{"id":{"repo_id":"carleton","oai_identifier":"oai:carleton.scholaris.ca:20.500.14718/43253"},"canonical_url":"https://search.dev.ndltd.org/etd/carleton/oai:carleton.scholaris.ca:20.500.14718/43253","repository":{"repo_id":"carleton","name":"Carleton University","base_url":"https://carleton.scholaris.ca/server/oai/request"},"display":{"title":"Quaternion-Based Human Gesture Recognition System Using Multiple Body-Worn Intertial Sensors","abstract":"In this study, we designed a multi-sensor gesture recognition system that can classify among six different human gestures. Data was collected from eleven participants using five gyroscopic motion sensors tied to their upper body. A total of 1080 samples were collected, which contain almost 6000 gestures collected within a span of 90 minutes. The data were processed and fed into a multiclass Pattern Classification system to classify the gestures. We trained Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios to compare the results. A similar study was performed before using modified Hidden Markov Model but the data was collected using a single sensor. Our study indicates that near perfect classification accuracies are achievable. However, such accuracies are more difficult to obtain when a participant does not participate in training even if the test set does not contain any data from the training set.","abstract_html":"In this study, we designed a multi-sensor gesture recognition system that can classify among six different human gestures. Data was collected from eleven participants using five gyroscopic motion sensors tied to their upper body. A total of 1080 samples were collected, which contain almost 6000 gestures collected within a span of 90 minutes. The data were processed and fed into a multiclass Pattern Classification system to classify the gestures. We trained Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios to compare the results. A similar study was performed before using modified Hidden Markov Model but the data was collected using a single sensor. Our study indicates that near perfect classification accuracies are achievable. However, such accuracies are more difficult to obtain when a participant does not participate in training even if the test set does not contain any data from the training set.","abstract_has_math":false,"creators":["Alavi, Shamir"],"institution":"Carleton University","degree_name":"Master of Applied Science (M.App.Sc.)","degree_level":"Master's","degree_discipline":"Engineering, Electrical and Computer","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016","date_published":"2016","updated_at":"2026-07-24T01:34:38Z","subjects":[],"languages":["en"],"rights":["Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. 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Data was collected from eleven participants using five gyroscopic motion sensors tied to their upper body. A total of 1080 samples were collected, which contain almost 6000 gestures collected within a span of 90 minutes. The data were processed and fed into a multiclass Pattern Classification system to classify the gestures. We trained Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios to compare the results. A similar study was performed before using modified Hidden Markov Model but the data was collected using a single sensor. Our study indicates that near perfect classification accuracies are achievable. However, such accuracies are more difficult to obtain when a participant does not participate in training even if the test set does not contain any data from the training set."]},{"key":"dc:title","label":"Title","values":["Quaternion-Based Human Gesture Recognition System Using Multiple Body-Worn Intertial Sensors"]}]}],"canonical_facts":{"dc:creator":["Alavi, Shamir"],"dc:date.accessioned":["2025-04-08T20:52:44Z"],"dc:date.available":["2025-04-08T20:52:44Z"],"dc:date.issued":["2016"],"dc:description.abstract":["In this study, we designed a multi-sensor gesture recognition system that can classify among six different human gestures. Data was collected from eleven participants using five gyroscopic motion sensors tied to their upper body. A total of 1080 samples were collected, which contain almost 6000 gestures collected within a span of 90 minutes. The data were processed and fed into a multiclass Pattern Classification system to classify the gestures. We trained Support Vector Machines and Artificial Neural Networks on the same dataset under two different scenarios to compare the results. A similar study was performed before using modified Hidden Markov Model but the data was collected using a single sensor. Our study indicates that near perfect classification accuracies are achievable. However, such accuracies are more difficult to obtain when a participant does not participate in training even if the test set does not contain any data from the training set."],"dc:identifier.doi":["10.22215/etd/2016-11292"],"dc:identifier.uri":["https://hdl.handle.net/20.500.14718/43253"],"dc:language.iso":["en"],"dc:publisher":["Carleton University"],"dc:rights":["Copyright © 2016 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. 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