scholarly journals Adaptive nowcasting of influenza outbreaks using  Google searches

2014 ◽  
Vol 1 (2) ◽  
pp. 140095 ◽  
Author(s):  
Tobias Preis ◽  
Helen Susannah Moat
Keyword(s):  
Real Time ◽  
Absolute Error ◽  
Time Estimates ◽  
Adaptive Models ◽  
Training Interval ◽  
Out Of Sample ◽  
Historic Data ◽  
The Mean ◽  
Influenza Outbreaks ◽  
Official Data

Seasonal influenza outbreaks and pandemics of new strains of the influenza virus affect humans around the globe. However, traditional systems for measuring the spread of flu infections deliver results with one or two weeks delay. Recent research suggests that data on queries made to the search engine Google can be used to address this problem, providing real-time estimates of levels of influenza-like illness in a population. Others have however argued that equally good estimates of current flu levels can be forecast using historic flu measurements. Here, we build dynamic ‘nowcasting’ models; in other words, forecasting models that estimate current levels of influenza, before the release of official data one week later. We find that when using Google Flu Trends data in combination with historic flu levels, the mean absolute error (MAE) of in-sample ‘nowcasts’ can be significantly reduced by 14.4%, compared with a baseline model that uses historic data on flu levels only. We further demonstrate that the MAE of out-of-sample nowcasts can also be significantly reduced by between 16.0% and 52.7%, depending on the length of the sliding training interval. We conclude that, using adaptive models, Google Flu Trends data can indeed be used to improve real-time influenza monitoring, even when official reports of flu infections are available with only one week's delay.

USING BAIDU INDEX TO NOWCAST MOBILE PHONE SALES IN CHINA

2019 ◽  
Vol 64 (01) ◽  
pp. 83-96 ◽  
Author(s):  
JIANCHUN FANG ◽  
WANSHAN WU ◽  
ZHOU LU ◽  
EUNHO CHO
Keyword(s):  
Real Time ◽  
Decision Makers ◽  
Extended Model ◽  
Time Data ◽  
Inventory Costs ◽  
Marketing Performance ◽  
Out Of Sample ◽  
Chinese Consumer ◽  
Lag Period ◽  
Official Data

Most of the official data are released with a lag period, which increases the difficulties for decision-makers assessing the situation. To solve the problem of data lag, we used real-time Baidu Index to nowcast the Chinese consumer behavior of buying the best-selling smartphone, Huawei Mate 7. We introduced keywords like “Mate 7” and “Huawei” in Baidu Index search queries to examine whether the introduction of real-time data can improve the efficiency of benchmark model. Overall, our finding is that the introduction of Baidu Index, both in-sample and out-of-sample, can improve the prediction accuracy of the model significantly. The extended model provided a 55.2% outperformance relative to benchmark one. This can not only make up for official data release lag, but also help firms gain near-real-time insight into the consumer demand trends and reduce inventory costs. The findings suggest that firms can improve marketing performance by use of search engine promotion campaign.

scholarly journals Real-Time Detection and Location of Potted Flowers Based on a ZED Camera and a YOLO V4-Tiny Deep Learning Algorithm

Horticulturae ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 21
Author(s):  
Jizhang Wang ◽  
Zhiheng Gao ◽  
Yun Zhang ◽  
Jing Zhou ◽  
Jianzhi Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Point Cloud ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Frame Rate ◽  
3D Point Cloud ◽  
The Real ◽  
Deep Learning Algorithm ◽  
The Mean

In order to realize the real-time and accurate detection of potted flowers on benches, in this paper we propose a method based on the ZED 2 stereo camera and the YOLO V4-Tiny deep learning algorithm for potted flower detection and location. First, an automatic detection model of flowers was established based on the YOLO V4-Tiny convolutional neural network (CNN) model, and the center points on the pixel plane of the flowers were obtained according to the prediction box. Then, the real-time 3D point cloud information obtained by the ZED 2 camera was used to calculate the actual position of the flowers. The test results showed that the mean average precision (MAP) and recall rate of the training model was 89.72% and 80%, respectively, and the real-time average detection frame rate of the model deployed under Jetson TX2 was 16 FPS. The results of the occlusion experiment showed that when the canopy overlap ratio between the two flowers is more than 10%, the recognition accuracy will be affected. The mean absolute error of the flower center location based on 3D point cloud information of the ZED 2 camera was 18.1 mm, and the maximum locating error of the flower center was 25.8 mm under different light radiation conditions. The method in this paper establishes the relationship between the detection target of flowers and the actual spatial location, which has reference significance for the machinery and automatic management of potted flowers on benches.

