A COVID-19 Non-contact Screening System Based on XGBoost and Logistic Regression (Preprint)

2021 ◽  
Author(s):  
Chunheng Shang ◽  
Yixian Qiao ◽  
Xiwen Liao ◽  
Xiaoning Yuan ◽  
Qin Cheng ◽  
...  
Keyword(s):  
Heart Rate ◽  
Logistic Regression ◽  
Body Movement ◽  
Recall Rate ◽  
Machine Learning Algorithms ◽  
Ultra Wideband ◽  
Monitoring Data ◽  
Screening System ◽  
Contact Screening ◽  
Radar Monitoring

BACKGROUND COVID-19 is a new infectious disease with high infectivity. At present, body temperature detection is the main method for primary screening, but this single detection method has poor accuracy and is easy to miss detection. OBJECTIVE The objective of our study was to propose a non-contact, high-precision COVID-19 screening system. METHODS We used impulse-radio ultra-wideband (IR-UWB) radar to detect the respiration, heart rate, body movement, sleep quality, and various other physiological indicators. We collected 140 radar monitoring data from 23 COVID-19 patients in Wuhan Tongji Hospital, and compared them with 144 radar monitoring data of healthy controls. Then XGBoost and logistic regression(XGBoost+LR) algorithm was used to classify the data of patients and healthy people; feature selection was performed by SHAP value; using ten-fold cross-validation, XGBoost+LR algorithm was compared with five other classic classification algorithms, and the classification performance was evaluated by precision, recall, and the area under the ROC curve( AUC ). RESULTS The XGBoost+LR algorithm demonstrate excellent discrimination (precision=99.1 %, recall rate = 94.1 %, AUC=98.7 %), which is superior to several other single machine learning algorithms. In addition, the SHAP value indicate that number of apnea during REM(‘ REMSATims’) and mean heart rate(‘meanHR’) are important features for classification. CONCLUSIONS The COVID-19 non-contact screening system based on XGBoost+LR algorithm can accurately predict COVID-19 patients and can be applied in isolation wards to effectively help medical staff.

scholarly journals Motion-Tolerant Non-Contact Heart-Rate Measurements from Radar Sensor Fusion

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1774
Author(s):  
Yu Rong ◽  
Arindam Dutta ◽  
Alex Chiriyath ◽  
Daniel W. Bliss
Keyword(s):  
Heart Rate ◽  
Vital Sign ◽  
Human Subjects ◽  
Body Movement ◽  
Processing Technique ◽  
Ultra Wideband ◽  
Signal Processing Technique ◽  
Active Motion ◽  
Microwave Radar ◽  
Radar Measurements

Microwave radar technology is very attractive for ubiquitous short-range health monitoring due to its non-contact, see-through, privacy-preserving and safe features compared to the competing remote technologies such as optics. The possibility of radar-based approaches for breathing and cardiac sensing was demonstrated a few decades ago. However, investigation regarding the robustness of radar-based vital-sign monitoring (VSM) is not available in the current radar literature. In this paper, we aim to close this gap by presenting an extensive experimental study of vital-sign radar approach. We consider diversity in test subjects, fitness levels, poses/postures, and, more importantly, random body movement (RBM) in the study. We discuss some new insights that lead to robust radar heart-rate (HR) measurements. A novel active motion cancellation signal-processing technique is introduced, exploiting dual ultra-wideband (UWB) radar system for motion-tolerant HR measurements. Additionally, we propose a spectral pruning routine to enhance HR estimation performance. We validate the proposed method theoretically and experimentally. Totally, we record and analyze about 3500 seconds of radar measurements from multiple human subjects.

scholarly journals HEAR: Approach for Heartbeat Monitoring with Body Movement Compensation by IR-UWB Radar

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 3077 ◽  
Author(s):  
Wenfeng Yin ◽  
Xiuzhu Yang ◽  
Lei Li ◽  
Lin Zhang ◽  
Nattapong Kitsuwan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Body Movement ◽  
Large Body ◽  
Ultra Wideband ◽  
Heart Rate Variability Analysis ◽  
Mean Deviation ◽  
Fast Time ◽  
Heart Rates ◽  
Mode Decomposition ◽  
Wideband Radar

Further applications of impulse radio ultra-wideband radar in mobile health are hindered by the difficulty in extracting such vital signals as heartbeats from moving targets. Although the empirical mode decomposition based method is applied in recovering waveforms of heartbeats and estimating heart rates, the instantaneous heart rate is not achievable. This paper proposes a Heartbeat Estimation And Recovery (HEAR) approach to expand the application to mobile scenarios and extract instantaneous heartbeats. Firstly, the HEAR approach acquires vital signals by mapping maximum echo amplitudes to the fast time delay and compensating large body movements. Secondly, HEAR adopts the variational nonlinear chirp mode decomposition in extracting instantaneous frequencies of heartbeats. Thirdly, HEAR extends the clutter removal method based on the wavelet decomposition with a two-parameter exponential threshold. Compared to heart rates simultaneously collected by electrocardiograms (ECG), HEAR achieves a minimum error rate 4.6% in moving state and 2.25% in resting state. The Bland–Altman analysis verifies the consistency of beat-to-beat intervals in ECG and extracted heartbeat signals with the mean deviation smaller than 0.1 s. It indicates that HEAR is practical in offering clinical diagnoses such as the heart rate variability analysis in mobile monitoring.

