{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/121379"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/121379","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Enhancing Online Yoga Instruction: Evaluating the Effectiveness of Visual Augmentations for Performance Assessment","abstract":"Yoga is a mind-body practice known for its substantial psychological and physiological benefit, contributing to a healthy lifestyle. However, without professional guidance, individuals may experience reduced performance and increased risk of injury. While online yoga classes on platforms like Zoom have grown in popularity, tools to support instructors in accurately assessing and monitoring student performance remain insufficient. For certain populations, this lack of real-time professional guidance poses safety risks and limits the effectiveness of the practice. This study examined the effectiveness of using computer-vision-based visual augmentations in enhancing instructors' ability to assess student performance and ensure safety. Specifically, we investigated the effectiveness of various visual augmentations in aiding instructors' visual search for unstable or unsafe poses. Eleven certified yoga instructors (8 female, 3 male), each holding 200 to 500 RYT certifications, participated in the study. Instructors completed eight trials assessing 12 yoga poses using four different visual augmentations—Raw Video, Skeleton (joint locations overlay), Contour (participant outlines), and Contour + Skeleton—across two camera views (Single vs. Multiple Views). During each trial, eye-tracking data was collected as instructors identified potentially unstable (unsafe) poses, and they subsequently completed a usability questionnaire and NASA - TLX rating. Upon finishing all trials, instructors provided overall feedback on the usability of the visual augmentations and camera views Instructors showed no significant difference in their assessment performance across different visual augmentations and camera views. The Skeleton augmentation led to increased cognitive workload, as indicated by larger pupil diameters. The Contour alone augmentation was less effective for visual search based on the usability ratings, and combining Contour with Skeleton did not offer notable improvements. Simpler visualizations, such as Raw and Skeleton, received higher usability ratings, and instructors preferred Single View layouts over Multiple Views for their ease of use and lower cognitive demand. In conclusion, while Skeleton augmentation increased cognitive load, it did not significantly enhance visual search performance. Future research should explore alternative visual augmentation techniques and configurations to better assist instructors on performance assessment which increases overall performance while not substantially increasing cognitive workload.","abstract_html":"Yoga is a mind-body practice known for its substantial psychological and physiological benefit, contributing to a healthy lifestyle. However, without professional guidance, individuals may experience reduced performance and increased risk of injury. While online yoga classes on platforms like Zoom have grown in popularity, tools to support instructors in accurately assessing and monitoring student performance remain insufficient. For certain populations, this lack of real-time professional guidance poses safety risks and limits the effectiveness of the practice. This study examined the effectiveness of using computer-vision-based visual augmentations in enhancing instructors' ability to assess student performance and ensure safety. Specifically, we investigated the effectiveness of various visual augmentations in aiding instructors' visual search for unstable or unsafe poses. Eleven certified yoga instructors (8 female, 3 male), each holding 200 to 500 RYT certifications, participated in the study. Instructors completed eight trials assessing 12 yoga poses using four different visual augmentations—Raw Video, Skeleton (joint locations overlay), Contour (participant outlines), and Contour + Skeleton—across two camera views (Single vs. Multiple Views). During each trial, eye-tracking data was collected as instructors identified potentially unstable (unsafe) poses, and they subsequently completed a usability questionnaire and NASA - TLX rating. Upon finishing all trials, instructors provided overall feedback on the usability of the visual augmentations and camera views Instructors showed no significant difference in their assessment performance across different visual augmentations and camera views. The Skeleton augmentation led to increased cognitive workload, as indicated by larger pupil diameters. The Contour alone augmentation was less effective for visual search based on the usability ratings, and combining Contour with Skeleton did not offer notable improvements. Simpler visualizations, such as Raw and Skeleton, received higher usability ratings, and instructors preferred Single View layouts over Multiple Views for their ease of use and lower cognitive demand. In conclusion, while Skeleton augmentation increased cognitive load, it did not significantly enhance visual search performance. Future research should explore alternative visual augmentation techniques and configurations to better assist instructors on performance assessment which increases overall performance while not substantially increasing cognitive workload.","abstract_has_math":false,"creators":["Gopal, Ajit