scholarly journals Real-Time Digital Biometric Monitoring during Elite Athletic Competition: System Feasibility with a Wearable Medical-Grade Sensor

2021 ◽  
pp. 37-43
Author(s):  
Mark A. Gorski ◽  
Stanley M. Mimoto ◽  
Vivek Khare ◽  
Viprali Bhatkar ◽  
Arthur H. Combs
Keyword(s):  
Real Time ◽  
Athletic Performance ◽  
Elite Athletes ◽  
Absolute Error ◽  
Athletic Competition ◽  
Technology System ◽  
Novel Technology ◽  
The Mean ◽  
Wearable Medical ◽  
Medical Grade

<b><i>Introduction:</i></b> Real-time digital heart rate (HR) monitoring in sports can provide unique physiological insights into athletic performance. However, most HR monitoring of elite athletes is limited to non-real-time, non-competition settings while utilizing sensors that are cumbersome. The present study was undertaken to test the feasibility of using small, wearable medical-grade sensors, paired with a novel technology system, to capture and process real-time HR data from elite athletes during professional competition. <b><i>Methods:</i></b> We examined the performance of the BioStamp nPoint® sensor compared to the Polar chest strap HR sensor in 15 Professional Squash Association (PSA) tournament matches in 2019–2020. Fourteen male professional squash players volunteered for the study (age = 23.8 ± 4.9 years; height = 177.9 ± 7.1 cm; weight = 71 ± 7.0 kg), which was approved by the PSA in accordance with their Code of General Conduct and Ethics. Algorithms developed by Sports Data Labs (SDL; Detroit, MI, USA) used proprietary data collection, transmission, and signal processing protocols to produce HR values in real-time during matches. We calculated the mean and maximum HR from both sensors and used widely accepted measures of agreement to compare their performance. <b><i>Results:</i></b> The system captured 99.8% of HR data across all matches (range 98.3–100%). The BioStamp’s mean HR was 170.4 ± 20.3 bpm, while the Polar’s mean HR was 169.4 ± 21.7 bpm. Maximum HR ranged from 182 to 202 bpm (Polar) and 185 to 203 bpm (BioStamp). Spearman’s correlation coefficient (<i>r</i><sub>s</sub>) was 0.986 (<i>p</i> &#x3c; 0.001), indicating a strong correlation between the 2 devices. The mean difference (<i>d</i>) in HR was 1.0 bpm, the mean absolute error was 2.2 bpm, and the percent difference was 0.72%, demonstrating high agreement between device measurements. <b><i>Conclusions:</i></b> It is feasible to accurately measure and monitor real-time HR in elite athletes during competition using BioStamp’s and SDL’s proprietary system. This system facilitates development and understanding of physiological digital biomarkers of athletic performance and physical and psychosocial demands in elite athletic competition.

scholarly journals Electrical stimulator with mechanomyography-based real-time monitoring, muscle fatigue detection, and safety shut-off: a pilot study

2020 ◽  
Vol 65 (4) ◽  
pp. 461-468
Author(s):  
Jannatul Naeem ◽  
Nur Azah Hamzaid ◽  
Amelia Wong Azman ◽  
Manfred Bijak
Keyword(s):  
Muscle Fatigue ◽  
Real Time ◽  
Isometric Force ◽  
Mean Square ◽  
Clear Indication ◽  
Initial Value ◽  
Cord Injury ◽  
The Mean ◽  
Electrical Stimulator ◽  
Fatigue Detection

