Enhancing the Quality of Infrared-Based Automatic Pedestrian Sensor Data by Nonparametric Statistical Method

2011 ◽  
Vol 2264 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Hong Yang ◽  
Kaan Ozbay ◽  
Bekir Bartin
Keyword(s):  
Statistical Method ◽  
Sensor Data ◽  
Nonparametric Statistical ◽  
Nonparametric Statistical Method

STATISTICAL APPROACH TO MODELING OF SPATIOTEMPORAL DYNAMICS

2001 ◽  
Vol 11 (11) ◽  
pp. 2731-2738 ◽  
Author(s):  
SIMON MANDELJ ◽  
IGOR GRABEC ◽  
EDVARD GOVEKAR
Keyword(s):  
System Dynamics ◽  
Statistical Method ◽  
Spatiotemporal Dynamics ◽  
State Variable ◽  
Local Modeling ◽  
Spatially Extended ◽  
Recorded Data ◽  
Nonparametric Statistical ◽  
Nonparametric Statistical Method ◽  
Extended Dynamic

Often in the analysis of spatially extended dynamic systems, we do not know an analytical model of the system dynamics, but we can provide spatiotemporal records of the characteristic state variable. The question then arises of how to extract a model of the system dynamics from the corresponding data. As a quite general solution of this problem, we propose a nonparametric statistical method of local modeling. The performance of the proposed method is demonstrated by predicting typical examples of spatiotemporal chaotic data. The results of modeling indicate that the statistical method can be applied to modeling the deterministic properties of spatiotemporal dynamics in terms of recorded data.

Income and Life Quality of Farmer Households Suffering Social Exclusion

2012 ◽  
pp. 63-87
Author(s):  
Anh Mai Ngoc ◽  
Ha Do Thi Hai ◽  
Huyen Nguyen Thi Ngoc
Keyword(s):  
Labor Market ◽  
Statistical Method ◽  
Social Exclusion ◽  
Central Government ◽  
Life Quality ◽  
Economic Aspect ◽  
Local Authorities ◽  
The Impact

This study uses descriptive statistical method to analyze the income and life qual- ity of 397 farmer households who are suffering social exclusion in an economic aspect out of a total of 725 households surveyed in five Northern provinces of Vietnam in 2010. The farmers’ opinions of the impact of the policies currently prac- ticed by the central government and local authorities to give them access to the labor market are also analyzed in this study to help management officers see how the poli- cies affect the beneficiaries so that they can later make appropriate adjustments.

Measuring patient engagement among cancer patients in eHealth and mHealth interventions on symptom management: an integrated review (Preprint)

2020 ◽  
Author(s):  
Juqing Zhao ◽  
Pei Chen ◽  
Guangming Wan
Keyword(s):  
Cancer Patients ◽  
Patient Engagement ◽  
Symptom Management ◽  
Subjective Experience ◽  
Participation Rate ◽  
Sensor Data ◽  
Quality Of Data ◽  
Mhealth Intervention

BACKGROUND There has been an increase number of eHealth and mHealth interventions aimed to support symptoms among cancer survivors. However, patient engagement has not been guaranteed and standardized in these interventions. OBJECTIVE The objective of this review was to address how patient engagement has been defined and measured in eHealth and mHealth interventions designed to improve symptoms and quality of life for cancer patients. METHODS Searches were performed in MEDLINE, PsychINFO, Web of Science, and Google Scholar to identify eHealth and mHealth interventions designed specifically to improve symptom management for cancer patients. Definition and measurement of engagement and engagement related outcomes of each intervention were synthesized. This integrated review was conducted using Critical Interpretive Synthesis to ensure the quality of data synthesis. RESULTS A total of 792 intervention studies were identified through the searches; 10 research papers met the inclusion criteria. Most of them (6/10) were randomized trial, 2 were one group trail, 1 was qualitative design, and 1 paper used mixed method. Majority of identified papers defined patient engagement as the usage of an eHealth and mHealth intervention by using different variables (e.g., usage time, log in times, participation rate). Engagement has also been described as subjective experience about the interaction with the intervention. The measurement of engagement is in accordance with the definition of engagement and can be categorized as objective and subjective measures. Among identified papers, 5 used system usage data, 2 used self-reported questionnaire, 1 used sensor data and 3 used qualitative method. Almost all studies reported engagement at a moment to moment level, but there is a lack of measurement of engagement for the long term. CONCLUSIONS There have been calls to develop standard definition and measurement of patient engagement in eHealth and mHealth interventions. Besides, it is important to provide cancer patients with more tailored and engaging eHealth and mHealth interventions for long term engagement.

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