AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance

Wang, Jianbo; Qiu, Kaibin; Peng, Houwen; Fu, Jianlong; Zhu, Jianke

doi:10.1145/3343031.3350910

Cited by 92 publications

(62 citation statements)

References 100 publications

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“…Ref. [ 27 ] create a structural-aware Spatial-Temporal relation convolution module to solve a usual problem in sports videos, which is suffering from blur due to the fast movement of athletes. Ref.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Badiola-Bengoa

Zorrilla

2021

Sensors

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Human Pose Estimation (HPE) has received considerable attention during the past years, improving its performance thanks to the use of Deep Learning, and introducing new interesting uses, such as its application in Sport and Physical Exercise (SPE). The aim of this systematic review is to analyze the literature related to the application of HPE in SPE, the available data, methods, performance, opportunities, and challenges. One reviewer applied different inclusion and exclusion criteria, as well as quality metrics, to perform the paper filtering through the paper databases. The Association for Computing Machinery Digital Library, Web of Science, and dblp included more than 500 related papers after the initial filtering, finally resulting in 20. In addition, research was carried out regarding the publicly available data related to this topic. It can be concluded that even if related public data can be found, much more data is needed to be able to obtain good performance in different contexts. In relation with the methods of the authors, the use of general purpose systems as base, such as Openpose, combined with other methods and adaptations to the specific use case can be found. Finally, the limitations, opportunities, and challenges are presented.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Badiola-Bengoa

Zorrilla

2021

Sensors

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“…Effective guidance is a key to DTC and many systems focus on helping trainees with pose estimation and correction such as AI Coach [ 70 ], which detects wrong poses based on video input and presents related video examples of 2D poses for athletes. Another coaching system in [ 47 ] shows a 2D projection motion comparison, 2D/3D joint distance and joint speed deviation.…”

Section: Discussionmentioning

confidence: 99%

“…Some researchers also tried to apply the model for all of the videos of exercise. In [ 70 ], a system proposed a system for videos of sports, which largely depended on the correctness of human poses. The system included trajectory extraction, human pose estimation and pose correctness.…”

Section: Categorizationmentioning

confidence: 99%

Digital Twin Coaching for Physical Activities: A Survey

Díaz

Ding

et al. 2020

Sensors

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Digital Twin technology has been rising in popularity thanks to the popularity of machine learning in the last decade. As the life expectancy of people around the world is increasing, so is the focus on physical activity to remain healthy especially in the current times where people are staying sedentary while in quarantine. This article aims to provide a survey on the field of Digital Twin technology focusing on machine learning and coaching techniques as they have not been explored yet. We also define what Digital Twin Coaching is and categorize the work done so far in terms of the objective of the physical activity. We also list common Digital Twin Coaching characteristics found in the articles reviewed in terms of concepts such as interactivity, privacy and security and also detail future perspectives in multimodal interaction and standardization, to name a few, that can guide researchers if they choose to work in this field. Finally, we provide a diagram for the Digital Twin Ecosystem showing the interaction between relevant entities and the information flow as well as an idealization of an ideal Digital Twin Ecosystem for team sports’ athlete tracking.

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“…As seen in Figure 1 , the strokes that have been detected in the classification phase [ 17 ] are ready for evaluation. An accurate assessment and scoring of the sport performance are essential to give guidance and feedback for the players, particularly novice ones [ 71 , 72 ]. Moreover, this feedback will help prevent a false stroke pattern from institutionalizing the player's performances.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

Tabrizi

Pashazadeh

Javani

2021

Computational Intelligence and Neuroscience

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Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke’ performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players’ Forehand strokes racket’s movement and orientation measurements; besides, the strokes’ evaluation scores were assigned by the three coaches. The authors investigated the ML models’ behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

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AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance

Cited by 92 publications

References 100 publications

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

A Systematic Review of the Application of Camera-Based Human Pose Estimation in the Field of Sport and Physical Exercise

Digital Twin Coaching for Physical Activities: A Survey

A Deep Learning Approach for Table Tennis Forehand Stroke Evaluation System Using an IMU Sensor

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