Sampling Frequency Research Articles

The Self-Assessment, Perturbation, and Resonance Values of Voice and Speech in Individuals with Snoring and Obstructive Sleep Apnea

SummaryNA PurposeThe static and dynamic soft tissue changes resulting in hypopnea and/or apnea in the subjects with obstructive sleep apnea (OSA) occur in the upper airway, which also serves as the voice or speech tract. In this study, we looked for the Voice Handicap Index-10 (VHI-10) and Voice-Related Quality of Life (V-RQOL) scores in addition to perturbation and formant values of the vowels in those with snoring and OSA. MethodsEpworth Sleepiness Scale (ESS), STOP-Bang scores, Body-Mass Index (BMI), neck circumference (NC), modified Mallampati Index (MI), tonsil size, Apnea-Hypopnea Index (AHI), VHI-10 and V-RQOL scores, perturbation and formant values, and fundamental frequency of the voice samples were taken to evaluate. ResultsThe data revealed that not the perturbation and formant values but scores of VHI-10 and V-RQOL were significantly different between the control and OSA subjects and that both were significantly correlated with ESS and NC. Further, a few significant correlations of BMI, and tonsil size with the formant and perturbation values were also found. ConclusionsOur data reveals that i) VHI-10 and V-RQOL were good identifiers for those with OSA, and ii) perturbation and formant values were related to particularly tonsil size, and further BMI. Hence, we could say that in an attempt to use a voice parameter to screen OSA, VHI-10, and V-RQOL appeared to be better than the objective voice measures, which could be variable due to the tonsil size and BMI of the subjects.

Sampled‐Data Based Second‐Order Discrete‐Time Sliding Mode Control via Output Feedback

ABSTRACTThis article proposes a second‐order discrete‐time sliding mode controller (DSMC) by only using sampled‐data information of the system output. On one hand, different from the continuous‐time sliding mode control, the finite‐time stability of the closed‐loop system under DSMC is usually not kept due to the limited sampling frequency. On the other hand, in the output feedback framework, the second‐order sliding mode observer cannot independently estimate the internal state of the system if there exist unknown mismatched term and unknown time‐varying coefficient of the control input. To solve the above problems, a joint design strategy of second‐order discrete‐time sliding mode observer and sliding mode controller based on the “non‐separation principle” is proposed. The stability and robustness of the closed‐loop system is analyzed based on the Lyapunov design method. Finally, simulation example verifies the correctness of the theory.

Measuring daily tourism mobility spillover at the intra-metropolis level with mobile positioning data

ABSTRACT Although tourism mobility spillover continues to be a key indicator for tourism management, more innovative research must be conducted at the micro level and high sampling frequency. Against the backdrop of an increasing number of global cities, in this paper, we evaluate the daily tourism mobility spillover inside a worldwide city of China: Shanghai. Based on the Granger causal network model and an original mobile positioning dataset, we analyse the causal relationship between local tourism flows and the spillover effects of tourism mobility within Shanghai. By categorising tourists into ‘local tourists from Shanghai’ and ‘tourists from out of Shanghai’, we reveal a significant causal relationship between Shanghai districts and flows generated by ‘tourists from out of Shanghai’. The analysis of the causal network structure also reveals key districts and points of interest that significantly contribute to congestion in tourism mobility and Shanghai's dynamics. This econometric approach offers policymakers a valuable tool to monitor mobility drivers and optimise flows within the city.

