MAP-i Thesis Proposal
Title: Movement analysis for functional fitness training
Coordinators: Ant´onio J. R. Neves (an@ua.pt), Institute of Electronics and Tele- matics Engineering of Aveiro, University of Aveiro
External Member: TBD (waiting for the answer of an invitation)
Research unit : Institute of Electronics and Telematics Engineering of Aveiro (IEETA / DETI / UA)
1 Objectives
Functional fitness is a classification of physical training that prepares the body for real-life movements and activities. It is also known as functional training or functional movement and the main goal is to train the human muscles to work together and prepare them for daily tasks, by simulating common movements that can be made at home, at work, or in different sports. Functional fitness is focused on building a body capable of doing real-life activities in real-life positions, such as squatting, reaching, pulling, and lifting.
Especially in the last couple of years, online fitness training systems and programs have increased [4-6]. However, current virtual training systems mostly provide only exercise demonstration and lack feedback to the athlete. As in any other training modality, for the individuals to reach their best results and to prevent injuries, there is the need for constant assistance from a coach or trainer, and for that reason, real-time feedback can become very important. A common observation is that even people who visit gyms regularly find it difficult to perform all steps in a workout accurately, in terms of body pose alignments.
Continuously doing an exercise incorrectly can lead to insufficient performance and may eventually cause severe long term injuries.
During this PhD work we pretend to explore existing algorithm and develop new ones for movement analysis in functional training using non-invasive monitoring based on deep learning and computer vision for pose estimation and recognition. The output of this PhD work will be able to provide athletes augmented feedback, both in terms of knowledge of performance and knowledge of results, without the need to wear special devices, sensors or markers. While recording a simple digital video, the athlete can assess and correct their movements during training, improving their body control, and obtain the feedback about results. Moreover, the proposed system can also be used by coaches as a tool to help them in the real time postural assessment and movement quality of their athletes, as well as a tool for effective remote coaching in all the situations where the athlete has
to practice outside the gym, like illness, travelling, or in cases like the recent context of social distancing that maintained most sports facilities closed for a period of time.
We expect that the developed technology could be used by anyone who has a smartphone with a digital camera and a network connection. This leads to an important issue that will be explored in this PhD regarding the acquisition of the videos under non controlled conditions. The methodology to be proposed will analyse a user’s body posture during a workout, representing the human body as a collection of limbs. The proposed approach builds on the latest advancements using deep learning for human body pose estimation [1- 3]. To analyse the body posture over time we will explore techniques for time series data alignment and optical flow tracking to describe the movement as sets of keypoints positi- ons as a function of time. Functional training exercises can be separated in body-weight exercises, when the user performing them does not use any object during the training and external weight exercises.
In order to correctly analyse exercises with external weights, there is the need to develop algorithms for external object detection that should be able to deal with a dif- ferent number of objects of various sizes, partially occluded, with bad illumination and in cluttered scenes. Two main approaches can be considered: one based on traditional classification algorithms where object classification will be based on the extraction of low level features and traditional classifiers, as well as conduct research on improving current deep learning approaches in this scenario. The referred part of this PhD work will be an important contribution for the scientific community. The output of the above-mentioned algorithms will be integrated into a user-friendly, accurate augmented feedback to be provided to the user. Software need to be developed or adapted to support the process of video recording, exercise specification and correspondent augmented feedback display.
The video will be uploaded to an external server that will run the movement recognition algorithms and will provide accurate feedback both in terms of numerical results (kno- wledge of results) and visual vector corrections for the body joints or parts that were positioned incorrectly during the performance (knowledge of performance).
2 References
[1] Muhammed Kocabas, Salih Karagoz, and Emre Akbas. Multiposenet: Fast multi- person pose estimation using pose residual network. In Proceedings of the European Conference on Computer Vision (ECCV), pages 417–433, 2018.
[2] Rıza Alp Guler, Natalia Neverova, and Iasonas Kokkinos. Densepose: Dense human pose estimation in the wild. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 7297–7306, 2018.
[3] Cao, Zhe, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. ”Open- Pose: realtime multi-person 2D pose estimation using Part Affinity Fields.”IEEE tran- sactions on pattern analysis and machine intelligence 43, no. 1 (2019): 172-186.
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[4] Friedhoff, J., and I. Alvarado. ”Move Mirror: An AI Experiment with Pose Estima- tion in the Browser using TensorFlow. js.”Google Creative Lab, A Medium Corporation (2018).
[5] Chen, Steven, and Richard R. Yang. ”Pose Trainer: correcting exercise posture using pose estimation.”arXiv preprint arXiv:2006.11718 (2020).
[6] Claiton LV Lisboa, Luciana Nedel, and Anderson Maciel. A study for postural evaluation and movement analysis of individuals. In 2016 XVIII Symposium on Virtual and Augmented Reality (SVR), pages 122–126. IEEE, 2016.
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