven modeling of the raw strain monitoring data. The key goal of the strategy is to develop a two-step approach to successively processing the short-term dynamic strain data in the fixed-size sliding time window and to decoupling structural damages and masking effects from operating loads in initial strain-based feature data (WPES). An experimental truss was customized with an emphasis on simulating time-varying operating loads through continuous random excitations. The proposed two-step approach’s feasibility and robustness were substantiated by analyzing experimental data under different damage scenarios.The results have shown that both data-driven methodologies (PCA and LSP) in the decoupling step can perform well in detecting the damage presences through the corresponding global DI. Meanwhile, these DI values can return to a new stable state after damage alert to detect further damages. Nevertheless, in terms of DIs for local damage identification, it appears that the performance of damage localization based on the LSP rationale is better than that based on PCA. Unlike the PCA indicator, the low-rank property of the DI by LSP makes appear the fluctuations between approximately zeros and spikes when identifying local damages, thus making it independent of the baseline. Furthermore, the robustness of the proposed approach was preliminarily demonstrated in the experiment to accommodate situations when different sliding time windows were employed, measurements were missing entirely, and measuring points were limited.From the SHM prospective, finding an indicator that is not sensitive to the effects of environmental and operational conditions and related only to the changes of structural parameters such as local stiffness is a critical part of achieving unsupervised structural condition assessment. Long-term environmental factors  have quite complex correlations with the strain/stress time variations, making it difficult to account for or eliminate them. Conversely, this work analyzed small time-scale structural events, i.e., the dynamic strain responses under short-term operating loads. The well-defined structural behavior thus permitted that the proposed approach could split the damage effects from the external load ones once the damage initiates, and at the same time, the low-rank property-based unsupervised machine learning tool should be a renewed valuable aid for splitting between the damage and masking effects. In addition, the data set in this study might be restricted by a series of single loadings in the experiment, so we are currently investigating the generalized frame from our two-step approach for more practical cases, such as strain responses obtained from the train-bridge interaction system.


The methodology addresses the design of general inertial MEMS devices employing suspended proof masses, in which the focus is typically on the dynamics associated with the first vibration modes. In particular, the proposed design tool is tested on a triaxial beating-heart MEMS gyroscope, an industrially relevant and adequately complex example. The sensor layout is schematized by treating the proof masses as rigid bodies, discretizing flexural springs by Timoshenko beam finite elements, and accounting for electrostatic softening effects by additional negative spring constants. The MEMS device is then optimized according to two possible formulations of the optimization problem, including typical design requirements from the MEMS industry, with particular focus on the tuning of the structural eigenfrequencies and on the maximization of the response to external angular rates. The validity of the proposed approach is then assessed through a comparison with full FEM schematizations: rapidly prototyped layouts at the device level show a good performance when simulated with more complex models and therefore require only minor adjustments to accomplish the subsequent physical-level design.In this paper, we propose a novel design and optimization environment for inertial MEMS devices based on a computationally efficient schematization of the structure at the a device level. This allows us to obtain a flexible and efficient design optimization tool, particularly useful for rapid device prototyping. The presented design environment— The popularity of inertial MEMS  sensors, namely accelerometers and gyroscopes, has grown continuously in recent decades. These devices, initially developed for military and space use, are now found in several automotive and consumer electronics applications and are employed in a large number of everyday products .Both MEMS accelerometers and gyroscopes employ internal suspended-proof masses to detect linear accelerations and angular velocities, respectively. In particular, the most relevant inertial MEMS devices in terms of industrial diffusion and complexity in the internal microstructure are probably Amplitude Modulated (AM) Coriolis Vibrating Gyroscopes (CVG): these employ vibrating proof masses and leverage coupling effects between the structural resonant modes, which, in the presence of external angular rates, are caused by Coriolis forces ,2,3. Thisystem level, at the top, focuses on block-diagram and lumped parameter descriptions, leading to a coupled set of ordinary differential equations (ODEs) to describe the dynamics of the system. At the device