Typical Scenario Load Identification Based on Feature Fusion and Transfer Learning

Background: The electricity demand is continuously increasing. However, various institutions, enterprises, and individuals exhibit many irregularities in their electricity usage, leading to significant wastage of electricity. To achieve effective energy management, researchers are attempting to analyze and regulate users' electricity demands by monitoring their load usage through Non- Intrusive Load Monitoring (NILM) technology. The accuracy of load identification in this technology will greatly impact the results of load monitoring. Although there are currently many articles and patents related to NILM, they utilize a large amount of computational resources and require high sampling rates from devices, yet the results are still unsatisfactory. Therefore, it is necessary to improve the accuracy of load identification in data with relatively low sampling frequencies. Objective: To improve the accuracy of load identification with low sampling frequency data, this paper proposes a typical scenario load identification method based on feature fusion and transfer learning. Methods: This method adopts the fusion of current and power factor angles to provide abundant identification information for NILM, effectively reducing the situation of single-feature overlap of different loads. By inputting the fused feature data into GoogLeNet and utilizing transfer learning for training, not only is the accuracy improved, but also the training time and the requirement for the sampling rate of training data are greatly reduced. In addition, selecting typical scenario loads can monitor loads in a targeted manner, reduce the waste of computing resources caused by irrelevant loads, and more effectively guide electricity usage strategies. Results: The proposed load identification method was tested on the low sampling frequency dataset used in this paper. It achieved an overall load identification accuracy of 94.61% across three scenarios, improving accuracy by 3% to 7% compared to other models. Conclusion: The simulation results indicate that this method achieves high load identification accuracy at low sampling frequencies. It also exhibits good generalization ability. This method not only reduces the performance requirements for monitoring equipment but also enhances monitoring efficiency.

Light Output Function and Pulse-Shape Discrimination Capability of p-Terphenyl Organic Scintillator in Wide Neutron Energy Range of 1.1 to 19 MeV

In this work, we studied the light-output properties, efficiency function, as well as the pulse-shape discrimination (PSD) capability of p-Terphenyl scintillator. The selected solid cylindrical scintillation detector has a thickness of 45 mm and a diameter of 45 mm. Recently presented studies of light-output functions have only been measured for low-neutron energies. Our motivation has been to determine the light output function for p-Terphenyl scintillator more accurately over a wider neutron energy range. The measurements have been carried out with mono-energetic neutron beams in the wide energy range from 1.1 to 19 MeV. The neutron–gamma spectrometric system which we developed has been used for the measurement. The input analog signal from the detector was digitized with a fast 12-bits analog to digital converter with a sampling frequency of 1 GHz. Measured data from the detector are processed into the gamma and neutron spectra. The accurate light output function for the p-Therphenyl scintillator has been calculated. The pulse-shape discrimination capability, as well as the detection efficiency, of a p-Terphenyl scintillator are lower in comparison with a NE-213 equivalent detector.

Near-surface particulate backscattering observations with bio-optical Lagrangian drifters

Abstract Particulate matter concentration is an essential ocean variable (EOV) useful for understanding biogeochemical processes, improving ocean productivity estimates, and constraining coupled physical and biogeochemical numerical models. While direct observations of this parameter are difficult to obtain, the optical backscattering coefficient of marine particles (bbp) can be used as a reliable proxy. However, most in-situ multi-spectral bbp measurements are ship-borne or obtained from moored systems, thus with a limited spatial and temporal coverage. Utilizing Surface Velocity Programme (SVP) drifting buoys with self-contained backscatter sensors designed for extended deployment periods offers a promising solution for collecting data in challenging marine environments. In this study, we presented for the first time the integration of a commercially available, high-frequency and multispectral optical backscatter sensor into an SVP drifter, the so-called BO-SVP drifter designed to collect bbp measurements near the ocean surface. bbp measurements obtained by the BO-SVP drifter were reliable over a wide range of environmental conditions, showing a good agreement with independent datasets (relative bias < 10%; relative standard deviation < 36%). The BO-SVP drifter captures the satellite sub- and pixel scale variability by combining the Lagrangian approach and a high frequency sampling. This configuration enables the acquisition of measurements across numerous pixels in a single day, enhancing validation activities for ongoing and future satellite products and the quantification of associated uncertainties. The measurements acquired by these platforms can offer valuable insights into particle distribution at fine spatial scales, daily variability, and its relationship with water masses type and ocean dynamics.