AbstractFunctional electrical stimulation (FES) has been used to produce force-related activities on the paralyzed muscle among spinal cord injury (SCI) individuals. Early muscle fatigue is an issue in all FES applications. If not properly monitored, overstimulation can occur, which can lead to muscle damage. A real-time mechanomyography (MMG)-based FES system was implemented on the quadriceps muscles of three individuals with SCI to generate an isometric force on both legs. Three threshold drop levels of MMG-root mean square (MMG-RMS) feature (thr50, thr60, and thr70; representing 50%, 60%, and 70% drop from initial MMG-RMS values, respectively) were used to terminate the stimulation session. The mean stimulation time increased when the MMG-RMS drop threshold increased (thr50: 22.7 s, thr60: 25.7 s, and thr70: 27.3 s), indicating longer sessions when lower performance drop was allowed. Moreover, at thr70, the torque dropped below 50% from the initial value in 14 trials, more than at thr50 and thr60. This is a clear indication of muscle fatigue detection using the MMG-RMS value. The stimulation time at thr70 was significantly longer (p = 0.013) than that at thr50. The results demonstrated that a real-time MMG-based FES monitoring system has the potential to prevent the onset of critical muscle fatigue in individuals with SCI in prolonged FES sessions.

scholarly journals Estimation of Motion and Respiratory Characteristics during the Meditation Practice Based on Video Analysis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3771
Author(s):  
Alexey Kashevnik ◽  
Walaa Othman ◽  
Igor Ryabchikov ◽  
Nikolay Shilov
Keyword(s):  
Respiratory Rate ◽  
Evaluation System ◽  
Body Movement ◽  
Absolute Error ◽  
The Body ◽  
Meditation Practice ◽  
Body Parts ◽  
Mental Health Training ◽  
Negative Thoughts ◽  
The Mean

Meditation practice is mental health training. It helps people to reduce stress and suppress negative thoughts. In this paper, we propose a camera-based meditation evaluation system, that helps meditators to improve their performance. We rely on two main criteria to measure the focus: the breathing characteristics (respiratory rate, breathing rhythmicity and stability), and the body movement. We introduce a contactless sensor to measure the respiratory rate based on a smartphone camera by detecting the chest keypoint at each frame, using an optical flow based algorithm to calculate the displacement between frames, filtering and de-noising the chest movement signal, and calculating the number of real peaks in this signal. We also present an approach to detecting the movement of different body parts (head, thorax, shoulders, elbows, wrists, stomach and knees). We have collected a non-annotated dataset for meditation practice videos consists of ninety videos and the annotated dataset consists of eight videos. The non-annotated dataset was categorized into beginner and professional meditators and was used for the development of the algorithm and for tuning the parameters. The annotated dataset was used for evaluation and showed that human activity during meditation practice could be correctly estimated by the presented approach and that the mean absolute error for the respiratory rate is around 1.75 BPM, which can be considered tolerable for the meditation application.

scholarly journals Real-Time Musculoskeletal Kinematics and Dynamics Analysis Using Marker- and IMU-Based Solutions in Rehabilitation

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1804
Author(s):  
Dimitar Stanev ◽  
Konstantinos Filip ◽  
Dimitrios Bitzas ◽  
Sokratis Zouras ◽  
Georgios Giarmatzis ◽  
...  
Keyword(s):  
Real Time ◽  
Small Error ◽  
Grand Challenge ◽  
Muscle Forces ◽  
Time Estimates ◽  
Musculoskeletal Models ◽  
Open Source Framework ◽  
Measurement Units ◽  
Technical Details ◽  
Significant Milestone

This study aims to explore the possibility of estimating a multitude of kinematic and dynamic quantities using subject-specific musculoskeletal models in real-time. The framework was designed to operate with marker-based and inertial measurement units enabling extensions far beyond dedicated motion capture laboratories. We present the technical details for calculating the kinematics, generalized forces, muscle forces, joint reaction loads, and predicting ground reaction wrenches during walking. Emphasis was given to reduce computational latency while maintaining accuracy as compared to the offline counterpart. Notably, we highlight the influence of adequate filtering and differentiation under noisy conditions and its importance for consequent dynamic calculations. Real-time estimates of the joint moments, muscle forces, and reaction loads closely resemble OpenSim’s offline analyses. Model-based estimation of ground reaction wrenches demonstrates that even a small error can negatively affect other estimated quantities. An application of the developed system is demonstrated in the context of rehabilitation and gait retraining. We expect that such a system will find numerous applications in laboratory settings and outdoor conditions with the advent of predicting or sensing environment interactions. Therefore, we hope that this open-source framework will be a significant milestone for solving this grand challenge.

