{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/71101"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/71101","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Multi-modal emotion detection using deep learning for interpersonal communication analytics","abstract":"In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. Communication skills, either in a small group learning environment or a large group setting, are always useful in any future workplace. In this study, we aim to analyze mutual communication and interactions between speakers/audiences from a broader perspective, including emotional and cognitive interactions, in TED talk or classroom settings. We are mainly interested in the recognition of facial and gesture emotions captured in such contexts. More specifically, we proposed a multi-modal emotion detection approach for facial expression, e.g., facial sentiment, gender, age, ethnicity, hairstyles, as well as gesture expression, e.g., sitting, standing, raising their hands, folded hands and crossed legs. The real-time feedback of the proposed system on individual or group communication can effectively be used for improving their communication skills.","abstract_html":"In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. Communication skills, either in a small group learning environment or a large group setting, are always useful in any future workplace. In this study, we aim to analyze mutual communication and interactions between speakers/audiences from a broader perspective, including emotional and cognitive interactions, in TED talk or classroom settings. We are mainly interested in the recognition of facial and gesture emotions captured in such contexts. More specifically, we proposed a multi-modal emotion detection approach for facial expression, e.g., facial sentiment, gender, age, ethnicity, hairstyles, as well as gesture expression, e.g., sitting, standing, raising their hands, folded hands and crossed legs. The real-time feedback of the proposed system on individual or group communication can effectively be used for improving their communication skills.","abstract_has_math":false,"creators":["Gogadi, Sravanthi"],"institution":"University of Missouri--Kansas City","degree_name":"M.S. (Master of Science)","degree_level":"M.S.","degree_discipline":"Computer Science (UMKC)","degree_department":null,"school":null,"contributors":[],"advisors":["Lee, Yugyung, 1960-"],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019","date_published":"2019","updated_at":"2026-07-24T05:17:53Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/10355/71101","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Lee, Yugyung, 1960-"]},{"key":"dc:creator","label":"Author","values":["Gogadi, Sravanthi"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2020-01-22T23:01:51Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2020-01-22T23:01:51Z"]},{"key":"dc:date.issued","label":"Date","values":["2019"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science (UMKC)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["M.S.","Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S. (Master of Science)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Missouri--Kansas City"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10355/71101"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Title from PDF of title page viewed January 28, 2020","Thesis advisor: Yugyung Lee","Vita","Includes bibliographical references (page 41-43)","Thesis (M.S.)--School of Computing and Engineering, University of Missouri--Kansas City, 2019"]},{"key":"dc:description.abstract","label":"Abstract","values":["In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. Communication skills, either in a small group learning environment or a large group setting, are always useful in any future workplace. In this study, we aim to analyze mutual communication and interactions between speakers/audiences from a broader perspective, including emotional and cognitive interactions, in TED talk or classroom settings. We are mainly interested in the recognition of facial and gesture emotions captured in such contexts. More specifically, we proposed a multi-modal emotion detection approach for facial expression, e.g., facial sentiment, gender, age, ethnicity, hairstyles, as well as gesture expression, e.g., sitting, standing, raising their hands, folded hands and crossed legs. The real-time feedback of the proposed system on individual or group communication can effectively be used for improving their communication skills."]},{"key":"dc:title","label":"Title","values":["Multi-modal emotion detection using deep learning for interpersonal communication analytics"]}]}],"canonical_facts":{"dc:contributor.advisor":["Lee, Yugyung, 1960-"],"dc:creator":["Gogadi, Sravanthi"],"dc:date.accessioned":["2020-01-22T23:01:51Z"],"dc:date.available":["2020-01-22T23:01:51Z"],"dc:date.issued":["2019"],"dc:description":["Title from PDF of title page viewed January 28, 2020","Thesis advisor: Yugyung Lee","Vita","Includes bibliographical references (page 41-43)","Thesis (M.S.)--School of Computing and Engineering, University of Missouri--Kansas City, 2019"],"dc:description.abstract":["In recent years, deep learning technologies have been increasingly applied to generate meaningful data for advanced research in humanities and sciences. Interpersonal communication skills are crucial to success in science. Communication skills, either in a small group learning environment or a large group setting, are always useful in any future workplace. In this study, we aim to analyze mutual communication and interactions between speakers/audiences from a broader perspective, including emotional and cognitive interactions, in TED talk or classroom settings. We are mainly interested in the recognition of facial and gesture emotions captured in such contexts. More specifically, we proposed a multi-modal emotion detection approach for facial expression, e.g., facial sentiment, gender, age, ethnicity, hairstyles, as well as gesture expression, e.g., sitting, standing, raising their hands, folded hands and crossed legs. The real-time feedback of the proposed system on individual or group communication can effectively be used for improving their communication skills."],"dc:identifier.uri":["https://hdl.handle.net/10355/71101"],"dc:title":["Multi-modal emotion detection using deep learning for interpersonal communication analytics"],"thesis:degree_discipline":["Computer Science (UMKC)"],"thesis:degree_level":["M.S.","Masters"],"thesis:degree_name":["M.S. (Master of Science)"],"thesis:institution_name":["University of Missouri--Kansas City"]},"updated_at":"2026-07-24T05:17:53Z"}