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ZoombaTogether: A Video Conference Add-on for Generating Interactive Visual Feedback for Online Group Exercise Through On-The-Fly Pose Tracking

Qingxiaoyang Zhu, Xiaoyu Zhang, Shuyan Dai, Noriko Satake, Hao-Chuan Wang

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

Abstract

Exercise benefits people not only in physical health but also in mental health. Compared to individual workouts, many people prefer group exercise because of the additional peer support like guidance from experienced peers, and socializing benefits like the opportunities to meet new friends when exercising with others as a group. However, during the pandemic, many group exercise sessions were shifted from in-person to online through video conferencing platforms. Such change significantly decreased the exercise feedback and social signals that participants could receive during the exercise, such as the immediate self-checking with a studio mirror, the group movement calibration with co-exercisers, and the sense of social presence (i.e., the existence of other co-exercisers) in the small community. To address these challenges, we present ZoombaTogether, a video platform add-on that enhances the remote Zumba dancing experience with on-the-fly pose detection and visualization techniques. It tracks and evaluates how well remote dancers follow the instructor with minor latency and provide them with visual feedback to simulate a joint exercise experience with group members. ZoombaTogether visualizes the participants' performance at both individual and group levels to help them understand their movement similarity to the instructor and other dancers with a set of extensible visualization modules. Meanwhile, ZoombaTogether addresses the participants' privacy concerns by supporting on-device computer vision-based body posture estimation so that they could keep their personal video private from other group members. In this work, we provide a solution to use visual feedback as a communication channel for exercise participants and demonstrate the possibility of enhancing the online group exercise experience. We encourage the community to carry on our work and explore solutions to the challenges of missing social signals in online group exercises. © 2023 Copyright held by the owner/author(s).
Original languageEnglish
Title of host publicationDIS 2023
Subtitle of host publicationProceedings of the 2023 ACM Designing Interactive Systems Conference
PublisherAssociation for Computing Machinery
Pages261-265
ISBN (Print)9781450398985
DOIs
Publication statusPublished - 2023
Externally publishedYes
EventACM SIGCHI Conference on Designing Interactive Systems 2023 (DIS 2023) - Carnegie Mellon University, Pittsburgh, United States
Duration: 10 Jul 202314 Jul 2023
https://dis.acm.org/2023/
https://programs.sigchi.org/dis/2023

Conference

ConferenceACM SIGCHI Conference on Designing Interactive Systems 2023 (DIS 2023)
Abbreviated titleACM DIS2023
PlaceUnited States
CityPittsburgh
Period10/07/2314/07/23
Internet address

Research Keywords

  • group-level performance visualization
  • online group exercise
  • pose detection and tracking
  • privacy
  • visual feedback

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