Load spectra of a light unmanned aircraft – data recording frequencies and resulting measurement variations

The article focuses on the load spectrum of a lightweight unmanned aircraft. It examines the impact of load signal sampling frequency on the load spectrum derived from the load signal. The flight sessions followed two scenarios: a maneuvering flight causing a wide range of load factor variations and a calm photogrammetric flight. The recorded load signals from two types of sensors were initially rescaled into load factor time series, with an optional signal smoothing process. These series were then downsampled by selecting every nth term, resulting in load factor sequences recorded at a lower frequency. Next, all sequences were transformed into sequences of local extremes of the Load Levels, resulting from quantifying the operational load variability into 32 intervals. Two options were considered: without filtering or with filtering of sequential pairs of terms generating small Load Level increments. Subsequently, the Rainflow Counting algorithm was applied, producing 32x32 load half-cycle arrays. Based on these arrays, incremental load spectra were calculated. The study provides a detailed comparison of load spectra for different load signal sources and recording frequencies. Additionally, calculations of the fatigue life of a structural element subjected to various load spectra were performed, leading to the formulated conclusions.

Lactose Breath Test: Possible Strategies to Optimize Test Performance, Accuracy, and Clinical Impact.

Lactose malabsorption (LM) refers to the incomplete absorption of lactose in the small intestine, resulting in the arrival of ingested lactose in the colon, which can give rise to symptoms defined as lactose intolerance (LI). The lactose breath test (LBT), thanks to its low cost, availability, and noninvasiveness, is the most used diagnostic method. However, the LBT is a tedious tool, requiring prolonged involvement of patients, qualified staff, and infrastructure, of which the most time-consuming factor is the frequency and number of breath samples needed. Objectives: To simplify the current LBT methodology, compliant with the current guidelines' statements, by reducing the test duration or the number of breath samples, without compromising the test's accuracy. Methods: The results of the standard LBT were compared with two simplified tests: a "shortened" test, lasting three hours, with samples taken every 30 min; and a "five-sample" test, lasting four hours, with samples taken every hour. Patients were stratified into three grades of malabsorption (mild, moderate, severe) based on the amount of gas exhaled. A clinical severity score was introduced to assess the clinical relevance of LI using a specific questionnaire. Results: Among the 543 patients enrolled (F 71.5%, mean age 43.7 ± 17.6 yrs), 60.4% (328/543) tested positive for LM. A total of 70.5% (383/543) presented LI, with 32.1% of those being true intolerants (LI without LM). The shortened test demonstrated an accuracy of 93.9%, with a sensitivity of 89.9% and a false negative rate of 10.1% (33/328). The five-sample test showed higher accuracy and sensitivity than the shortened test (96.5% and 94.2%, respectively; p = 0.03) with a false negative rate of 5.8% (19/328). Of the 19 false negatives in the five-sample test, 95% (18/19) were categorized as mild malabsorbents. No statistical correlation was found between the clinical severity score and LBT results. Conclusions: The five-sample test, involving hourly breath measurements, is a reliable option for simplifying the LBT without significantly reducing the procedure's sensitivity.

Co-occurrence and correlation of arsenic, cadmium, and lead in processed foods intended for infants and young children: evaluation of two statistical approaches adapted to censored data and their implications for risk management

We evaluated the co-occurrence of arsenic (As), cadmium (Cd), and lead (Pb) in 10 categories of processed foods intended for infants and young children (<2 years old) and adapted methodology to characterise and evaluate the impact of correlations on toxic element (TE) concentrations in these foods. Co-occurrence was assessed by calculating the frequency of samples having concentrations above the limit of detection (LOD) among TE sets (i.e. As-Cd, As-Pb, Cd-Pb, and As-Cd-Pb). Pairwise correlations were evaluated using two statistical approaches adapted to censored data: (i) non-parametric Kendall’s tau and (ii) parametric Bayesian modelling. Nonparametric and parametric correlation analyses showed similar results. We found positive correlations among one or more pairs of the TEs (As, Pb, and Cd) in 9 of the 10 categories of foods intended for infants and young children and a negative correlation for one TE pair for a single food category. Where positive correlations are observed, removing samples with high concentrations of a given TE reduces the mean of the other TEs in that food. We also explored the impact of correlations among TEs on the fraction of the supply below maximum levels. Positive correlations among TEs have the potential to reduce the impact on the supply when setting multiple maximum levels.

