{"id":{"repo_id":"njit","oai_identifier":"oai:digitalcommons.njit.edu:dissertations-1613"},"canonical_url":"https://search.dev.ndltd.org/etd/njit/oai:digitalcommons.njit.edu:dissertations-1613","repository":{"repo_id":"njit","name":"NJIT","base_url":"https://digitalcommons.njit.edu/do/oai/"},"display":{"title":"Handgrip pattern recognition","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Chen, Zong"],"institution":null,"degree_name":"Doctor of Philosophy in Computing Sciences - (Ph.D.)","degree_level":null,"degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Michael Recce","James A. McHugh","Frank Y. Shih"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2003,"date_issued":"2003-01-31T08:00:00Z","date_published":"2003-01-31T08:00:00Z","updated_at":"2026-07-24T03:22:52Z","subjects":["Handgrip","Fingergrip","Pattern recognition","Biometric","Computer Sciences"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.njit.edu/dissertations/558","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Michael Recce","James A. McHugh","Frank Y. Shih"]},{"key":"dc:creator","label":"Author","values":["Chen, Zong"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy in Computing Sciences - (Ph.D.)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Handgrip","Fingergrip","Pattern recognition","Biometric","Computer Sciences"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.njit.edu/dissertations/558"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Handgrip pattern recognition"]}]}],"canonical_facts":{"dc:contributor":["Michael Recce","James A. McHugh","Frank Y. Shih"],"dc:creator":["Chen, Zong"],"dc:description.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."],"dc:identifier":["https://digitalcommons.njit.edu/dissertations/558"],"dc:subject":["Handgrip","Fingergrip","Pattern recognition","Biometric","Computer Sciences"],"dc:title":["Handgrip pattern recognition"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_name":["Doctor of Philosophy in Computing Sciences - (Ph.D.)"]},"updated_at":"2026-07-24T03:22:52Z"}