A Study of Feature Extraction of Individual Characteristics in Driving Behavior From Accelerometer Data.

2017 ◽  
Vol 2017 (0) ◽  
pp. 2P1-N08
Author(s):  
Kazumu KOBAYASHI ◽  
Takuma AKIDUKI ◽  
Zhong ZHANG
Keyword(s):  
Feature Extraction ◽  
Driving Behavior ◽  
Individual Characteristics ◽  
Accelerometer Data

scholarly journals Associations between time spent in green areas and physical activity among late middle-aged adults

2016 ◽  
Vol 11 (3) ◽  
Author(s):  
Bart Dewulf ◽  
Tijs Neutens ◽  
Delfien Van Dyck ◽  
Ilse De Bourdeaudhuij ◽  
Steven Broekx ◽  
...  
Keyword(s):  
Physical Activity ◽  
Global Positioning System ◽  
Individual Characteristics ◽  
Positioning System ◽  
Accelerometer Data ◽  
Middle Aged ◽  
Physically Active ◽  
Global Positioning ◽  
Green Areas ◽  
Middle Aged Adults

Physical activity is an important facilitator for health and wellbeing, especially for late middle-aged adults, who are more susceptible to cardiovascular diseases. Physical activity performed in green areas is supposed to be particularly beneficial, so we studied whether late middle- aged adults are more active in green areas than in non-green areas and how this is influenced by individual characteristics and the level of neighbourhood greenness. We tracked 180 late middle-aged (58 to 65 years) adults using global positioning system and accelerometer data to know whether and where they were sedentary or active. These data were combined with information on land use to obtain information on the greenness of sedentary and active hotspots. We found that late middle-aged adults are more physically active when spending more time in green areas than in non-green areas. Spending more time at home and in non-green areas was found to be associated with more sedentary behaviour. Time spent in non-green areas was found to be related to more moderate-to-vigorous physical activity (MVPA) for males and to less MVPA for females. The positive association between time spent in green areas and MVPA was the strongest for highly educated people and for those living in a green neighbourhood. This study shows that the combined use of global positioning system and accelerometer data facilitates understanding of where people are sedentary or physically active, which can help policy makers encourage activity in this age cohort.

scholarly journals A Comparison of Feature Extraction Methods for the Classification of Dynamic Activities From Accelerometer Data

2009 ◽  
Vol 56 (3) ◽  
pp. 871-879 ◽  
Author(s):  
Stephen J. Preece ◽  
John Yannis Goulermas ◽  
Laurence P. J. Kenney ◽  
David Howard
Keyword(s):  
Feature Extraction ◽  
Extraction Methods ◽  
Accelerometer Data

1P1-G05 Investigation of Individual Characteristics in Human Motion by using Accelerometer Data

2015 ◽  
Vol 2015 (0) ◽  
pp. _1P1-G05_1-_1P1-G05_4
Author(s):  
Ikumi KAMIO ◽  
Hirotaka TAKAHASHI ◽  
Takuma AKIDUKI ◽  
Natsuki NAKADA ◽  
Zhong ZHANG
Keyword(s):  
Human Motion ◽  
Individual Characteristics ◽  
Accelerometer Data

Relationship between Individual Characteristics and Driving Behavior of Novice Driver

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Min Duan ◽  
Xujia Liang ◽  
Xiaobing Wei ◽  
Lian Xie
Keyword(s):  
Driving Behavior ◽  
Individual Characteristics ◽  
Novice Driver

A Study of Feature Extraction for Individual Characteristics of Drivers’ Body Motion

2019 ◽  
Vol 2019 (0) ◽  
pp. 2P1-N10
Author(s):  
Takuma Akiduki ◽  
Kazumu Kobayashi ◽  
Zhong Zhang ◽  
Tomoaki Mashimo ◽  
Hirotaka Takahashi
Keyword(s):  
Feature Extraction ◽  
Individual Characteristics ◽  
Body Motion

Multi-Source Feature Extraction and Visualization for Driving Behavior Analysis

2019 ◽  
Author(s):  
Guoliang Yuan ◽  
Yafei Wang ◽  
Yuxiao Yan ◽  
Tianyi Shen ◽  
Weitao Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Behavior Analysis ◽  
Driving Behavior

scholarly journals Feature Extraction and Random Forest to Identify Sheep Behavior from Accelerometer Data

2020 ◽  
pp. 408-419
Author(s):  
Natasa Kleanthous ◽  
Abir Hussain ◽  
Wasiq Khan ◽  
Jenny Sneddon ◽  
Alex Mason
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Accelerometer Data
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