First top-down diurnal adjustment to NOx emissions inventory in Asia informed by the Geostationary Environment Monitoring Spectrometer (GEMS) tropospheric NO2 columns

Pioneering the use of the Geostationary Environment Monitoring Spectrometer’s (GEMS) observation data in air quality modeling, we adjusted Asia’s NOx emissions inventory by leveraging the instrument’s unprecedented sampling frequency. GEMS tropospheric NO2 columns served as top-down constraints, guiding our Bayesian inversion to constrain NOx emissions in Asia during spring 2022. This enabled the model to better capture the diurnal variation in NOx emissions, such as its morning rush hour peak, particularly when more retrievals were available each day, improving the simulation accuracy to a certain extent. The GEMS-informed adjustment reduced the extent of model underestimation of surface NO2 concentrations from 17.38 to 5.58% in Korea and from 13.05 to 4.54% in China, showing about 9.40% and 5.77% greater improvements, respectively, compared to the adjustment based on the sun-synchronous low earth orbit observation proxy. Our findings highlight the potential of geostationary observation data in refining the diurnal cycle of inventoried NOx emissions, thereby more effectively improving the accuracy of air quality simulations.

Autoantibodies towards HFE and SYT5 in anti-neutrophil cytoplasm antibody-associated vasculitis relapse.

Identification of those at high and low risk of disease relapse is a major unmet need in the management of patients with ANCA-associated vasculitis (AAV). Precise stratification would allow tailoring of immunosuppressive medication. We profiled the autoantibody repertoire of AAV patients in remission to identify novel autoantibodies associated with relapse risk. Plasma samples collected from 246 AAV patients in remission were screened for novel autoantibodies using in-house generated protein arrays including 42 000 protein fragments representing 18 000 unique human proteins. Patients were categorized based on the occurrence and frequency of relapses. We modelled the association between these antibodies and relapse occurrence using descriptive and high dimensional regression approaches. We observed nine autoantibodies at higher frequency in samples from AAV patients experiencing multiple relapses compared with patients in long-term remission off therapy (LTROT). LASSO analysis identified six autoantibodies that exhibited an association with relapse occurrence after sample collection. Antibodies targeting HFE and SYT5 were identified as associated with relapse in both analyses. Through a broad protein array-based autoantibody screening, we identified two novel autoantibodies directed against HFE and SYT5 as candidate biomarkers of relapse in AAV.

Distribution and seasonality of viral respiratory infections in pediatric oncologic patients in a tropical country

Abstract Background Comprehension of the distribution and seasonality viral respiratory infections pediatric oncologic patients in tropical localities remains poor. Due to lack mandatory report of all viruses and limitations in access to molecular biology techniques there is currently few data published on this topic. We report the distribution and seasonality of pediatric viral respiratory infections at a tertiary care oncological hospital in Guayaquil, Ecuador. Methods The laboratory database with results of real time PCR panel (Biofire® respiratory panel 2.1) from January to December 2021 was reviewed to collect the relative frequency and seasonality of identifiable viral infections in pediatric (ages &amp;lt; 18 years) patients. The frequency of infection according to sample characteristics including sex and age was compared using chi square test with a significance of 0.05. The frequency of detection was organized per month within the year. Percentage of coinfections was also determined. Results Out of 217 included PCR results, 143 (65.8%) tested positive for detectable viral organisms. Mean age was 7.3 years SD 4.8 years. The infection frequency for girl samples was higher than that of boy samples, X2 (1, N=217) = 4.16, p 0.041. The relation between age groups and the frequency of infection was not statistically significant X2 (2, N=217) = 5.24, p 0.073. The most prevalent viruses included Rhinovirus/Enterovirus 53 cases (relative frequency 37.0%), Parainfluenza virus 3 42 cases (relative frequency 29.3%) and Respiratory syncytial virus 19 cases (relative frequency 13.2%). Rhinovirus/Enterovirus, Paramyxovirus and Adenovirus respiratory infections occurred throughout the year while other respiratory viruses including Coronaviruses showed a higher detection rate during the rainy season (December – April). A total of 13 (5.9%) patients were coinfected with two up to four viruses. The most common combination was Parainfluenza virus 3 with Rhinovirus/Enterovirus. No bacterial targets were detected. Conclusion Larger and more complex studies are necessary to understand the burden of viral respiratory infections in pediatric oncological patients. In Ecuador, detection was higher in girls and school aged children. Rhinovirus/Enterovirus, Paramyxovirus and Adenovirus were present all year long while seasonality was common for other viruses following dry/rainy season pattern in the region. More understanding of seasonality will aim for preventive measures during more difficult months and to distribute resources accordingly. Figure 1. Seasonality of respiratory viral infections.

