在活动eCoaching中自动和个性化的推建模,使用深度学习和本体学
Ayan Chatterjee1,2, Andreas Prinz3, Michael Alexander Riegler4
1Department of Information and Communication Technology, University of Agder, 4879, Grimstad, Norway. ayan.chatterjee@uia.no.
Scientific reports
|June 22, 2023
概括
这项研究引入了一种新的深度学习和语义本体学方法,用于个性化的eCoaching建议,特别是对体力活动的建议. 混合模型在预测和分类方面实现了高精度,增强了用户指导.
科学领域:
- * 计算机科学 计算机科学
- * 人工智能 * 人工智能
- * 医疗信息学 医疗信息学
背景情况:
- *电子辅导 (eCoach) 旨在优化人类行为以实现目标.
- * 在eCoaching中,自动生成个性化推是一个重大挑战.
- *目前的方法缺乏真正个性化指导的复杂性.
研究的目的:
- * 开发一种新的方法,用于在eCoaching中生成混合和个性化的建议.
- *利用深度学习和语义本体学来增强推系统.
- * 应用和评估使用体力活动作为案例研究的方法.
主要方法:
- *使用时间序列预测 (CNN1D,LSTM,GRU) 和分类 (MLP,Rocket).
- *使用OntoeCoach语义本体学集成处理的数据到活动数据集中.
- * 使用SPARQL进行语义表示,推理和可理解的推生成.
主要成果:
- *CNN1D模型实现了97%的预测准确度;MLP分类器达到74%的准确度.
- * OntoeCoach 实体学证明了有效的推规划和生成.
- *对公共 (PMData) 和私人 (MOX2-5) 活动数据集进行评估.
结论:
- * 拟议的混合方法有效地产生个性化的体育活动建议.
- * OntoeCoach 实体学增强了电子辅导中的解释性和推理能力.
- * 这项研究通过智能系统推进了个性化行为优化领域.
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