Predicting physical fatigue in athletes in rope skipping training using ECG signals

Feng, Weibin; Zeng, Kelong; Zeng, Xiaomei; Chen, Jiejia; Peng, Hong; Hu, Bin; Liu, Guangyuan

doi:10.1016/j.bspc.2023.104663

Cited by 10 publications

(2 citation statements)

References 54 publications

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“…By contrast, a number of algorithms have incorporated machine learning and deep learning models to create physical techniques that rely on input from EEG [28,29], ECG [30], and EOG. These techniques represent a fusion of physical and behavioral strategies.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

2023

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Due to the widespread issue of road accidents, researchers have been drawn to investigate strategies to prevent them. One major contributing factor to these accidents is driver fatigue resulting from exhaustion. Various approaches have been explored to address this issue, with machine and deep learning proving to be effective in processing images and videos to detect asymmetric signs of fatigue, such as yawning, facial characteristics, and eye closure. This study proposes a multistage system utilizing machine and deep learning techniques. The first stage is designed to detect asymmetric states, including tiredness and non-vigilance as well as yawning. The second stage is focused on detecting eye closure. The machine learning approach employs several algorithms, including Support Vector Machine (SVM), k-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Decision Tree (DT), Logistic Regression (LR), and Random Forest (RF). Meanwhile, the deep learning approach utilizes 2D and 3D Convolutional Neural Networks (CNNs). The architectures of proposed deep learning models are designed after several trials, and their parameters have been selected to achieve optimal performance. The effectiveness of the proposed methods is evaluated using video and image datasets, where the video dataset is classified into three states: alert, tired, and non-vigilant, while the image dataset is classified based on four facial symptoms, including open or closed eyes and yawning. A more robust system is achieved by combining the image and video datasets, resulting in multiple classes for detection. Simulation results demonstrate that the 3D CNN proposed in this study outperforms the other methods, with detection accuracies of 99 percent, 99 percent, and 98 percent for the image, video, and mixed datasets, respectively. Notably, this achievement surpasses the highest accuracy of 97 percent found in the literature, suggesting that the proposed methods for detecting drowsiness are indeed effective solutions.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

2023

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“…Therefore, the application of multiple types of wearable sensors can achieve high-precision recognition of human activities and states, enabling these devices to handle complex sports movements with increased robustness. Consequently, wearable sensors play a crucial role in sports activity recognition ( Feng et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors

Wang,

Tang,

Xiao

et al. 2024

Preventive Medicine Reports

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Recent Progress in the Development of Flexible Wearable Electrodes for Electrocardiogram Monitoring During Exercise

Kang,

Lee,

Kwon

et al. 2024

Advanced NanoBiomed Research

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Electrocardiogram (ECG) monitoring has recently been an important indicator of cardiac health diagnosis. In the past, ECG could be measured under limited conditions in hospitals with 12‐lead electrode systems. Recently, portable and wearable devices have offered continuous, real‐time monitoring of ECG signals in real life. However, developing wearable ECG sensors that provide low‐motion artifacts and high‐quality signals during exercise conditions is still challenging. Herein, this review reports a systematic summary of the key characteristics, properties, and requirements of flexible wearable ECG devices for the early diagnosis of heart dysfunction in dynamic motions, including exercise. In addition, the recent progress in controlling sensor adhesion and novel materials for designing dry electrodes are discussed to improve ECG signal quality in exercise. Finally, various aspects of electrode developmental challenges and limitations are reviewed, and research directions for future studies are discussed.

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Predicting physical fatigue in athletes in rope skipping training using ECG signals

Cited by 10 publications

References 54 publications

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

An Adaptive Fatigue Detection System Based on 3D CNNs and Ensemble Models

Optimization system for training efficiency and load balance based on the fusion of heart rate and inertial sensors

Recent Progress in the Development of Flexible Wearable Electrodes for Electrocardiogram Monitoring During Exercise

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