Automated Gym Exercise Form Checker: Deep Learning-Based Pose Estimation
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1320-2_7
Reference9 articles.
1. Zhang J, Zhang D, Xu X, Jia F, Liu Y, Liu X, Ren J, Zhang Y (2020) MobiPose: real-time multi-person pose estimation on mobile devices. In: Proceedings of the 18th conference on embedded networked sensor systems (SenSys ’20). Association for Computing Machinery, New York, NY, USA, pp 136–149. https://doi.org/10.1145/3384419.3430726
2. Rangari T, Kumar S, Roy PP et al (2022) Video based exercise recognition and correct pose detection. Multimed Tools Appl 81:30267–30282. https://doi.org/10.1007/s11042-022-12299-z
3. Singh V, Singh S, Gupta P (2020) Real-time anomaly recognition through CCTV using neural networks. Procedia Comput Sci 173:254–263. ISSN 1877-0509. https://doi.org/10.1016/j.procs.2020.06.030
4. Khurana R, Ahuja K, Yu Z, Mankoff J, Harrison C, Goel M (2018) GymCam: detecting, recognizing and tracking simultaneous exercises in unconstrained scenes. In: Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies, vol 2, no 4, pp 1–17. https://doi.org/10.1145/3287063
5. Liu AL, Chu WT (2020) A posture evaluation system for fitness videos based on recurrent neural network. In: 2020 international symposium on computer, consumer and control (IS3C). IEEE, pp 185–188. https://doi.org/10.1109/IS3C50286.2020.00055
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