scholarly journals A Low-cost Life Sign Detection Method based on Time Series Analysis of Facial Feature Points

2017 ◽  
Author(s):  
Timon Bloecher ◽  
Leyre Garralda Iriarte ◽  
Johannes Schneider ◽  
Christoph Zimmermann ◽  
Wilhelm Stork
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Detection Method ◽  
Low Cost ◽  
Facial Feature ◽  
Feature Points ◽  
Series Analysis ◽  
Sign Detection

Robot Collision Detection Without External Sensors Based on Time-Series Analysis

2020 ◽  
Vol 143 (4) ◽  
Author(s):  
Tie Zhang ◽  
Peizhong Ge ◽  
Yanbiao Zou ◽  
Yingwu He
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Collision Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Industrial Robot ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
External Torque ◽  
Series Analysis

Abstract To ensure the human safety in the process of human–robot cooperation, this paper proposes a robot collision detection method without external sensors based on time-series analysis (TSA). In the investigation, first, based on the characteristics of the external torque of the robot, the internal variation of the external torque sequence during the movement of the robot is analyzed. Next, a time-series model of the external torque is constructed, which is used to predict the external torque according to the historical motion information of the robot and generate a dynamic threshold. Then, the detailed process of time-series analysis for collision detection is described. Finally, the real-machine experiment scheme of the proposed real-time collision detection algorithm is designed and is used to perform experiments with a six degrees-of-freedom (6DOF) articulated industrial robot. The results show that the proposed method helps to obtain a detection accuracy of 100%; and that, as compared with the existing collision detection method based on a fixed symmetric threshold, the proposed method based on TSA possesses smaller detection delay and is more feasible in eliminating the sensitivity difference of collision detection in different directions.

scholarly journals Divergences on expected pneumonia cases during the COVID-19 epidemic in Catalonia: A time-series analysis of primary care electronic health records covering about 6 million people

2021 ◽  
Author(s):  
Ermengol Coma ◽  
Leonardo Méndez-Boo ◽  
Núria Mora ◽  
Carolina Guiriguet ◽  
Mència Benítez ◽  
...  
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Electronic Health Records ◽  
Time Series Analysis ◽  
Surveillance System ◽  
Low Cost ◽  
Time Series Regression ◽  
Health Records ◽  
Series Analysis ◽  
Electronic Health

AbstractBackgroundPneumonia is one of the complications of COVID-19. Primary care electronic health records (EHR) have shown the utility as a surveillance system.AimTo analyze the trends of pneumonia during two waves of COVID-19 pandemic in order to use it as a clinical surveillance system and an early indicator of severity.MethodsTime series analysis of pneumonia cases, January 2014-December 2020. We collected pneumonia diagnoses from primary care EHR, covering >6 million people in Catalonia (Spain). We compared the trend of pneumonia in the season 2019-2020 with that in the previous years. We estimated the expected pneumonia cases with data from 2014 to 2018 using a time series regression adjusted by seasonality and influenza epidemics.ResultsBetween 4 March and 5 May 2020, 11,704 excess pneumonia cases (95% CI: 9,909 to 13,498) were identified. We observed a second excess pneumonia period from 22 october to 15 november of 1,377 excess cases (95% CI: 665 to 2,089). In contrast, we observed two great periods with reductions of pneumonia cases in children, accounting for 131 days and 3,534 less pneumonia cases (95% CI: 1,005 to 6,064) from March to July; and 54 days and 1,960 less pneumonia cases (95% CI 917 to 3,002) from October to December.ConclusionsDiagnoses of pneumonia from the EHR could be used as an early and low cost surveillance system to monitor the spread of COVID-19.

scholarly journals Divergences on expected pneumonia cases during the COVID-19 epidemic in Catalonia: a time-series analysis of primary care electronic health records covering about 6 million people

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ermengol Coma ◽  
Leonardo Méndez-Boo ◽  
Núria Mora ◽  
Carolina Guiriguet ◽  
Mència Benítez ◽  
...  
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Electronic Health Records ◽  
Time Series Analysis ◽  
Surveillance System ◽  
Low Cost ◽  
Time Series Regression ◽  
Health Records ◽  
Series Analysis ◽  
Electronic Health

Abstract Background Pneumonia is one of the complications of COVID-19. Primary care electronic health records (EHR) have shown the utility as a surveillance system. We therefore analyse the trends of pneumonia during two waves of COVID-19 pandemic in order to use it as a clinical surveillance system and an early indicator of severity. Methods Time series analysis of pneumonia cases, from January 2014 to December 2020. We collected pneumonia diagnoses from primary care EHR, a software system covering > 6 million people in Catalonia (Spain). We compared the trend of pneumonia in the season 2019–2020 with that in the previous years. We estimated the expected pneumonia cases with data from 2014 to 2018 using a time series regression adjusted by seasonality and influenza epidemics. Results Between 4 March and 5 May 2020, 11,704 excess pneumonia cases (95% CI: 9909 to 13,498) were identified. Previously, we identified an excess from January to March 2020 in the population older than 15 years of 20%. We observed another excess pneumonia period from 22 october to 15 november of 1377 excess cases (95% CI: 665 to 2089). In contrast, we observed two great periods with reductions of pneumonia cases in children, accounting for 131 days and 3534 less pneumonia cases (95% CI, 1005 to 6064) from March to July; and 54 days and 1960 less pneumonia cases (95% CI 917 to 3002) from October to December. Conclusions Diagnoses of pneumonia from the EHR could be used as an early and low cost surveillance system to monitor the spread of COVID-19.

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