一种新的身体活动识别方法,使用深层合奏优化变压器和强化学习
Sajad Ahmadian1, Mehrdad Rostami2, Vahid Farrahi3
1Faculty of Information Technology, Kermanshah University of Technology, Kermanshah, Iran.
概括
这项研究引入了一种先进的深度学习组合方法,用于使用心率,速度和距离数据准确识别人类的身体活动. 这种新的方法优化了超参数,并整合了模型结果,在医疗保健和田径运动中实现了卓越的表现.
科学领域:
- * 计算机科学 计算机科学
- * 生物医学工程 * 生物医学工程
- * 数据科学数据科学
背景情况:
- *人类体育活动的识别对于医疗保健,人机交互,生活方式监测和体育运动至关重要.
- *深度学习模型被广泛使用,但需要最佳的超参数调整,这通常是手动和耗时的.
- * 整合多样化的数据源和模型输出在体育活动识别系统中提出了挑战.
研究的目的:
- * 提出一种用于增强体育活动识别的新型组合方法.
- * 通过修改的算术优化算法,自动优化深度学习模型的超参数.
- * 开发一种基于强化学习的组合,用于整合多模式时间序列数据 (心率,速度,距离).
主要方法:
- * 开发了一个基于深度变压器的时间序列分类模型.
- *使用修改后的算术优化算法进行了超参数优化.
- *用于集体学习的强化学习方法用于整合分类结果.
主要成果:
- * 拟议的组合方法在与最先进的模型相比,在真实世界的数据集上表现出更高的性能.
- *在关键指标中观察到显著的改善:准确度 (+3.44%),精度 (+9.45%),回忆力 (+5.43%),特异性 (+2.54%),F1得分 (+7.53%).
- *该方法有效地整合了心率,速度和距离的时间序列数据,以实现强大的活动识别.
结论:
- * 新型组合方法为自动体育活动识别提供了一个有前途且高效的解决方案.
- * 基于强化学习的自动超参数优化和集成提高了系统的性能和适用性.
- * 这种方法在健康监测,体育科学和人机交互方面有很大的应用潜力.
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