Quantifying the Uncertainty in the Coefficient of Restitution Obtained with Accelerometer Data from a Crash Test

Author(s):  
Nathan A. Rose ◽  
Gray Beauchamp ◽  
Will Bortles
Author(s):  
M Mastandrea ◽  
D Vangi

The aim of this research is to find out the influence of braking force on the impact dynamics in low-speed rear-end collisions. The method used to simulate the crash is a step-by-step impulsive model including tyre friction and the rolling resistance effect. The simulation allows a separate analysis of the compression and restitution phases. The first is based upon experimental data of the crash test between the vehicles, the latter leads to two different choices of reconstruction, considering the bumper rebound or neglecting it. The coefficient of restitution is calculated by the force reached at compression ending, thus including the influence of external forces (as friction). The results show a great accuracy and matching with experimental data. The main changes in the impact dynamics due to the rise in braking force are the enlargement of the crush with a delay of the restitution start and a lowering of the coefficient of restitution, the increase of the impact duration and the decrease of the velocities at the collision end. These effects depend on the closing velocity and are more marked at low speed. This study therefore demonstrates that the braking force is not negligible in a good reconstruction of impact dynamics at low speed.


Nature ◽  
2001 ◽  
Author(s):  
Philip Ball
Keyword(s):  

2014 ◽  
Vol 102 (41) ◽  
pp. 301-308
Author(s):  
Jiri Drozda ◽  
Jan Marek ◽  
Tomas Rotter

2018 ◽  
Vol 27 (4) ◽  
pp. 1049-1057
Author(s):  
Kwang-Hyeuk Kim ◽  
Jae-Won Choi ◽  
In-Cheol Kang ◽  
Jae-Kil Han

2020 ◽  
Author(s):  
Anis Davoudi ◽  
Mamoun T. Mardini ◽  
Dave Nelson ◽  
Fahd Albinali ◽  
Sanjay Ranka ◽  
...  

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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