Ayyadurai"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Industrial and Systems Engineering","degree_department":"Industrial and Systems Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Lim, Sol Ie"],"committee_members":["Harden, Samantha M.","Lau, Nathan Ka Ching"],"year":2024,"date_issued":"2024-10-23","date_published":"2024-10-23","updated_at":"2026-07-22T22:19:41Z","subjects":["Online yoga","computer vision","pose estimation","yoga instruction"],"languages":["en"],"rights":["Creative Commons Attribution 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:41678"],"render_values":[{"text":"vt_gsexam:41678","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/121379","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Lim, Sol Ie"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Harden, Samantha M.","Lau, Nathan Ka Ching"]},{"key":"dc:contributor.department","label":"Department","values":["Industrial and Systems Engineering"]},{"key":"dc:creator","label":"Author","values":["Gopal, Ajit Ayyadurai"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-10-24T08:00:10Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-10-24T08:00:10Z"]},{"key":"dc:date.issued","label":"Date","values":["2024-10-23"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Systems Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Online yoga","computer vision","pose estimation","yoga instruction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:41678"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/121379"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Yoga is a mind-body practice known for its substantial psychological and physiological benefit, contributing to a healthy lifestyle. However, without professional guidance, individuals may experience reduced performance and increased risk of injury. While online yoga classes on platforms like Zoom have grown in popularity, tools to support instructors in accurately assessing and monitoring student performance remain insufficient. For certain populations, this lack of real-time professional guidance poses safety risks and limits the effectiveness of the practice. This study examined the effectiveness of using computer-vision-based visual augmentations in enhancing instructors' ability to assess student performance and ensure safety. Specifically, we investigated the effectiveness of various visual augmentations in aiding instructors' visual search for unstable or unsafe poses. Eleven certified yoga instructors (8 female, 3 male), each holding 200 to 500 RYT certifications, participated in the study. Instructors completed eight trials assessing 12 yoga poses using four different visual augmentations—Raw Video, Skeleton (joint locations overlay), Contour (participant outlines), and Contour + Skeleton—across two camera views (Single vs. Multiple Views). During each trial, eye-tracking data was collected as instructors identified potentially unstable (unsafe) poses, and they subsequently completed a usability questionnaire and NASA - TLX rating. Upon finishing all trials, instructors provided overall feedback on the usability of the visual augmentations and camera views Instructors showed no significant difference in their assessment performance across different visual augmentations and camera views. The Skeleton augmentation led to increased cognitive workload, as indicated by larger pupil diameters. The Contour alone augmentation was less effective for visual search based on the usability ratings, and combining Contour with Skeleton did not offer notable improvements. Simpler visualizations, such as Raw and Skeleton, received higher usability ratings, and instructors preferred Single View layouts over Multiple Views for their ease of use and lower cognitive demand. In conclusion, while Skeleton augmentation increased cognitive load, it did not significantly enhance visual search performance. Future research should explore alternative visual augmentation techniques and configurations to better assist instructors on performance assessment which increases overall performance while not substantially increasing cognitive workload."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Yoga is a great way to improve both mental and physical health. However, practicing yoga without proper guidance can sometimes lead to injuries or mistakes. With more people attending yoga classes online, like through Zoom, it's harder for instructors to closely monitor how their students are performing, which can reduce the safety and benefits of the practice. This study looked at whether certain computer tools could help instructors better see and correct their students' poses during online yoga classes. Eleven experienced yoga instructors tried out different visual aids while watching students perform yoga poses. These aids included a simple video, a video with lines showing where the students' joints were (called Skeleton), a video that showed just the outline of the student (Contour), and a mix of both (Contour + Skeleton). The instructors were asked to identify any unstable or unsafe poses while using these aids. The results showed that none of the visual aids helped the instructors spot mistakes better than regular video. While the Skeleton aid made the instructors work harder mentally, it didn't actually help them perform better. The instructors preferred using simple video over the more complex tools and found that using a single camera view was easier to work with. In short, more complex visual tools didn't help instructors improve their performance. Future studies should explore other ways, like using different camera angles or adding sound, to help instructors in online yoga classes."