Document Type
Dissertation
Date of Award
Fall 1-31-2003
Degree Name
Doctor of Philosophy in Computing Sciences - (Ph.D.)
Department
Computer Science
First Advisor
Michael Recce
Second Advisor
James A. McHugh
Third Advisor
Frank Y. Shih
Fourth Advisor
Timothy Nam Chang
Fifth Advisor
Michael Beres
Abstract
There are numerous tragic gun deaths each year. Making handguns safer by personalizing them could prevent most such tragedies. Personalized handguns, also called "smart" guns, are handguns that can only be fired by the authorized user. Handgrip pattern recognition holds great promise in the development of the smart gun.
Two algorithms, static analysis algorithm and dynamic analysis algorithm, were developed to find the patterns of a person about how to grasp a handgun. The static analysis algorithm measured 160 subjects' fingertip placements on the replica gun handle. The cluster analysis and discriminant analysis were applied to these fingertip placements, and a classification tree was built to find the fingertip pattern for each subject.
The dynamic analysis algorithm collected and measured 24 subjects' handgrip pressure waveforms during the trigger pulling stage. A handgrip recognition algorithm was developed to find the correct pattern. A DSP box was built to make the handgrip pattern recognition to be done in real time. A real gun was used to evaluate the handgrip recognition algorithm. The result was shown and it proves that such a handgrip recognition system works well as a prototype.
Recommended Citation
Chen, Zong, "Handgrip pattern recognition" (2003). Dissertations. 558.
https://digitalcommons.njit.edu/dissertations/558