level, macro-models or reduced-order models are usually employed to capture the essential behavior of the system components while having a compatible description with the system level. At the physical level, the device is studied as a three-dimensional continuum, whose behavior is described by partial differential equations (PDEs) and modeled through finite-element, boundary-element, or finite-difference methods. Finally, the process level, at the bottom, is where the detailed design of the process sequence and the photomask for manufacturing is performed. Here, the modeling is a highly sophisticated numerical activity for which a number of specific commercial tools have been developed.The design of complex MEMS gyroscopes and inertial MEMS devices in general is not an easy task. As explained in , the usuAt each of the presented levels of detail, the design of inertial MEMS devices is usually trial and error and therefore largely depends on the expertise of the design engineer. MEMS devices are typically designed starting from simple mechanical elements  that are then combined through scaling laws, parametric studies, and engineering insight. In general, finding a satisfying combination that complies with technological, application, and performance requirements involves a highly costly and time-consuming design process. Many iterations are in fact manually performed to adjust the local geometries of the device and to validate the expected improvements via numerical simulations and experimental studies ,11. In oAn interesting strategy to speed up the design of MEMS devices is the use of parametric macro-models, implemented in several tools developed in academia, such as NODAS from Carnegie Mellon University  or SUGARThe optimization technique with the biggest potentialities of application in the inertial MEMS industry is structural size optimization: in this approach, the dimensions and positions of the mechanical elements composing the structure are parametrized by a set of design variables, which are then optimized by employing usual methods for numerical optimization, such as gradient-based  or stochTopology optimization techniques that do not require any initial layout parametrization are also gaining traction in the field, mainly due to their ability of exploring non-intuitive design spaces . This apIn the case of MEMS gyroscopes, one of the most challenging design cases is represented by triaxial layouts, whose complex internal microstructure has to be carefully designed to match target resonant frequencies while simultaneously maximizing the sensor response to the three Cartesian components of the external angular rate. The structural size optimization of triaxial gyroscopes was first addressed in ,28, wherfeMEMS) that allows the layout to be optimized directly at the physical level. The layout of the MEMS device was parametrized and automatically generated, while the physical-level simulations of its dynamic behavior were performed using 3D finite elements, and the simulation tool was coupled with a gradient based optimization algorithm to iteratively update the design based on user-defined requirements. In addition, computational costs were reduced through the use of substructuring techniques for speeding up simulations and adjoint methods for analytical sensitivity computations. However, this approach requires the design engineer to perform analytical derivations for sensitivity analysis whenever new objective functions or constraints are considered in the optimization problem and to introduce the related low-level code modifications in the optimization tool.In a recent publication , the autfeMEMSlite, an automatic design tool for rapid prototyping, can be used to optimize MEMS layouts in a flexible and computationally efficient way. The proposed approach employs a computationally cheap, device-level schematization of the MEMS structure: proof masses are treated as rigid bodies, springs are discretized by Timoshenko beam finite elements , and else as in  will allfeMEMSlite is described in The paper is organized as follows. A general overview of feMEMSlite is provided in feMEMSlite is similar to feMEMS presented in [An overview of ented in , and theThe software requires as input a parametrization of the MEMS geometry by the design engineer, with design variables that are typically related to the position and dimensions of the composing mechanical elements, such as proof masses and springs. The structural layout is then automatically generated, and the linear dynamic behavior of the MEMS structure is simulated through a device-level schematization. This treats the proof masses as rigid bodies, discretizes the springs by Timoshenko beam finite elements, and accounts for electrostatic softening effects due to electromechanical interactions with additional negative spring constants cf. . The autfeMEMS and feMEMSlite, in the following we will employ the same reference design case and the same optimization objectives as in [In order to allow a straightforward comparison between es as in : both thThe details of the optimization steps are described considering the reference design case of a triaxial ”beating heart” MEMS gyroscope, shown in Triaxial beating heart MEMS gyroscopes, introduced by STMicroelectronics ,32, are Two sets of comb finger electrodes  drive: b. Two adWhen the driven