scholarly journals Non-contact screening system based for COVID-19 on XGBoost and logistic regression

2021 ◽  
pp. 105003
Author(s):  
Chunjiao Dong ◽  
Yixian Qiao ◽  
Chunheng Shang ◽  
Xiwen Liao ◽  
Xiaoning Yuan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Screening System ◽  
Contact Screening

scholarly journals Feasibility of Using Floor Vibration to Detect Human Falls

2020 ◽  
Vol 18 (1) ◽  
pp. 200
Author(s):  
Yu Shao ◽  
Xinyue Wang ◽  
Wenjie Song ◽  
Sobia Ilyas ◽  
Haibo Guo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Human Body ◽  
Nearest Neighbor ◽  
Body Movement ◽  
Modern Society ◽  
Recognition System ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Study Results ◽  
Floor Vibration

With the increasing aging population in modern society, falls as well as fall-induced injuries in elderly people become one of the major public health problems. This study proposes a classification framework that uses floor vibrations to detect fall events as well as distinguish different fall postures. A scaled 3D-printed model with twelve fully adjustable joints that can simulate human body movement was built to generate human fall data. The mass proportion of a human body takes was carefully studied and was reflected in the model. Object drops, human falling tests were carried out and the vibration signature generated in the floor was recorded for analyses. Machine learning algorithms including K-means algorithm and K nearest neighbor algorithm were introduced in the classification process. Three classifiers (human walking versus human fall, human fall versus object drop, human falls from different postures) were developed in this study. Results showed that the three proposed classifiers can achieve the accuracy of 100, 85, and 91%. This paper developed a framework of using floor vibration to build the pattern recognition system in detecting human falls based on a machine learning approach.

Psychological behavior signal analysis for sports person during their activity session

2021 ◽  
pp. 1-10
Author(s):  
Guochun Liu ◽  
Jian Zheng ◽  
Lin Jiang ◽  
Karthik Chandran ◽  
Beenu Mago
Keyword(s):  
Heart Rate ◽  
Signal Detection ◽  
Signal Analysis ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Psychological Skills ◽  
Social Dimensions ◽  
Useful Knowledge ◽  
Structural Problems ◽  
Psychological Behavior

The signal analysis helps us derive useful knowledge from biological processes to analyze, describe, and understand their origin mechanisms. However, biomedical signals are not immune and have time-consuming statistics. The major challenges of signal analysis of sportsperson are reliability and accuracy. Sports psychology uses psychological skills to discuss the optimum success and well-being of sports athletes, the developmental and social dimensions of the sport and sports facilities, and structural problems. The signal detection tool is used to detect the best combination of long-term practice predictors for active, sedentary adults’ signal. This paper proposed the wearable assisted signal detection method (WASDM) to find the sportspersons’ behavior signal analysis. This method performs an IoT based heart rate monitoring using a wearable device named intelligent bracelet mounted on the sportsperson to track the variations in his/her human heart rate. The wearable signal detector method analysis the heart rate abnormality and predicts health status, followed by an alarm to the physician and the respective personnel while performing activity session. In this research, various machine learning algorithms have been tried to perform signal analysis and prediction and compared their results to suggest the best in this application scenario. Finally, the experimental analysis shows better outcomes for the sportspersons’ psychological behavior signal analysis than the conventional methods.

scholarly journals Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

2020 ◽  
Vol 30 (1) ◽  
pp. 192-208 ◽  
Author(s):  
Hamza Aldabbas ◽  
Abdullah Bajahzar ◽  
Meshrif Alruily ◽  
Ali Adil Qureshi ◽  
Rana M. Amir Latif ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Language Processing ◽  
Mobile Application ◽  
Knowledge Engineering ◽  
Machine Learning Algorithms ◽  
Application Development ◽  
User Reviews ◽  
N Gram ◽  
Logistic Regression Algorithm ◽  
Google Play

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.

scholarly journals UWB-gestures, a public dataset of dynamic hand gestures acquired using impulse radar sensors

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Shahzad Ahmed ◽  
Dingyang Wang ◽  
Junyoung Park ◽  
Sung Ho Cho
Keyword(s):  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Ultra Wideband ◽  
Reproducible Research ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Radar Sensors ◽  
The Past ◽  
Human Volunteers ◽  
Public Dataset

AbstractIn the past few decades, deep learning algorithms have become more prevalent for signal detection and classification. To design machine learning algorithms, however, an adequate dataset is required. Motivated by the existence of several open-source camera-based hand gesture datasets, this descriptor presents UWB-Gestures, the first public dataset of twelve dynamic hand gestures acquired with ultra-wideband (UWB) impulse radars. The dataset contains a total of 9,600 samples gathered from eight different human volunteers. UWB-Gestures eliminates the need to employ UWB radar hardware to train and test the algorithm. Additionally, the dataset can provide a competitive environment for the research community to compare the accuracy of different hand gesture recognition (HGR) algorithms, enabling the provision of reproducible research results in the field of HGR through UWB radars. Three radars were placed at three different locations to acquire the data, and the respective data were saved independently for flexibility.