Evaluation of wind-induced response of high-rise structure with TMD system by real-time hybrid simulation test with motor-driven shaking table

Due to the difficulty in constructing the small-scaled model of tuned mass damper (TMD) system to meet the requirement of dynamic similarity law, it is hard to evaluate the vibration control effect of passive TMD system on the wind-induced response of high-rise structure by wind tunnel test. The Real-Time Hybrid Simulation (RTHS) test could provide a suitable solution to this problem by increasing the size of the scaled TMD model to overcome the limitation in dynamic similarity law. A RTHS test system with a linear electric motor-driven shaking table was developed by the interface with model interface toolkit in general real time system operating environment. Taking the Guangzhou New TV Tower with a passive TMD system as an example, the Finite Element (FE) model of Guangzhou New TV Tower was selected as the numerical substructure and TMD system was selected as the experimental substructure, the RTHS test was conducted at a sampling frequency of 250 Hz to estimate the wind-induced response of this high-rise structure with a TMD system in various dynamic wind loading cases. To reduce the time delay effect generated by the nonlinear interaction between motion control system and experimental substructure, the adaptive time series (ATS) compensator for time delay compensation was proposed in the RTHS test. The RTHS results were also compared with the numerical simulation results using the FE model of the investigated object. The RTHS test with additional ATS time delay compensator can obtain more accurate experimental results. The effectiveness of the proposed RTHS test system was testified to be powerful test system to evaluate the wind-induced response of high-rise structures with complex passive TMD system.

Electromyographic activity of shoulder muscles on two preventive exercise programmes for swimmer’s shoulder: elastic band versus weight

ABSTRACT Strength programmes have shown good results in preventing swimmer’s shoulder. However, there aren’t studies based on electromyographic (EMG) analysis in these programmes. This study aims to compare an EMG activity of the middle trapezius (MT), lower trapezius (LT), infraspinatus (IS), serratus anterior (SA), and pectoralis major (PM) in two swimmer’s shoulder preventive programmes – one performed with elastic band and the other with weights. Twenty competitive swimmers performed two strength programmes consisting of five exercises each, while the analysis of EMG activity in MT, LT, IS, SA, and PM for each exercise was recorded. The superficial EMG was used to collect data at a sampling frequency of 1000 hz. The Paired Sample T-test or the Wilcoxon test was applied to compare EMG activity between programmes. The internal rotation at 90º (p < 0.001) and external rotation at 90º (p ≤ 0.01) exercises produced high myoelectric shoulder muscle activity with an elastic band. Conversely, scapular punches (p < 0.001) exercise has high shoulder EMG activity when performed with weights. Performing the same preventive exercise programme with two different instruments produces great variability in the myoelectric activity of the shoulder muscles.

Satellite-Based Detection of Algal Blooms in Large Alpine Lake Sevan: Can Satellite Data Overcome the Unavoidable Limitations in Field Observations?

