对于精神疲劳状态评估的深度学习方法
Jiaxing Fan1, Lin Dong1,2, Gang Sun1
1Institute of Artificial Intelligence in Sports, Capital University of Physical Education and Sports, Beijing 100191, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
这项研究使用深度学习从心电图数据中检测运动员的精神疲劳,达到95.29%的准确性. 这种新的方法超越了传统的体育表现分析方法.
科学领域:
- 运动科学 运动科学 运动科学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 在体育运动中检测精神疲劳的传统方法通常依赖于心率变化 (HRV) 分析.
- 现有的深度学习模型,如CNN和LSTM已经显示出潜力,但可以改进复杂的生理信号分析.
研究的目的:
- 开发和验证混合深度神经网络模型,用于精确检测体育运动中的精神疲劳.
- 将拟议模型的性能与传统机器学习和其他深度学习技术进行比较.
主要方法:
- 一个混合深度神经网络集成残余网络 (ResNet) 和双向长短期内存 (Bi-LSTM) 被用于特征提取.
- 一个变压器架构被用于功能融合.
- 该模型使用原始心电图数据,2D光谱特征和生理信息进行了训练和测试.
主要成果:
- 拟议的混合深度学习模型在识别精神疲劳方面取得了95.29%的高精度.
- 该模型显著优于传统方法,如支持矢量机 (SVM) 和随机森林 (RF).
- 实验结果也显示出优于其他深度学习模型,如卷积神经网络 (CNNs) 和长短期记忆 (LSTM).
结论:
- 这项研究提出了一种非常准确和有效的基于深度学习的方法,用于从生理信号中识别精神疲劳.
- 这种方法为提高体育表现和体能训练提供了一个有前途的工具.
- 这些发现表明,疲劳监测的新方向超越了传统的HRV分析.
相关概念视频
01:19High-Level and Low-Level Awareness
245
Controlled processes in human consciousness represent high-alert mental states where individuals deliberately focus their attention on achieving specific goals. Controlled processes can be seen in situations like mastering new technology, where a person might become so absorbed that they ignore surrounding distractions. Such processes involve selective attention, requiring one to concentrate on particular elements of experience while disregarding others. These are governed by executive...
245
01:03Long-term Depression
2.5K
Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over...
Calcium Ion Concentration Mechanism
If over...
2.5K