Relationship Between Body Composition and Death in Patients with COVID-19 Differs Based on the Presence of Gastrointestinal Symptoms.

BACKGROUND: Patients with SARS-CoV-2 who present with gastrointestinal symptoms have a milder clinical course than those who do not. Risk factors for severe COVID-19 disease include increased adiposity and sarcopenia. AIMS: To determine whether body composition risk factors are associated with worse outcomes among patients with gastrointestinal symptoms. METHODS: This was a retrospective study of hospitalized patients with COVID-19 who underwent abdominal CT scan for clinical indications. Abdominal body composition measures including skeletal muscle index (SMI), intramuscular adipose tissue index (IMATI), visceral adipose tissue index (VATI), subcutaneous adipose tissue index (SATI), visceral-to-subcutaneous adipose tissue ratio (VAT/SAT ratio), and liver and spleen attenuation were collected. The association between body composition measurements and 30-day mortality was evaluated in patients with and without gastrointestinal symptoms at the time of positive SARS-CoV-2 test. RESULTS: Abdominal CT scans of 190 patients with COVID-19 were evaluated. Gastrointestinal symptoms including nausea, vomiting, diarrhea, or abdominal pain were present in 117 (62%). Among patients without gastrointestinal symptoms, those who died had greater IMATI (p = 0.049), less SMI (p = 0.010), and a trend toward a greater VAT/SAT ratio. Among patients with gastrointestinal symptoms, those who died had significantly greater IMATI (p = 0.025) but no differences in other measures. CONCLUSIONS: Among patients with COVID-19, those without gastrointestinal symptoms showed the expected associations between mortality and low SMI, high IMATI, and trend toward higher VAT/SAT ratio, but those with gastrointestinal symptoms did not. Future studies should explore the mechanisms for the altered disease course in patients with COVID-19 who present with gastrointestinal symptoms.

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

INTRODUCTION: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. METHODS: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. RESULTS: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%.

Inertial Sensor Estimation of Initial and Terminal Contact during In-Field Running.

Given the popularity of running-based sports and the rapid development of Micro-electromechanical systems (MEMS), portable wireless sensors can provide in-field monitoring and analysis of running gait parameters during exercise. This paper proposed an intelligent analysis system from wireless micro-Inertial Measurement Unit (IMU) data to estimate contact time (CT) and flight time (FT) during running based on gyroscope and accelerometer sensors in a single location (ankle). Furthermore, a pre-processing system that detected the running period was introduced to analyse and enhance CT and FT detection accuracy and reduce noise. Results showed pre-processing successfully detected the designated running periods to remove noise of non-running periods. Furthermore, accelerometer and gyroscope algorithms showed good consistency within 95% confidence interval, and average absolute error of 31.53 ms and 24.77 ms, respectively. In turn, the combined system obtained a consistency of 84-100% agreement within tolerance values of 50 ms and 30 ms, respectively. Interestingly, both accuracy and consistency showed a decreasing trend as speed increased (36% at high-speed fore-foot strike). Successful CT and FT detection and output validation with consistency checking algorithms make in-field measurement of running gait possible using ankle-worn IMU sensors. Accordingly, accurate IMU-based gait analysis from gyroscope and accelerometer information can inform future research on in-field gait analysis.

Design of Electronic Nose Detection System for Apple Quality Grading Based on Computational Fluid Dynamics Simulation and K-Nearest Neighbor Support Vector Machine.

Apples are one of the most widely planted fruits in the world, with an extremely high annual production. Several issues should be addressed to avoid the damaging of samples during the quality grading process of apples (e.g., the long detection period and the inability to detect the internal quality of apples). In this study, an electronic nose (e-nose) detection system for apple quality grading based on the K-nearest neighbor support vector machine (KNN-SVM) was designed, and the nasal cavity structure of the e-nose was optimized by computational fluid dynamics (CFD) simulation. A KNN-SVM classifier was also proposed to overcome the shortcomings of the traditional SVMs. The performance of the developed device was experimentally verified in the following steps. The apples were divided into three groups according to their external and internal quality. The e-nose data were pre-processed before features extraction, and then Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimension of the datasets. The recognition accuracy of the PCA-KNN-SVM classifier was 96.45%, and the LDA-KNN-SVM classifier achieved 97.78%. Compared with other commonly used classifiers, (traditional KNN, SVM, Decision Tree, and Random Forest), KNN-SVM is more efficient in terms of training time and accuracy of classification. Generally, the apple grading system can be used to evaluate the quality of apples during storage.