Lake Sevan in Armenia is a unique, large, alpine lake given its surface, volume, and geographic location. The lake suffered from progressing eutrophication and, since 2018, massive cyanobacterial blooms repeatedly occurred. Although the lake is comparatively intensely monitored, the feasibility to reliably detect the algal bloom events appeared to be limited by the established in situ monitoring, mostly because algal bloom dynamics are far more dynamic than the realized monitoring frequency of monthly samplings. This mismatch of monitoring frequency and ecosystem dynamics is a notorious problem in lakes, where plankton dynamics often work at relatively short time scales. Satellite-based monitoring with higher overpass frequency, e.g., by Sentinel-3 OLCI with its daily overcasts, are expected to fill this gap. The goal of our study was therefore the establishment of a fast detection of algal blooms in Lake Sevan that operates at the time scale of days instead of months. We found that algal bloom detection in Lake Sevan failed, however, when it was only based on chlorophyll due to complications with optical water properties and atmospheric corrections. Instead, we obtained good results when true-color RGB images were analyzed or a specifically designed satellite-based HAB indicator was applied. These methods provide reliable and very fast bloom detection at a scale of days. At the same time, our results indicated that there are still considerable limitations for the use of remote sensing when it comes to a fully quantitative assessment of algal dynamics in Lake Sevan. The observations made so far indicate that algal blooms are a regular feature in Lake Sevan and occur almost always when water temperatures surpass approximately 20 °C. Our satellite-based method effectively allowed for bloom detection at short time scales and identified blooms over several years where classical sampling failed to do so, simply because of the unfortunate timing of sampling dates and blooming phases. The extension of classical in situ sampling by satellite-based methods is therefore a step towards a more reliable, faster, and more cost-effective detection of algal blooms in this valuable lake.

Ex vivo propagation of synaptically-evoked cortical depolarizations in a mouse model of Alzheimer’s disease at 20 Hz, 40 Hz, or 83 Hz

Sensory stimulations at 40 Hz gamma (but not any other frequency), have shown promise in reversing Alzheimer’s disease (AD)-related pathologies. What distinguishes 40 Hz? We hypothesized that stimuli at 40 Hz might summate more efficiently (temporal summation) or propagate more efficiently between cortical layers (vertically), or along cortical laminas (horizontally), compared to inputs at 20 or 83 Hz. To investigate these hypotheses, we used brain slices from AD mouse model animals (5xFAD). Extracellular (synaptic) stimuli were delivered in cortical layer 4 (L4). Leveraging a fluorescent voltage indicator (VSFP) expressed in cortical pyramidal neurons, we simultaneously monitored evoked cortical depolarizations at multiple sites, at 1 kHz sampling frequency. Experimental groups (AD-Female, CTRL-Female, AD-Male, and CTRL-Male) were tested at three stimulation frequencies (20, 40, and 83 Hz). Despite our initial hypothesis, two parameters—temporal summation of voltage waveforms and the strength of propagation through the cortical neuropil—did not reveal any distinct advantage of 40 Hz stimulation. Significant physiological differences between AD and Control mice were found at all stimulation frequencies tested, while the 40 Hz stimulation frequency was not remarkable.

Research of the efficiency of Kravchenko weighting functions and combinations based on them in the problem of narrow band interference rejection

The influence of weight functions (WFs) of preliminary weighting on the quality of narrowband interference rejection based on direct and inverse discrete Fourier transforms has been studied. Classical and modern WFs are considered: Kravchenko, Kravchenko–Dolph–Chebyshev, Kravchenko–Gauss, Kravchenko–Bernstein–Rogozinsky. Quantitative estimates of efficiency were obtained – the coefficients of interference suppression and signal transmission, as well as their product – the total efficiency coefficient. Graphic examples of the behavior of the total efficiency coefficient depending on the interference frequency are shown. Estimates of the indicated quality indicators are presented when a fixed number of frequency samples are removed from the spectrum. The families of dependences of the probabilities of correctly performing a search for a spread spectrum signal on the “interference/signal” ratio when using modern WFs to weigh the implementations of an additive mixture of signal, interference and noise are presented. The significant advantage of modern WFs formed through combinations with Kravchenko functions in the problem of interference rejection has been demonstrated and confirmed.