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Enhancing Online Yoga Instruction: Evaluating the Effectiveness of Visual Augmentations for Performance Assessment"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Lim, Sol Ie"],"dc:contributor.committeemember":["Harden, Samantha M.","Lau, Nathan Ka Ching"],"dc:contributor.department":["Industrial and Systems Engineering"],"dc:creator":["Gopal, Ajit Ayyadurai"],"dc:date.accessioned":["2024-10-24T08:00:10Z"],"dc:date.available":["2024-10-24T08:00:10Z"],"dc:date.issued":["2024-10-23"],"dc:description.abstract":["Yoga is a mind-body practice known for its substantial psychological and physiological benefit, contributing to a healthy lifestyle. However, without professional guidance, individuals may experience reduced performance and increased risk of injury. While online yoga classes on platforms like Zoom have grown in popularity, tools to support instructors in accurately assessing and monitoring student performance remain insufficient. For certain populations, this lack of real-time professional guidance poses safety risks and limits the effectiveness of the practice. This study examined the effectiveness of using computer-vision-based visual augmentations in enhancing instructors' ability to assess student performance and ensure safety. Specifically, we investigated the effectiveness of various visual augmentations in aiding instructors' visual search for unstable or unsafe poses. Eleven certified yoga instructors (8 female, 3 male), each holding 200 to 500 RYT certifications, participated in the study. Instructors completed eight trials assessing 12 yoga poses using four different visual augmentations—Raw Video, Skeleton (joint locations overlay), Contour (participant outlines), and Contour + Skeleton—across two camera views (Single vs. Multiple Views). During each trial, eye-tracking data was collected as instructors identified potentially unstable (unsafe) poses, and they subsequently completed a usability questionnaire and NASA - TLX rating. Upon finishing all trials, instructors provided overall feedback on the usability of the visual augmentations and camera views Instructors showed no significant difference in their assessment performance across different visual augmentations and camera views. The Skeleton augmentation led to increased cognitive workload, as indicated by larger pupil diameters. The Contour alone augmentation was less effective for visual search based on the usability ratings, and combining Contour with Skeleton did not offer notable improvements. Simpler visualizations, such as Raw and Skeleton, received higher usability ratings, and instructors preferred Single View layouts over Multiple Views for their ease of use and lower cognitive demand. In conclusion, while Skeleton augmentation increased cognitive load, it did not significantly enhance visual search performance. Future research should explore alternative visual augmentation techniques and configurations to better assist instructors on performance assessment which increases overall performance while not substantially increasing cognitive workload."],"dc:description.abstractgeneral":["Yoga is a great way to improve both mental and physical health. However, practicing yoga without proper guidance can sometimes lead to injuries or mistakes. With more people attending yoga classes online, like through Zoom, it's harder for instructors to closely monitor how their students are performing, which can reduce the safety and benefits of the practice. This study looked at whether certain computer tools could help instructors better see and correct their students' poses during online yoga classes. Eleven experienced yoga instructors tried out different visual aids while watching students perform yoga poses. These aids included a simple video, a video with lines showing where the students' joints were (called Skeleton), a video that showed just the outline of the student (Contour), and a mix of both (Contour + Skeleton). The instructors were asked to identify any unstable or unsafe poses while using these aids. The results showed that none of the visual aids helped the instructors spot mistakes better than regular video. While the Skeleton aid made the instructors work harder mentally, it didn't actually help them perform better. The instructors preferred using simple video over the more complex tools and found that using a single camera view was easier to work with. In short, more complex visual tools didn't help instructors improve their performance. Future studies should explore other ways, like using different camera angles or adding sound, to help instructors in online yoga classes."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:41678"],"dc:identifier.uri":["https://hdl.handle.net/10919/121379"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Online yoga","computer vision","pose estimation","yoga instruction"],"dc:title":["Enhancing Online Yoga Instruction: Evaluating the Effectiveness of Visual Augmentations for Performance Assessment"],"dc:type":["Thesis"],"thesis:degree_discipline":["Industrial and Systems Engineering"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:41Z"}