{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/100299"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/100299","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Marking Time : increased scope and accuracy for sketch classification of the clock drawing test","abstract":"In this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance.","abstract_html":"In this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance.","abstract_has_math":false,"creators":["Chien, Jonathan"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.","school":null,"contributors":[],"advisors":["Randall Davis."],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-22T22:20:48Z","subjects":["Electrical Engineering and Computer Science."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/100299","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Randall Davis."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:contributor.other","label":"Dc Contributor Other","values":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."]},{"key":"dc:creator","label":"Author","values":["Chien, Jonathan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2015-12-16T15:54:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2015-12-16T15:54:17Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:publisher","label":"Institution","values":["Massachusetts Institute of Technology"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Electrical Engineering and Computer Science."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://dspace.mit.edu/handle/1721.1/7582"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1721.1/100299"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 85-86)."]},{"key":"dc:description.abstract","label":"Abstract","values":["In this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M. Eng."]},{"key":"dc:title","label":"Title","values":["Marking Time : increased scope and accuracy for sketch classification of the clock drawing test"]}]}],"canonical_facts":{"dc:contributor.advisor":["Randall Davis."],"dc:contributor.department":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:contributor.other":["Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science."],"dc:creator":["Chien, Jonathan"],"dc:date.accessioned":["2015-12-16T15:54:17Z"],"dc:date.available":["2015-12-16T15:54:17Z"],"dc:date.issued":["2015"],"dc:description":["Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.","This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.","Cataloged from student-submitted PDF version of thesis.","Includes bibliographical references (pages 85-86)."],"dc:description.abstract":["In this thesis, I designed and implemented improvements to an automatic classifier for the digitized clock drawing test, a diagnostic tool for assessing cognitive impairment, which asks the patient to draw an analog clock face using a digital pen. The classifier handles both the grouping of strokes into clock components and the subsequent labeling of those groups. Despite the domain-specificity, classification is a challenging problem because the subject often has cognitive or motor impairments. It is thus important for the classifier to be able to handle a wide range of input with distorted, overwritten, or missing components. I improve the robustness of the classifier, particularly for messy clinical data, by incorporating intrinsic stroke properties, developing additional symbol recognizers, and creating a global context evaluator. I describe in this thesis properties for isolated symbol recognition, features for symbol recognition and match scoring, as well as common sense rules based on a symbol's local and global context in a drawing. I combine these elements into a new system that locally maximizes a global label assignment based on match quality and context. I demonstrate that this system accurately recognizes a wide variety of clinical input, improving overall classification performance."],"dc:description.degree":["M. Eng."],"dc:identifier.uri":["http://hdl.handle.net/1721.1/100299"],"dc:language.iso":["eng"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"dc:rights.uri":["http://dspace.mit.edu/handle/1721.1/7582"],"dc:subject":["Electrical Engineering and Computer Science."],"dc:title":["Marking Time : increased scope and accuracy for sketch classification of the clock drawing test"],"dc:type":["Thesis"]},"updated_at":"2026-07-22T22:20:48Z"}