基于Openpose和长短期记忆网络的艺术教学的交互和心理特征
1School of Art, Sichuan University Jinjiang College, Meishan, Sichuan, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究将深度学习与心率监测相结合,用于准确的艺术舞蹈动作识别. 综合方法显著提高了识别准确性,为舞蹈教学和交互分析提供了新的见解.
科学领域:
- * 人工智能 * 人工智能
- * 生物医学工程 * 生物医学工程
- * 艺术教育 艺术教育
背景情况:
- *对艺术欣赏和学习的日益增长的需求需要创新的教学方法.
- *传统的艺术教学模式缺乏个性化的心理理解和创造性的反.
- * 整合技术可以提高艺术教育的分析和有效性.
研究的目的:
- * 开发一种新的方法来识别艺术舞蹈教学中的动作,使用深度学习和心率数据.
- *将姿势估计和生理数据结合起来,以全面了解学习者参与.
- *通过数据融合建立最佳识别模型,以提高评估舞蹈技巧的准确性.
主要方法:
- * 利用OpenPose网络和图形卷积网络进行视频数据处理和动作识别.
- *使用长期短期记忆 (LSTM) 网络来识别心率数据.
- * 在两个决策级别实现了适应性重量分析,用于数据融合的姿势和心率识别.
主要成果:
- * 拟议的分类融合模型的准确性从85.0%提高到97.5%.
- * 综合方法与单模识别技术相比,表现优越.
- * 该系统有效地评估心率与高精度的动作识别一起.
结论:
- * 基于深度学习的动作识别和心率监测的融合为分析艺术舞蹈教学提供了一个强大的方法.
- * 这种方法为评估学习者的表现和参与艺术学科提供了一个新的范式.
- * 这项研究为未来对艺术互动教学系统的研究开辟了道路.
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