Human Pose Estimation based Speed Detection System for Running on Treadmill

Zhao, Zhonghan; Lan, Shanzhen; Zhang, Shujun

doi:10.1109/iccst50977.2020.00108

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Body-node-based methods detect fitness movements by calculating body nodes, which can be performed using OpenPose, Mediapipe, Simple Baselines, etc. [ 10 , 11 , 12 , 13 ]. Using these methods, nodes of the body and fitness movements can be detected through changes in the coordinates of the nodes.…”

Section: Introductionmentioning

confidence: 99%

“…Using these methods, nodes of the body and fitness movements can be detected through changes in the coordinates of the nodes. In addition to detecting the speed of fitness movements [ 10 ], these methods can also classify the current fitness movement type [ 11 ] or the error between fitness movements and standard movements [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

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Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network

Chen

Shin

Hasan

et al. 2022

Sensors

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Fitness is important in people’s lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yoga mats, and horizontal bars to complete fitness exercises and can effectively avoid contact with people, so it is deeply loved by people. People who work out at home use social media to obtain fitness knowledge, but learning ability is limited. Incomplete fitness is likely to lead to injury, and a cheap, timely, and accurate fitness detection system can reduce the risk of fitness injuries and can effectively improve people’s fitness awareness. In the past, many studies have engaged in the detection of fitness movements, among which the detection of fitness movements based on wearable devices, body nodes, and image deep learning has achieved better performance. However, a wearable device cannot detect a variety of fitness movements, may hinder the exercise of the fitness user, and has a high cost. Both body-node-based and image-deep-learning-based methods have lower costs, but each has some drawbacks. Therefore, this paper used a method based on deep transfer learning to establish a fitness database. After that, a deep neural network was trained to detect the type and completeness of fitness movements. We used Yolov4 and Mediapipe to instantly detect fitness movements and stored the 1D fitness signal of movement to build a database. Finally, MLP was used to classify the 1D signal waveform of fitness. In the performance of the classification of fitness movement types, the mAP was 99.71%, accuracy was 98.56%, precision was 97.9%, recall was 98.56%, and the F1-score was 98.23%, which is quite a high performance. In the performance of fitness movement completeness classification, accuracy was 92.84%, precision was 92.85, recall was 92.84%, and the F1-score was 92.83%. The average FPS in detection was 17.5. Experimental results show that our method achieves higher accuracy compared to other methods.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network

Chen

Shin

Hasan

et al. 2022

Sensors

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“…Similarly, for the sport of curling, an object detector was used for player and curling stone detection [ 33 ]. Other HPE-CNN applications include speed detection in running [ 34 ] and player pose analysis in tennis [ 35 ]. Other machine learning approaches have been used for the detection and classification of direction changes in tennis [ 36 ] and referee signal recognition in basketball [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

Brumann

Kukuk

Reinsberger

2021

Sensors

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In sport science, athlete tracking and motion analysis are essential for monitoring and optimizing training programs, with the goal of increasing success in competition and preventing injury. At present, contact-free, camera-based, multi-athlete detection and tracking have become a reality, mainly due to the advances in machine learning regarding computer vision and, specifically, advances in artificial convolutional neural networks (CNN), used for human pose estimation (HPE-CNN) in image sequences. Sport science in general, as well as coaches and athletes in particular, would greatly benefit from HPE-CNN-based tracking, but the sheer amount of HPE-CNNs available, as well as their complexity, pose a hurdle to the adoption of this new technology. It is unclear how many HPE-CNNs which are available at present are ready to use in out-of-the-box inference to squash, to what extent they allow motion analysis and if detections can easily be used to provide insight to coaches and athletes. Therefore, we conducted a systematic investigation of more than 250 HPE-CNNs. After applying our selection criteria of open-source, pre-trained, state-of-the-art and ready-to-use, five variants of three HPE-CNNs remained, and were evaluated in the context of motion analysis for the racket sport of squash. Specifically, we are interested in detecting player’s feet in videos from a single camera and investigated the detection accuracy of all HPE-CNNs. To that end, we created a ground-truth dataset from publicly available squash videos by developing our own annotation tool and manually labeling frames and events. We present heatmaps, which depict the court floor using a color scale and highlight areas according to the relative time for which a player occupied that location during matchplay. These are used to provide insight into detections. Finally, we created a decision flow chart to help sport scientists, coaches and athletes to decide which HPE-CNN is best for player detection and tracking in a given application scenario.

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STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification

Bang,

Kang,

Lee

et al. 2024

Complex Intell. Syst.

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The class imbalance problem occurs when there is an unequal distribution of classes in a dataset and is a significant issue in various artificial intelligence applications. This study focuses on the severe multiclass imbalance problem of human activity recognition in rehabilitation exercises for people with disabilities. To overcome this problem, we present a novel human action-centric augmentation method for human skeleton-based pose estimation. This study proposes the state transition-oriented conditional variational autoencoder (STO-CVAE) to capture action patterns in repeated exercises. The proposed approach generates action samples by capturing temporal information of human skeletons to improve the identification of minority disability classes. We conducted experimental studies with a real-world dataset gathered from rehabilitation exercises and confirmed the superiority and effectiveness of the proposed method. Specifically, all investigated classifiers (i.e., random forest, support vector machine, extreme gradient boosting, light gradient boosting machine, and TabNet) trained with the proposed augmentation method outperformed the models trained without augmentation in terms of the F1-score and accuracy, with F1-score showing the most improvement. Overall, the prediction accuracy of most classes was improved; in particular, the prediction accuracy of the minority classes was greatly improved. Hence, the proposed STO-CVAE can be used to improve the accuracy of disability classification in the field of physical medicine and rehabilitation and to provide suitable personal training and rehabilitation exercise programs.

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Human Pose Estimation based Speed Detection System for Running on Treadmill

Cited by 8 publications

References 7 publications

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network

Evaluation of Open-Source and Pre-Trained Deep Convolutional Neural Networks Suitable for Player Detection and Motion Analysis in Squash

STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification

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