Smart Driving: Influence of Context and Behavioral Data on Driving Style

2016 ◽  
pp. 141-151 ◽  
Author(s):  
Mikhail Sysoev ◽  
Andrej Kos ◽  
Matevž Pogačnik
Keyword(s):  
Behavioral Data ◽  
Driving Style

Development of Reading Remediation for Dyslexic Individuals

2016 ◽  
Vol 224 (4) ◽  
pp. 240-246 ◽  
Author(s):  
Mélanie Bédard ◽  
Line Laplante ◽  
Julien Mercier
Keyword(s):  
20Th Century ◽  
Scientific Literature ◽  
Behavioral Data ◽  
Educational Neuroscience ◽  
Reading Remediation ◽  
Functional Brain ◽  
Neurophysiological Data ◽  
Brain Correlates ◽  
Research Questions ◽  
The Brain

Abstract. Dyslexia is a phenomenon for which the brain correlates have been studied since the beginning of the 20th century. Simultaneously, the field of education has also been studying dyslexia and its remediation, mainly through behavioral data. The last two decades have seen a growing interest in integrating neuroscience and education. This article provides a quick overview of pertinent scientific literature involving neurophysiological data on functional brain differences in dyslexia and discusses their very limited influence on the development of reading remediation for dyslexic individuals. Nevertheless, it appears that if certain conditions are met – related to the key elements of educational neuroscience and to the nature of the research questions – conceivable benefits can be expected from the integration of neurophysiological data with educational research. When neurophysiological data can be employed to overcome the limits of using behavioral data alone, researchers can both unravel phenomenon otherwise impossible to document and raise new questions.

Behavioral data in the design of aircrew training devices

1975 ◽  
Author(s):  
B. W. Cream ◽  
F. T. Eggemeier ◽  
G. A. Klein
Keyword(s):  
Behavioral Data

Driving Style Influence on Vehicles Performances

2018 ◽  
Vol 1 (1) ◽  
pp. 39-42
Author(s):  
Laszlo Barothi ◽  
◽  
Daniel Sava ◽  
Cătălin-Dumitru Darie ◽  
Leonard-Iulian Cucu ◽  
...  
Keyword(s):  
Driving Style

Privacy, confidentiality, and safety considerations for conducting geographic momentary assessment studies among persons who use drugs and men who have sex with men (Preprint)

2018 ◽  
Author(s):  
Abby Rudolph ◽  
April Young ◽  
Jennifer Havens
Keyword(s):  
Risk Behaviors ◽  
Response Times ◽  
Ecological Validity ◽  
Qualitative Interviews ◽  
Behavioral Data ◽  
Atlantic City ◽  
Sensitive Information ◽  
Safety Concerns ◽  
Sex With Men ◽  
Persons Who Use Drugs

BACKGROUND Geographic momentary assessments (GMA) collect real-time behavioral data in one’s natural environment using a smartphone and could potentially increase the ecological validity of behavioral data. Several studies have evaluated the feasibility and acceptability of GMA among persons who use drugs (PWUD) and men who have sex with men (MSM), but fewer have discussed privacy, confidentiality, and safety concerns, particularly when illegal or stigmatized behavioral data were collected. OBJECTIVE This study explores perceptions regarding privacy, confidentiality, and safety of GMA research among PWUD and MSM recruited in three different settings (rural Appalachia, a mid-sized city in the South, and a mid-Atlantic city). METHODS Between November 2014 and April 2017 we recruited 35 PWUD from rural Appalachian Kentucky (N=20) and Baltimore, Maryland (N=15), and 20 MSM from Lexington, Kentucky to complete semi-structured qualitative interviews. Through thematic analyses, we identified and compared privacy, confidentiality, and safety concerns by demographic characteristics, risk behaviors, and setting. RESULTS Privacy, confidentiality, and safety concerns varied by setting, age, smartphone ownership, use of illegal drugs, and history of drug-related arrests. Among those who used drugs, participants reported concerns with being tracked and burden associated with carrying and safeguarding study phones and responding to survey prompts. Privacy and confidentiality concerns were noted in each setting, but tracking concerns were greatest among Baltimore participants and led many to feel that they (or others) would be unwilling to participate or comply with study procedures. While locations considered to be sensitive varied by setting, participants in all settings said they would take measures to prevent sensitive information from being collected (i.e. intentionally disable devices, leave phones at home, alter response times). CONCLUSIONS Privacy, confidentiality, and safety concerns may limit the accuracy of risk location information, study compliance, and participation. As concerns were often greatest among those engaging in illegal behaviors and with the highest risk behaviors, selection bias and non-response bias could negatively influence the representativeness and validity of study findings.

scholarly journals Appropriate Drone Flight Altitude for Horse Behavioral Observation

Drones ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 71
Author(s):  
Tomoko Saitoh ◽  
Moyu Kobayashi
Keyword(s):  
Avoidance Behavior ◽  
Correlation Coefficients ◽  
Behavioral Observation ◽  
Animal Research ◽  
Behavioral Data ◽  
Flight Altitude ◽  
Recording Time ◽  
Livestock Management ◽  
Behavioral Observations ◽  
Video Observations

Recently, drone technology advanced, and its safety and operability markedly improved, leading to its increased application in animal research. This study demonstrated drone application in livestock management, using its technology to observe horse behavior and verify the appropriate horse–drone distance for aerial behavioral observations. Recordings were conducted from September to October 2017 on 11 horses using the Phantom 4 Pro drone. Four flight altitudes were tested (60, 50, 40, and 30 m) to investigate the reactions of the horses to the drones and observe their behavior; the recording time at each altitude was 5 min. None of the horses displayed avoidance behavior at any flight altitude, and the observer was able to distinguish between any two horses. Recorded behaviors were foraging, moving, standing, recumbency, avoidance, and others. Foraging was the most common behavior observed both directly and in the drone videos. The correlation coefficients of all behavioral data from direct and drone video observations at all altitudes were significant (p < 0.01). These results indicate that horse behavior can be discerned with equal accuracy by both direct and recorded drone video observations. In conclusion, drones can be useful for recording and analyzing horse behavior.

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

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