WPO-Net: Windowed Pose Optimization Network for Monocular Visual Odometry Estimation.

Visual odometry is the process of estimating incremental localization of the camera in 3-dimensional space for autonomous driving. There have been new learning-based methods which do not require camera calibration and are robust to external noise. In this work, a new method that do not require camera calibration called the "windowed pose optimization network" is proposed to estimate the 6 degrees of freedom pose of a monocular camera. The architecture of the proposed network is based on supervised learning-based methods with feature encoder and pose regressor that takes multiple consecutive two grayscale image stacks at each step for training and enforces the composite pose constraints. The KITTI dataset is used to evaluate the performance of the proposed method. The proposed method yielded rotational error of 3.12 deg/100 m, and the training time is 41.32 ms, while inference time is 7.87 ms. Experiments demonstrate the competitive performance of the proposed method to other state-of-the-art related works which shows the novelty of the proposed technique.

A Hybrid Feature Selection and Extraction Methods for Sleep Apnea Detection Using Bio-Signals.

People with sleep apnea (SA) are at increased risk of having stroke and cardiovascular diseases. Polysomnography (PSG) is used to detect SA. This paper conducts feature selection from PSG signals and uses a support vector machine (SVM) to detect SA. To analyze SA, the Physionet Apnea Database was used to obtain various features. Electrocardiography (ECG), oxygen saturation (SaO2), airflow, abdominal, and thoracic signals were used to provide various frequency-, time-domain and non-linear features (n = 87). To analyse the significance of these features, firstly, two evaluation measures, the rank-sum method and the analysis of variance (ANOVA) were used to evaluate the significance of the features. These features were then classified according to their significance. Finally, different class feature sets were presented as inputs for an SVM classifier to detect the onset of SA. The hill-climbing feature selection algorithm and the k-fold cross-validation method were applied to evaluate each classification performance. Through the experiments, we discovered that the best feature set (including the top-five significant features) obtained the best classification performance. Furthermore, we plotted receiver operating characteristic (ROC) curves to examine the performance of the SVM, and the results showed the SVM with Linear kernel (regularization parameter = 1) outperformed other classifiers (area under curve = 95.23%, sensitivity = 94.29%, specificity = 96.17%). The results confirm that feature subsets based on multiple bio-signals have the potential to identify patients with SA. The use of a smaller subset avoids dimensionality problems and reduces the computational load.

Advancing the deer calcaneus model for bone adaptation studies: ex vivo strains obtained after transecting the tension members suggest an unrecognized important role for shear strains.

Sheep and deer calcanei are finding increased use as models for studies of bone adaptation, including advancing understanding of how the strain (deformation) environment influences the ontogenetic emergence of biomechanically relevant structural and material variations in cortical and trabecular bone. These artiodactyl calcanei seem ideal for these analyses because they function like simply loaded short-cantilevered beams with net compression and tension strains on the dorsal and plantar cortices, respectively. However, this habitual strain distribution requires more rigorous validation because it has been shown by limited in vivo and ex vivo strain measurements obtained during controlled ambulation (typically walking and trotting). The conception that these calcanei are relatively simply and habitually loaded 'tension/compression bones' could be invalid if infrequent, though biologically relevant, loads substantially change the location of the neutral axis (NA) that separates 'compression' and 'tension' regions. The effect on calcaneus strains of the tension members (plantar ligament and flexor tendon) is also not well understood and measuring strains after transecting them could reveal that they significantly modulate the strain distribution. We tested the hypothesis that the NA location previously described during simulated on-axis loads of deer calcanei would exhibit limited variations even when load perturbations are unusual (e.g. off-axis loads) or extreme (e.g. after transection of the tension members). We also examined regional differences in the predominance of the three strain modes (tension, compression, and shear) in these various load conditions in dorsal, plantar, medial, and lateral cortices. In addition to considering principal strains (tension and compression) and maximum shear strains, we also considered material-axis (M-A) shear strains. M-A shear strains are those that are aligned along the long axis of the bone and are considered to have greater biomechanical relevance than maximum shear strains because failure theories of composite materials and bone are often based on stresses or strains in the principal material directions. We used the same load apparatus from our prior study of mule deer calcanei. Results showed that although the NA rotated up to 8° medially and 15° laterally during these off-axis loads, it did not shift dramatically until after transection of all tension members. When comparing results based on maximum shear strain data vs. M-A shear strain data, the dominant strain mode changed only in the plantar cortex - as expected (in accordance with our a priori view) it was tension when M-A shear strains were considered (shear : tension = 0.2) but changed to dominant shear when maximum shear strain data were considered (shear : tension = 1.3). This difference leads to different conclusions and speculations regarding which specific strain modes and magnitudes most strongly influence the emergence of the marked mineralization and histomorphological differences in the dorsal vs. plantar cortices. Consequently, our prior simplification of the deer calcaneus model as a simply loaded 'tension/compression bone' (i.e. plantar/dorsal) might be incorrect. In vivo and in finite element analyses are needed to determine whether describing it as a 'shear-tension/compression' bone is more accurate. Addressing this question will help to advance the artiodactyl calcaneus as an experimental model for bone adaptation studies.

