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

2007 ◽  
Author(s):  
Nathan A. Rose ◽  
Gray Beauchamp ◽  
Will Bortles
Keyword(s):  
Coefficient Of Restitution ◽  
Crash Test ◽  
Accelerometer Data

Influence of braking force in low-speed vehicle collisions

2005 ◽  
Vol 219 (2) ◽  
pp. 151-164 ◽  
Author(s):  
M Mastandrea ◽  
D Vangi
Keyword(s):  
Experimental Data ◽  
Rolling Resistance ◽  
Great Accuracy ◽  
Coefficient Of Restitution ◽  
Impact Dynamics ◽  
Crash Test ◽  
Separate Analysis ◽  
Low Speed ◽  
Braking Force ◽  
The Impact

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.

New crash test model

Nature ◽  
2001 ◽  
Author(s):  
Philip Ball
Keyword(s):  
Crash Test ◽  
Test Model

Numerical simulation of crash test for bridge safety barrier

2014 ◽  
Vol 102 (41) ◽  
pp. 301-308
Author(s):  
Jiri Drozda ◽  
Jan Marek ◽  
Tomas Rotter
Keyword(s):  
Numerical Simulation ◽  
Crash Test ◽  
Bridge Safety ◽  
Safety Barrier

Comparative Test on Performance of Golf Ball with Different Coefficient of Restitution and Ball Compression

2018 ◽  
Vol 27 (4) ◽  
pp. 1049-1057
Author(s):  
Kwang-Hyeuk Kim ◽  
Jae-Won Choi ◽  
In-Cheol Kang ◽  
Jae-Kil Han
Keyword(s):  
Coefficient Of Restitution ◽  
Comparative Test ◽  
Golf Ball

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.

Sign in / Sign up

Export Citation Format

Share Document