Evaluating survey techniques in wastewater-based epidemiology for accurate COVID-19 incidence estimation

Wastewater-based epidemiology (WBE) requires high-quality survey methods to determine the incidence of infections in wastewater catchment areas. In this study, the wastewater survey methods necessary for comprehending the incidence of infection by WBE are clarified. This clarification is based on the correlation with the number of confirmed coronavirus disease 2019 (COVID-19) cases, considering factors such as handling non-detect data, calculation method for representative values, analytical sensitivity, analytical reproducibility, sampling frequency, and survey duration. Data collected from 15 samples per week for two and a half years using a highly accurate analysis method were regarded as gold standard data, and the correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater and confirmed COVID-19 cases was analyzed by Monte Carlo simulation under the hypothetical situation where the quality of the wastewater survey method was reduced. Regarding data handling, it was appropriate to replace non-detect data with estimates based on distribution, and to use geometric means to calculate representative values. For the analysis of SARS-CoV-2 RNA in samples, using a highly sensitive and reproducible method (non-detect rates of <40 %; ≤0.4 standard deviation) and surveying at least three samples, preferably five samples, per week were considered desirable. Furthermore, conducting the survey over a period of time that included at least 50 weeks was necessary. A WBE that meets these survey criteria is sufficient for the determination of the COVID-19 infection incidence in the catchment. Furthermore, WBE can offer additional insights into infection rates in the catchment, such as the estimated 48 % decrease in confirmed COVID-19 cases visiting a clinic following a COVID-19 legal reclassification in Japan.

Older Adult Fall Risk Prediction with Deep Learning and Timed Up and Go (TUG) Test Data.

Falls are a major health hazard for older adults; therefore, in the context of an aging population, predicting the risk of a patient suffering falls in the near future is of great impact for health care systems. Currently, the standard prospective fall risk assessment instrument relies on a set of clinical and functional mobility assessment tools, one of them being the Timed Up and Go (TUG) test. Recently, wearable inertial measurement units (IMUs) have been proposed to capture motion data that would allow for the building of estimates of fall risk. The hypothesis of this study is that the data gathered from IMU readings while the patient is performing the TUG test can be used to build a predictive model that would provide an estimate of the probability of suffering a fall in the near future, i.e., assessing prospective fall risk. This study applies deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) to build such predictive models based on features extracted from IMU data acquired during TUG test realizations. Data were obtained from a cohort of 106 older adults wearing wireless IMU sensors with sampling frequencies of 100 Hz while performing the TUG test. The dependent variable is a binary variable that is true if the patient suffered a fall in the six-month follow-up period. This variable was used as the output variable for the supervised training and validations of the deep learning architectures and competing machine learning approaches. A hold-out validation process using 75 subjects for training and 31 subjects for testing was repeated one hundred times to obtain robust estimations of model performances At each repetition, 5-fold cross-validation was carried out to select the best model over the training subset. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

Identification of the Payne effect in a viscoelastic material coupling Bayesian identification and digital twin

The Payne or Fletcher-Gent effect is a particular behavior occurring in viscoelastic materials containing fillers. It induces a nonlinear dependency of the viscoelastic storage modulus on the amplitude of the applied strain. This amplitude dependency must be taken into account in engineering applications as it does change the elastodynamic behavior of the overall structure integrating the material. A methodology is developed in the proposed work to identify this nonlinear effect. The approach first starts with the identification by Bayesian inference of the frequency and damping properties of a viscoelastic sample. The input data are obtained from a modified Oberst test, conducted for different displacement load amplitudes. A digital twin of the experimental set-up is then used to evaluate the stiffness behavior, and in particular the Young's modulus of the material, at the measured frequencies, and to deduce the strain level within the sample. This work shows that the Payne effect, which leads to the decrease with the amplitude of the Young's modulus, can thus be estimated in terms of mean and standard deviation by combining experiments and numerical simulations.