Dynamic changes in electroencephalogram spectral power with varying apnea duration in older adults.

Sleep apnea elicits brain and physiological changes and its duration varies across the night. This study investigates the changes in the relative powers in electroencephalogram (EEG) frequency bands before and at apnea termination and as a function of apnea duration. The analysis was performed on 30 sleep records (375 apnea events) of older adults diagnosed with sleep apnea. Power spectral analysis centered on two 10-s EEG epochs, before apnea termination (BAT) and after apnea termination (AAT), for each apnea event. The relative power changes in EEG frequency bands were compared with changes in apnea duration, defined as Short (between 10 and 20 s), Moderate (between 20 and 30 s) and Long (between 30 and 40 s). A significant reduction in EEG relative powers for lower frequency bands of alpha and sigma were observed for the Long compared to the Moderate and Short apnea duration groups at BAT, and reduction in relative theta, alpha and sigma powers for the Long compared to the Moderate and Short groups at AAT. The proportion of apnea events showed a significantly decreased trend with increased apnea duration for non-rapid eye movement sleep but not rapid eye movement sleep. The proportion of central apnea events decreased with increased apnea duration, but not obstructive episodes. The findings suggest EEG arousal occurred both before and at apnea termination and these transient arousals were associated with a reduction in relative EEG powers of the low-frequency bands: theta, alpha and sigma. The clinical implication is that these transient EEG arousals, without awakenings, are protective of sleep. Further studies with large datasets and different age groups are recommended.

Air Quality Monitoring for Vulnerable Groups in Residential Environments Using a Multiple Hazard Gas Detector.

This paper presents a smart "e-nose" device to monitor indoor hazardous air. Indoor hazardous odor is a threat for seniors, infants, children, pregnant women, disabled residents, and patients. To overcome the limitations of using existing non-intelligent, slow-responding, deficient gas sensors, we propose a novel artificial-intelligent-based multiple hazard gas detector (MHGD) system that is mounted on a motor vehicle-based robot which can be remotely controlled. First, we optimized the sensor array for the classification of three hazardous gases, including cigarette smoke, inflammable ethanol, and off-flavor from spoiled food, using an e-nose with a mixing chamber. The mixing chamber can prevent the impact of environmental changes. We compared the classification results of all combinations of sensors, and selected the one with the highest accuracy (98.88%) as the optimal sensor array for the MHGD. The optimal sensor array was then mounted on the MHGD to detect and classify the target gases without a mixing chamber but in a controlled environment. Finally, we tested the MHGD under these conditions, and achieved an acceptable accuracy (70.00%).

Dynamic characteristics of oxygen consumption.

