scholarly journals Thermosphere density response to the 20–21 November 2003 solar and geomagnetic storm from CHAMP and GRACE accelerometer data

2006 ◽  
Vol 111 (A6) ◽  
Author(s):  
Sean Bruinsma ◽  
Jeffrey M. Forbes ◽  
R. Steven Nerem ◽  
Xiaoli Zhang
Keyword(s):  
Geomagnetic Storm ◽  
Accelerometer Data ◽  
Density Response

scholarly journals Wind and temperature effects on thermosphere mass density response to the November 2004 geomagnetic storm

2010 ◽  
Vol 115 (A5) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jiuhou Lei ◽  
Jeffrey P. Thayer ◽  
Alan G. Burns ◽  
Gang Lu ◽  
Yue Deng
Keyword(s):  
Geomagnetic Storm ◽  
Temperature Effects ◽  
Mass Density ◽  
Density Response

scholarly journals Correction to “Wind and temperature effects on thermosphere mass density response to the November 2004 geomagnetic storm”

2010 ◽  
Vol 115 (A6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Jiuhou Lei ◽  
Jeffrey P. Thayer ◽  
Alan G. Burns ◽  
Gang Lu ◽  
Yue Deng
Keyword(s):  
Geomagnetic Storm ◽  
Temperature Effects ◽  
Mass Density ◽  
Density Response

What is an “Intense Geomagnetic Storm"?

Space Weather ◽  
2006 ◽  
Vol 4 (6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Yohsuke Kamide
Keyword(s):  
Geomagnetic Storm

The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation (Preprint)

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Energy Expenditure ◽  
Activity Recognition ◽  
Recognition Accuracy ◽  
Body Position ◽  
Computer Work ◽  
Accelerometer Data ◽  
Upper Arm ◽  
Lifestyle Activities

BACKGROUND Research shows the feasibility of human activity recognition using Wearable accelerometer devices. Different studies have used varying number and placement for data collection using the sensors. OBJECTIVE To compare accuracy performance between multiple and variable placement of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. METHODS Participants (n=93, 72.2±7.1 yrs) completed a total of 32 activities of daily life in a laboratory setting. Activities were classified as sedentary vs. non-sedentary, locomotion vs. non-locomotion, and lifestyle vs. non-lifestyle activities (e.g. leisure walk vs. computer work). A portable metabolic unit was worn during each activity to measure metabolic equivalents (METs). Accelerometers were placed on five different body positions: wrist, hip, ankle, upper arm, and thigh. Accelerometer data from each body position and combinations of positions were used in developing Random Forest models to assess activity category recognition accuracy and MET estimation. RESULTS Model performance for both MET estimation and activity category recognition strengthened with additional accelerometer devices. However, a single accelerometer on the ankle, upper arm, hip, thigh, or wrist had only a 0.03 to 0.09 MET increase in prediction error as compared to wearing all five devices. Balanced accuracy showed similar trends with slight decreases in balanced accuracy for detection of locomotion (0-0.01 METs), sedentary (0.13-0.05 METs) and lifestyle activities (0.08-0.04 METs) compared to all five placements. The accuracy of recognizing activity categories increased with additional placements (0.15-0.29). Notably, the hip was the best single body position for MET estimation and activity category recognition. CONCLUSIONS Additional accelerometer devices only slightly enhance activity recognition accuracy and MET estimation in older adults. However, given the extra burden of wearing additional devices, single accelerometers with appropriate placement appear to be sufficient for estimating energy expenditure and activity category recognition in older adults.

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