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Khurana, Rushil and Ahuja, Karan and Yu, Zac and Mankoff, Jennifer and Harrison, Chris and Goel, Mayank (2018) GymCam. In: UNSPECIFIED.

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<jats:p>Worn sensors are popular for automatically tracking exercises. However, a wearable is usually attached to one part of the body, tracks only that location, and thus is inadequate for capturing a wide range of exercises, especially when other limbs are involved. Cameras, on the other hand, can fully track a user's body, but suffer from noise and occlusion. We present GymCam, a camera-based system for automatically detecting, recognizing and tracking multiple people and exercises simultaneously in unconstrained environments without any user intervention. We collected data in a varsity gym, correctly segmenting exercises from other activities with an accuracy of 84.6%, recognizing the type of exercise at 93.6% accuracy, and counting the number of repetitions to within ± 1.7 on average. GymCam advances the field of real-time exercise tracking by filling some crucial gaps, such as tracking whole body motion, handling occlusion, and enabling single-point sensing for a multitude of users.</jats:p>


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Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
CreatorsEmailPitt UsernameORCID
Khurana, Rushil
Ahuja, Karan
Yu, Zaczac.yu@pitt.eduZHY460000-0002-2452-5034
Mankoff, Jennifer
Harrison, Chris
Goel, Mayank
Date: 27 December 2018
Date Type: Publication
Journal or Publication Title: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume: 2
Number: 4
Publisher: Association for Computing Machinery (ACM)
Page Range: 1 - 17
Event Type: Conference
DOI or Unique Handle: 10.1145/3287063
Schools and Programs: School of Computing and Information > Computer Science
Refereed: Yes
Date Deposited: 04 Mar 2019 14:51
Last Modified: 22 Mar 2021 01:55


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