scholarly journals Prediction of Patients with Acute Cholecystitis Requiring Emergent Cholecystectomy: A Simple Score

2010 ◽  
Vol 2010 ◽  
pp. 1-5 ◽  
Author(s):  
Wael N. Yacoub ◽  
Mikael Petrosyan ◽  
Indu Sehgal ◽  
Yanling Ma ◽  
Parakrama Chandrasoma ◽  
...  
Keyword(s):  
Heart Rate ◽  
Logistic Regression ◽  
Acute Cholecystitis ◽  
High Probability ◽  
Acute Inflammation ◽  
Gallbladder Wall ◽  
Cutoff Score ◽  
Gangrenous Cholecystitis ◽  
Pathological Review ◽  
Low Probability

The objective was to develop a score, to stratify patients with acute cholecystitis into high, intermediate, or low probability of gangrenous cholecystitis. The probability of gangrenous cholecystitis (score) was derived from a logistic regression of a clinical and pathological review of 245 patients undergoing urgent cholecystectomy. Sixty-eight patients had gangrenous inflammation, 132 acute, and 45 no inflammation. The score comprised of: age > 45 years (1 point), heart rate > 90 beats/min (1 point), male (2 points), Leucocytosis > 13,000/mm3(1.5 points), and ultrasound gallbladder wall thickness>4.5 mm (1 point). The prevalence of gangrenous cholecystitis was 13% in the low-probability (0–2 points), 33% in the intermediate-probability (2–4.5 points), and 87% in the high probability category (>4.5 points). A cutoff score of 2 identified 31 (69%) patients with no acute inflammation (PPV 90%). This scoring system can prioritize patients for emergent cholecystectomy based on their expected pathology.

Implementation of Machine Learning Algorithms for Prediction of Fluidelastic Instability in Tube Arrays

2021 ◽  
Vol 143 (2) ◽  
Author(s):  
Joaquin E. Moran ◽  
Yasser Selima
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Two Phase ◽  
Factors Affecting ◽  
Logistic Regression Models ◽  
Number Of Factors ◽  
Tube Arrays ◽  
Fluidelastic Instability

Abstract Fluidelastic instability (FEI) in tube arrays has been studied extensively experimentally and theoretically for the last 50 years, due to its potential to cause significant damage in short periods. Incidents similar to those observed at San Onofre Nuclear Generating Station indicate that the problem is not yet fully understood, probably due to the large number of factors affecting the phenomenon. In this study, a new approach for the analysis and interpretation of FEI data using machine learning (ML) algorithms is explored. FEI data for both single and two-phase flows have been collected from the literature and utilized for training a machine learning algorithm in order to either provide estimates of the reduced velocity (single and two-phase) or indicate if the bundle is stable or unstable under certain conditions (two-phase). The analysis included the use of logistic regression as a classification algorithm for two-phase flow problems to determine if specific conditions produce a stable or unstable response. The results of this study provide some insight into the capability and potential of logistic regression models to analyze FEI if appropriate quantities of experimental data are available.

scholarly journals Non-intrusive vital sign monitoring using an intelligent pillow based on a piezoelectric ceramic sensor

2020 ◽  
Vol 15 ◽  
pp. 155892502097726
Author(s):  
Wei Wang ◽  
Zhiqiang Pang ◽  
Ling Peng ◽  
Fei Hu
Keyword(s):  
Heart Rate ◽  
Real Time ◽  
Piezoelectric Ceramic ◽  
Low Cost ◽  
Body Movement ◽  
Vital Signs ◽  
High Heart Rate ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Real Time Monitoring

Performing real-time monitoring for human vital signs during sleep at home is of vital importance to achieve timely detection and rescue. However, the existing smart equipment for monitoring human vital signs suffers the drawbacks of high complexity, high cost, and intrusiveness, or low accuracy. Thus, it is of great need to develop a simplified, nonintrusive, comfortable and low cost real-time monitoring system during sleep. In this study, a novel intelligent pillow was developed based on a low-cost piezoelectric ceramic sensor. It was manufactured by locating a smart system (consisting of a sensing unit i.e. a piezoelectric ceramic sensor, a data processing unit and a GPRS communication module) in the cavity of the pillow made of shape memory foam. The sampling frequency of the intelligent pillow was set at 1000 Hz to capture the signals more accurately, and vital signs including heart rate, respiratory rate and body movement were derived through series of well established algorithms, which were sent to the user’s app. Validation experimental results demonstrate that high heart-rate detection accuracy (i.e. 99.18%) was achieved in using the intelligent pillow. Besides, human tests were conducted by detecting vital signs of six elder participants at their home, and results showed that the detected vital signs may well predicate their health conditions. In addition, no contact discomfort was reported by the participants. With further studies in terms of validity of the intelligent pillow and large-scale human trials, the proposed intelligent pillow was expected to play an important role in daily sleep monitoring.

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