device is subjected to an external angular rate, sinusoidal Coriolis forces arise and excite the “sense” modes of the structure. Each sense mode is excited by a specific Cartesian component of the external angular rate: the yaw mode e is forcll modes c,d are fectrodes  in a difIn order to obtain a suitable sensor performance, a careful choice of the structural natural frequencies has to be made. The value of the drive natural frequency Hz range . In ordeThe complexity of inertial MEMS structures such as beating heart MEMS gyroscopes, along with the variety of requirements to be satisfied, motivate the benefits of developing automatic design tools based on structural optimization. In order to do so, the MEMS structure layout is parametrized by a set of design variables, which are then optimized to maximize the performance of the device. Similarly to , and refw, length L, and distance between parallel beams d). In particular, springs of similar type  are certainly possible and are identified as future work.As described in Once the geometry of the structure has been generated, the MEMS behavior is simulated using the schematization shown in Each Timoshenko beam finite element  is associ-th rigid mass are associated with the degrees of freedom of its center of mass i-th mass, the mass matrix i-th rigid mass and The proof masses, modeled as rigid bodies, are instead only associated with mass properties; i.e., possible effects related to their flexibility are neglected. All the properties of the The matrices related to rigid masses and Timoshenko beams are computed considering polysilicon material properties, i.e., a mass density A typical detection strategy for sense displacements in inertial MEMS devices is to use variable-gap parallel plate electrodes ,2. Due tg is the initial gap between the two plates and In order to detect the out-of-plane motion of proof masses , rectangular detection electrodes are usually placed beneath the proof masses, resulting in out-of-plane variable-gap capacitors as shown in The total out-of-plane capacitance as a function of Equation  over thex direction, and differential sensing is obtained through two sets of capacitors that are oppositely influenced by proof mass displacements, namely The in-plane motion of the proof masses is detected by a series of differential in-plane electrodes as shown in y as:t is the out-of-plane thickness of the MEMS structure. The total in-plane capacitance can be then computed by integrating Equations (−ωh(−ωh(14) is solvEquation ) are solThe formulation of the optimization problem translates into mathematical terms the design objectives: usually the aim is to maximize the sensor performance or response while satisfying a set of constraints, e.g., related to the modal behavior of the structure. For what concerns the reference gyroscope design case, two possible formulations problem are presented, similar to the ones treated in .The first optimization problem (P1) aims to maximize the response to the external angular rates while matching given target natural frequencies and their desired ordering:The second optimization problem (P2) is a simplification of (P1) and is mainly focused on the tuning of the structural eigenfrequencies. In particular, we aim at maximizing the distance of the spurious modes from the operating frequency range while matching the target drive and sense natural frequencies:In (P1), we maximize the minimum response to the considered angular rates along the three Cartesian axes, while in (P2), we maximize the distance between the first spurious and the drive natural frequency, i.e., Equation ) is in bvided in , as donevided in . NormaliThe gradient based Method of Moving Asymptotes (MMA)  is used Adjoint sensitivity analysis is usually employed for computationally expensive simulations, such as physical-level ones . The maFor the reasons highlighted above, we propose a finite differences method, which simply involves perturbing each design variable and performing an additional simulation each time. While this obviously requires an additional computational effort , device-level simulations are fairly cheap, such that the overall computational cost is contained. This allows us to obtain an extremely flexible tool that only requires minimal human intervention and effort whenever new problem formulations are introduced.feMEMSlite are discussed. First, the proposed device-level schematization is validated with a comparison with the physical-level schematization from feMEMS and Abaqus commercial software (taken as reference), focusing on accuracy and computational costs. Second, the layouts optimized with feMEMSlite are presented, following the two formulations of the problem described in In this Section, the simulation and optimization results obtained with feMEMSlite device-level schematization are discussed. This is done by comparing with the physical-level simulations from feMEMS and Abaqus, whose results reported in feMEMS employs quadratic hexaedral elements to mesh the springs and quadratic triangular shell elements to mesh the proof masses, while in Abaqus, the structure is fully discretized by quadratic hexedral elements (HEX20). All the results refer to the example gyroscope layout shown in In the following, the accuracy and computational cost of feMEMSlite guarantees an extremely low computational