scholarly journals Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 68
Author(s):  
Jiwei Fan ◽  
Xiaogang Yang ◽  
Ruitao Lu ◽  
Xueli Xie ◽  
Weipeng Li
Keyword(s):  
Deep Learning ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Active Role ◽  
Absolute Error ◽  
Face Mask ◽  
Learning Technology ◽  
Flight System ◽  
Crowd Density ◽  
The Mean

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals Feasibility study of real-time online text-based CBT to support self-management for people with type 1 diabetes: the Diabetes On-line Therapy (DOT) Study

2021 ◽  
Vol 9 (1) ◽  
pp. e001934
Author(s):  
Anne M Doherty ◽  
Anne Herrmann-Werner ◽  
Arann Rowe ◽  
Jennie Brown ◽  
Scott Weich ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Glycemic Control ◽  
Real Time ◽  
Instant Messaging ◽  
Controlled Trial ◽  
Self Management ◽  
Diabetes Distress ◽  
The Mean ◽  
Distress Scale

IntroductionThis study examines the feasibility of conducting diabetes-focused cognitive–behavioral therapy (CBT) via a secure online real-time instant messaging system intervention to support self-management and improve glycemic control in people with type 1 diabetes.Research design and methodsWe used a pre–post uncontrolled intervention design over 12 months. We recruited adults with type 1 diabetes and suboptimal glycemic control (HbA1c ≥69 mmol/mol (DCCT 8.5%) for 12 months) across four hospitals in London. The intervention comprised 10 sessions of diabetes-focused CBT delivered by diabetes specialist nurses. The primary outcomes were number of eligible patients, rates of recruitment and follow-up, number of sessions completed and SD of the main outcome measure, change in HbA1c over 12 months. We measured the feasibility of collecting secondary outcomes, that is, depression measured using Patient Health Questionnaire-9 (PHQ-9), anxiety measured Generalised Anxiety Disorder (GAD) and the Diabetes Distress Scale (DDS).ResultsWe screened 3177 patients, of whom 638 were potentially eligible, from whom 71 (11.1%) were recruited. The mean age was 28.1 (13.1) years, and the mean HbA1c was 84.6 mmol/mol (17.8), DCCT 9.9%. Forty-six (65%) patients had at least 1 session and 29 (41%) completed all sessions. There was a significant reduction in HbA1c over 12 months (mean difference −6.2 (2.3) mmol/mol, DCCT 0.6%, p=0.038). The change scores in PHQ-9, GAD and DDS also improved.ConclusionsIt would be feasible to conduct a full-scale text-based synchronized real-time diabetes-focused CBT as an efficacy randomized controlled trial.

scholarly journals An Estimation Method of Continuous Non-Invasive Arterial Blood Pressure Waveform Using Photoplethysmography: A U-Net Architecture-Based Approach

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1867
Author(s):  
Tasbiraha Athaya ◽  
Sunwoong Choi
Keyword(s):  
Blood Pressure ◽  
Cardiovascular Health ◽  
Estimation Method ◽  
Absolute Error ◽  
British Hypertension Society ◽  
Non Invasive ◽  
Arterial Blood ◽  
Blood Pressure Waveform ◽  
The Mean ◽  
Mimic Iii

Blood pressure (BP) monitoring has significant importance in the treatment of hypertension and different cardiovascular health diseases. As photoplethysmogram (PPG) signals can be recorded non-invasively, research has been highly conducted to measure BP using PPG recently. In this paper, we propose a U-net deep learning architecture that uses fingertip PPG signal as input to estimate arterial BP (ABP) waveform non-invasively. From this waveform, we have also measured systolic BP (SBP), diastolic BP (DBP), and mean arterial pressure (MAP). The proposed method was evaluated on a subset of 100 subjects from two publicly available databases: MIMIC and MIMIC-III. The predicted ABP waveforms correlated highly with the reference waveforms and we have obtained an average Pearson’s correlation coefficient of 0.993. The mean absolute error is 3.68 ± 4.42 mmHg for SBP, 1.97 ± 2.92 mmHg for DBP, and 2.17 ± 3.06 mmHg for MAP which satisfy the requirements of the Association for the Advancement of Medical Instrumentation (AAMI) standard and obtain grade A according to the British Hypertension Society (BHS) standard. The results show that the proposed method is an efficient process to estimate ABP waveform directly using fingertip PPG.

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