BACKGROUND: Previous studies have indicated that oxygen uptake ([Formula: see text]) is one of the most accurate indices for assessing the cardiorespiratory response to exercise. In most existing studies, the response of [Formula: see text] is often roughly modelled as a first-order system due to the inadequate stimulation and low signal to noise ratio. To overcome this difficulty, this paper proposes a novel nonparametric kernel-based method for the dynamic modelling of [Formula: see text] response to provide a more robust estimation. METHODS: Twenty healthy non-athlete participants conducted treadmill exercises with monotonous stimulation (e.g., single step function as input). During the exercise, [Formula: see text] was measured and recorded by a popular portable gas analyser ([Formula: see text], COSMED). Based on the recorded data, a kernel-based estimation method was proposed to perform the nonparametric modelling of [Formula: see text]. For the proposed method, a properly selected kernel can represent the prior modelling information to reduce the dependence of comprehensive stimulations. Furthermore, due to the special elastic net formed by [Formula: see text] norm and kernelised [Formula: see text] norm, the estimations are smooth and concise. Additionally, the finite impulse response based nonparametric model which estimated by the proposed method can optimally select the order and fit better in terms of goodness-of-fit comparing to classical methods. RESULTS: Several kernels were introduced for the kernel-based [Formula: see text] modelling method. The results clearly indicated that the stable spline (SS) kernel has the best performance for [Formula: see text] modelling. Particularly, based on the experimental data from 20 participants, the estimated response from the proposed method with SS kernel was significantly better than the results from the benchmark method [i.e., prediction error method (PEM)] ([Formula: see text] vs [Formula: see text]). CONCLUSIONS: The proposed nonparametric modelling method is an effective method for the estimation of the impulse response of VO2-Speed system. Furthermore, the identified average nonparametric model method can dynamically predict [Formula: see text] response with acceptable accuracy during treadmill exercise.

Genetically encoded calcium indicator illuminates calcium dynamics in primary cilia.

Visualization of signal transduction in live primary cilia constitutes a technical challenge owing to the organelle's submicrometer dimensions and close proximity to the cell body. Using a genetically encoded calcium indicator targeted to primary cilia, we visualized calcium signaling in cilia of mouse fibroblasts and kidney cells upon chemical or mechanical stimulation with high specificity, high sensitivity and wide dynamic range.

Modelling and regulating of cardio-respiratory response for the enhancement of interval training.

BACKGROUND: The interval training method has been a well known exercise protocol which helps strengthen and improve one's cardiovascular fitness. PURPOSE: To develop an effective training protocol to improve cardiovascular fitness based on modelling and analysis of Heart Rate (HR) and Oxygen Uptake (VO2) dynamics. METHODS: In order to model the cardiorespiratory response to the onset and offset exercises, the (K4b2, Cosmed) gas analyzer was used to monitor and record the heart rate and oxygen uptake for ten healthy male subjects. An interval training protocol was developed for young health users and was simulated using a proposed RC switching model which was presented to accommodate the variations of the cardiorespiratory dynamics to running exercises. A hybrid system model was presented to describe the adaptation process and a multi-loop PI control scheme was designed for the tuning of interval training regime. RESULTS: By observing the original data for each subject, we can clearly identify that all subjects have similar HR and VO2 profiles. The proposed model is capable to simulate the exercise responses during onset and offset exercises; it ensures the continuity of the outputs within the interval training protocol. Under some mild assumptions, a hybrid system model can describe the adaption process and accordingly a multi-loop PI controller can be designed for the tuning of interval training protocol. The self-adaption feature of the proposed controller gives the exerciser the opportunity to reach his desired setpoints after a certain number of training sessions. CONCLUSIONS: The established interval training protocol targets a range of 70-80% of HRmax which is mainly a training zone for the purpose of cardiovascular system development and improvement. Furthermore, the proposed multi-loop feedback controller has the potential to tune the interval training protocol according to the feedback from an individual exerciser.

An equivalent circuit model for onset and offset exercise response.