cost. For what concerns the time taken by the simulation, i.e., the time needed to assemble the matrices of the system and to perform the modal and the harmonic response analyses, we achieve an improvement by three orders of magnitude with respect to Abaqus and one of two orders of magnitude lower with respect to feMEMS. A further considerable time reduction with respect to usual commercial FEM softwares is given in feMEMSlite and feMEMS by the automatic generation of the geometric layout: this improvement is less easy to explicitly quantify and results are user-dependent, but we estimate that it can be in the order of some of minutes.Referring to feMEMSlite is associated with a certain degree of approximation with respect to Abaqus, which is increased with respect to feMEMS. In particular, for purely mechanical simulations, 2–5% of errors are related to the eigenfrequencies and the harmonic response for in-plane motion, while errors around 11–13% are associated with out-of-plane motion. Furthermore, the last column in feMEMSlite in allowing the fast design of MEMS devices, provided that the increased degree of approximation does not affect the effectiveness of the optimized layouts. In the following, we show some examples of rapidly prototyped gyroscope layouts at a device level, and we assess their performance when moving to physical-level design.The analysis of computational costs and accuracy already underlines the potentialities of The optimized layouts obtained by solving problems (P1) and (P2) are shown in In feMEMSlite perform when considering the physical-level design. This is done by comparing the simulation results for layouts (b) and (c) between feMEMSlite and Abaqus HEX20. The results are reported in The final aim of the discussion is to assess how the layouts optimized at device level with feMEMS [The layouts optimized at the device level perform well even when considering physical-level schematizations. In fact, the drive frequency is also close to the 20 kHz target in Abaqus simulations, and the different natural frequencies maintain the desired mode ordering, with a suitable distance between the operating range and spurious modes. Moreover, the drive mode shape parameter h feMEMS ).feMEMSlite —that employs a computationally cheap schematization at the device level. It allows a rapid device prototyping and represents a valid alternative to more sophisticated physical-level design tools.In this paper, we have presented an automatic design tool for inertial MEMS devices—feMEMSlite has been inherited from its physical-level counterpart feMEMS: the proposed tool allows the parametric and automatic generation of the MEMS geometry, the simulation of the device dynamics, and the optimization of the layout. In order to demonstrate the effectiveness of the proposed tool, we have presented the design example of triaxial beating heart MEMS gyroscopes, in which 43 design variables have been employed to describe the dimensions and position of its internal mechanical elements.The general workflow of feMEMSlite, the schematization of the MEMS structure is performed by treating the proof masses as rigid bodies, discretizing the flexural springs by Timoshenko beam finite elements, and considering additional negative spring constants to account for the electrostatic softening effects. The derivations of such spring constants, related to both translational and rotational degrees of freedom of the proof masses, are also presented.In The reference layout has been optimized following two possible industrially-relevant problems: achieving a sensor with the maximum response to the external angular rate and maximizing the distance of spurious natural frequencies from the operating frequency range. Appropriate constraints have been included to satisfy technological and performance requirements and to match the target eigenfrequencies of the structure. The formulated optimization problems have been solved using the gradient-based Method of Moving Asymptotes (MMA), and the needed sensitivity analysis has been performed through a finite differences scheme in order to maximize the tool flexibility.feMEMS and Abaqus. The efficient schematization of feMEMSlite reduces the simulation time by three orders of magnitude vs. Abaqus and by two orders of magnitude vs. feMEMS, with a general slight overestimation of the structural eigenfrequencies (around 5–10%). The layouts optimized at the device level therefore show good performances also when simulated at physical level: a correct mode ordering is maintained, while preserving adequate responses to the external angular rates and proper distance of spurious modes from the operational frequency range. Only minor adjustments are therefore required to fine-tune the design.Finally, the rapidly prototyped layouts at device level have been validated by comparing the results with those obtained running full 3D simulations in While the current version of the tool requires as manual input the list of parametrized nodal points for the springs and polygons for the masses, future versions can introduce libraries of pre-parametrized standard mechanical elements. In addition, the employed schematization can be extended to capture mechanical nonlinearities and multiphysical effects, such as nonlinear electrostatics, thermoelasticity and fluid–structure interactions.