BACKGROUND: The switching exercise (e.g., Interval Training) has been a commonly used exercise protocol nowadays for the enhancement of exerciser's cardiovascular fitness. The current difficulty for simulating human onset and offset exercise responses regarding the switching exercise is to ensure the continuity of the outputs during onset-offset switching, as well as to accommodate the exercise intensities at both onset and offset of exercise. METHODS: Twenty-one untrained healthy subjects performed treadmill trials following both single switching exercise (e.g., single-cycle square wave protocol) and repetitive switching exercise (e.g., interval training protocol). During exercise, heart rate (HR) and oxygen uptake (VO2) were monitored and recorded by a portable gas analyzer (K4b 2, Cosmed). An equivalent single-supply switching resistance-capacitor (RC) circuit model was proposed to accommodate the observed variations of the onset and offset dynamics. The single-cycle square wave protocol was utilized to investigate the respective dynamics at onset and offset of exercise with the aerobic zone of approximate 70%-77% of HR max, and verify the adaption feature for the accommodation of different exercise strengths. The design of the interval training protocol was to verify the transient properties during onset-offset switching. A verification method including Root-mean-square-error (RMSE) and correlation coefficient, was introduced for comparisons between the measured data and model outputs. RESULTS: The experimental results from single-cycle square wave exercises clearly confirm that the onset and offset characteristics for both HR and VO2 are distinctly different. Based on the experimental data for both single and repetitive square wave exercise protocols, the proposed model was then presented to simulate the onset and offset exercise responses, which were well correlated indicating good agreement with observations. CONCLUSIONS: Compared with existing works, this model can accommodate the different exercise strengths at both onset and offset of exercise, while also depicting human onset and offset exercise responses, and guarantee the continuity of outputs during onset-offset switching. A unique adaption feature by allowing the time constant and steady state gain to re-shift back to their original states, more closely mimics the different exercise strengths during normal daily exercise activities.

Visceral Adiposity Independently Predicts Time to Flare in Inflammatory Bowel Disease but Body Mass Index Does Not.

BACKGROUND: Obesity is associated with progression of inflammatory bowel disease (IBD). Visceral adiposity may be a more meaningful measure of obesity compared with traditional measures such as body mass index (BMI). This study compared visceral adiposity vs BMI as predictors of time to IBD flare among patients with Crohn's disease and ulcerative colitis. METHODS: This was a retrospective cohort study. IBD patients were included if they had a colonoscopy and computed tomography (CT) scan within a 30-day window of an IBD flare. They were followed for 6 months or until their next flare. The primary exposure was the ratio of visceral adipose tissue to subcutaneous adipose tissue (VAT:SAT) obtained from CT imaging. BMI was calculated at the time of index CT scan. RESULTS: A total of 100 Crohn's disease and 100 ulcerative colitis patients were included. The median age was 43 (interquartile range, 31-58) years, 39% had disease duration of 10 years or more, and 14% had severe disease activity on endoscopic examination. Overall, 23% of the cohort flared with median time to flare 90 (interquartile range, 67-117) days. Higher VAT:SAT was associated with shorter time to IBD flare (hazard ratio of 4.8 for VAT:SAT ≥1.0 vs VAT:SAT ratio <1.0), whereas higher BMI was not associated with shorter time to flare (hazard ratio of 0.73 for BMI ≥25 kg/m2 vs BMI <25 kg/m2). The relationship between increased VAT:SAT and shorter time to flare appeared stronger for Crohn's than for ulcerative colitis. CONCLUSIONS: Visceral adiposity was associated with decreased time to IBD flare, but BMI was not. Future studies could test whether interventions that decrease visceral adiposity will improve IBD disease activity.

An increased ratio of visceral to subcutaneous adipose tissue was associated with a shorter time to flare in patients with both Crohn's and ulcerative colitis. Conversely, increased body mass index was not associated with a shorter time to flare in inflammatory bowel disease patients.

Spatiotemporal Compliance Control for a Wearable Lower Limb Rehabilitation Robot.

Compliance control is crucial for physical human-robot interaction, which can enhance the safety and comfort of robot-assisted rehabilitation. In this study, we designed a spatiotemporal compliance control strategy for a new self-designed wearable lower limb rehabilitation robot (WLLRR), allowing the users to regulate the spatiotemporal characteristics of their motion. The high-level trajectory planner consists of a trajectory generator, an interaction torque estimator, and a gait speed adaptive regulator, which can provide spatial and temporal compliance for the WLLRR. A radial basis function neural network adaptive controller is adopted as the low-level position controller. Over-ground walking experiments with passive control, spatial compliance control, and spatiotemporal compliance control strategies were conducted on five healthy participants, respectively. The results demonstrated that the spatiotemporal compliance control strategy allows participants to adjust reference trajectory through physical human-robot interaction, and can adaptively modify gait speed according to participants' motor performance. It was found that the spatiotemporal compliance control strategy could provide greater enhancement of motor variability and reduction of interaction torque than other tested control strategies. Therefore, the spatiotemporal compliance control strategy has great potential in robot-assisted rehabilitation training and other fields involving physical human-robot interaction.

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network.

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates.