Sinus of Valsalva aneurysm (SVA) is a rare cardiac anomaly that can undergo spontaneous rupture into other cardiac chambers or the pericardial space. A ruptured SVA has a very poor prognosis with high morbidity and mortality. These aneurysms often present as a rupture from the right coronary sinus into the right ventricle. Transcatheter closure has become an effective alternative to surgical treatment. However, it has been rarely reported in patients after ventricular defect repair in the past. We here describe a 3-year-and-3-month-old boy who was found to have a ruptured sinus of Valsalva. He underwent surgical closure of a ventricular septal defect at the age of 2 months, which occurred in the non-coronary sinus (NCS) and ruptured into the right atrium. We successfully occluded the ruptured sinus of Valsalva with a ventricular septal occluder. Sinus of Valsalva (SOA) rupture is a rare cardiovascular disease, and it occurs in the presence of sinus of Valsalva aneurysm (SVA) . SVA is A 3-year-old boy underwent surgical repair of ventricular septal defect (VSD) at the age of 3 months. A large perimembranous ventricular septal defect with a size of about 10 mm was detected in the child after birth, so we performed a ventricular defect repair when the child was 2 months old. Two months after surgery, transthoracic echocardiography (TTE) revealed the shunt between the aortic sinus and the right atrium without significant aortic regurgitation, showing that the diameter of the break was about 1.5 mm. Therefore, we decided to have the child undergo regular follow-up transthoracic echocardiography and observe the change in the size of the break, instead of immediate surgical intervention. TTE showed that the diameter of the break of the SOA rupture was 3 mm, accompanied by a mild-to-moderate aortic regurgitation during regular follow-ups in the hospital at 3 years after ventricular defect repair . Upon exA sinus of Valsalva aneurysm (SVA) is typically the result of a lack of fusion between the tunica media and the annulus fibrosus of the aortic valve . HoweverSurgery has been the main treatment for SOV rupture since 1956. The indications for surgical repair include the location of SOV and whether a rupture takes place. However, due to the implementation of sternotomy and cardiopulmonary bypass, the associated morbidity is high. Currently, there is a percutaneous closure technique, which has been proved to be a simple method with a low incidence of complications . In 1994In the past, few children underwent SOV surgery. The youngest one on record was 18 months old. Surgery or occlusion operation on SOV in children can prevent the formation of aortic sinus aneurysm caused by long-term pathological changes and further problems such as heart enlargement and reduction of cardiac function. Amplatzer Duct Occluder IIs (ADOIIs) are the primary tool for occlusion in children. However, ADOII is not currently available in China due to trade wars and other factors. By careful comparison, we concluded that the ventricular septal defect occluder is the most suitable alternative. The ventricular septal defect occluder is an improvement over the Amplatzer ventricular septal defect occluder, and the most important improvement is that the left umbrella disk is slightly larger than the right umbrella disk. The left umbrella disk is slightly larger than the right umbrella disk. Due to this design, the smaller right umbrella disc is located in the aortic sinus, reducing the impact on the aortic valve. This allows for the complete closure of larger defects while minimizing the impact on the aorta. This occluder can be used for any type of SOV or SVA in both adults and children .In the case reported, the site of SOV rupture entered the right atrium from the non-coronary sinus of Valsalva. We used a double disc perimembranous ventricular septal defect occluder to repair SOV rupture with a retrograde method. The size of the selected device was larger than that of the aortic end of the defect: 2 mm. Because the rupture flows into the right atrium, the VSD occluder can provide stability and does not run the risk of atrial hemodynamic changes. Retrograde closure and rupture of SOV may be safer and faster than the antegrade method .The percutaneous closure with the ventricular septal defect occluder of ruptured SOV can be successfully performed. Percutaneous occlusion with a suitable device could be a reliable and safe alternative to surgery in small children.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author/s.Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.TZ and SH were responsible for the diagnosis and treatment of the patient. XG and SH prepared the manuscript. JW and LW collected clinical data. All authors have read and approved the final manuscript, have agreed to be accountable for the content of the work, and contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.