Human Mpox Virus Proctitis With Persistent Rectal Ulcers on Sigmoidoscopy.

The 2022 Mpox outbreak has caused public health concerns worldwide. Mpox infection often manifests as papular skin lesions; other systemic complications have also been reported. We present the case of a 35-year-old man with HIV who presented with rectal pain and hematochezia and was found to have severe ulceration and exudate on sigmoidoscopy consistent with Mpox proctitis.

The use of novel electronic nose technology to locate missing persons for criminal investigations.

The search for missing persons is a major challenge for investigations involving presumed deceased individuals. Currently, the most effective tool is the use of cadaver-detection dogs; however, they are limited by their cost, limited operation times, and lack of granular information reported to the handler. Thus, there is a need for discrete, real-time detection methods that provide searchers explicit information as to whether human-decomposition volatiles are present. A novel e-nose (NOS.E) developed in-house was investigated as a tool to detect a surface-deposited individual over time. The NOS.E was able to detect the victim throughout most stages of decomposition and was influenced by wind parameters. The sensor responses from different chemical classes were compared to chemical class abundance confirmed by two-dimensional gas chromatography - time-of-flight mass spectrometry. The NOS.E demonstrated its ability to detect surface-deposited individuals days and weeks since death, demonstrating its utility as a detection tool.

Unsupervised machine-learning method for improving the performance of ambulatory fall-detection systems.

BACKGROUND: Falls can cause trauma, disability and death among older people. Ambulatory accelerometer devices are currently capable of detecting falls in a controlled environment. However, research suggests that most current approaches can tend to have insufficient sensitivity and specificity in non-laboratory environments, in part because impacts can be experienced as part of ordinary daily living activities. METHOD: We used a waist-worn wireless tri-axial accelerometer combined with digital signal processing, clustering and neural network classifiers. The method includes the application of Discrete Wavelet Transform, Regrouping Particle Swarm Optimization, Gaussian Distribution of Clustered Knowledge and an ensemble of classifiers including a multilayer perceptron and Augmented Radial Basis Function (ARBF) neural networks. RESULTS: Preliminary testing with 8 healthy individuals in a home environment yields 98.6% sensitivity to falls and 99.6% specificity for routine Activities of Daily Living (ADL) data. Single ARB and MLP classifiers were compared with a combined classifier. The combined classifier offers the greatest sensitivity, with a slight reduction in specificity for routine ADL and an increased specificity for exercise activities. In preliminary tests, the approach achieves 100% sensitivity on in-group falls, 97.65% on out-group falls, 99.33% specificity on routine ADL, and 96.59% specificity on exercise ADL. CONCLUSION: The pre-processing and feature-extraction steps appear to simplify the signal while successfully extracting the essential features that are required to characterize a fall. The results suggest this combination of classifiers can perform better than MLP alone. Preliminary testing suggests these methods may be useful for researchers who are attempting to improve the performance of ambulatory fall-detection systems.

Matlab Open Source Code: Noise-Assisted Multivariate Empirical Mode Decomposition Based Causal Decomposition for Causality Inference of Bivariate Time Series.

Causality inference has arrested much attention in academic studies. Currently, multiple methods such as Granger causality, Convergent Cross Mapping (CCM), and Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) are introduced to solve the problem. Motivated by the researchers who uploaded the open-source code for causality inference, we hereby present the Matlab code of NA-MEMD Causal Decomposition to help users implement the algorithm in multiple scenarios. The code is developed on Matlab2020 and is mainly divided into three subfunctions: na_memd, Plseries, and cd_na_memd. na_memd is called in the main function to generate the matrix of Intrinsic Mode Functions (IMFs) and Plseries can display the average frequency and phase difference of IMFs of the same order in a matrix which can be used for the selection of the main Intrinsic Causal Component (ICC) and ICCs set. cd_na_memd is called to perform causal redecomposition after removing the main ICC from the original time series and output the result of NA-MEMD Causal Decomposition. The performance of the code is evaluated from the perspective of executing time, robustness, and validity. With the data amount enlarging, the executing time increases linearly with it and the value of causal strength oscillates in an ideally small interval which represents the relatively high robustness of the code. The validity is verified based on the open-access predator-prey data (wolf-moose bivariate time series from Isle Royale National Park in Michigan, USA) and our result is aligned with that of Causal Decomposition.