半监督集体学习以识别人为活动在 casas 京都数据集
Ariza-Colpas Paola Patricia1, Pacheco-Cuentas Rosberg1, Shariq Butt-Aziz2
1Universidad de la Costa, Department of Computer Science and Electronics, Barranquilla, Colombia.
Heliyon
|April 24, 2024
概括
本研究介绍了一种新的半监督集体学习模型,用于人类活动识别 (HAR). 该方法通过使用聚类和分类准确识别日常活动,提高老年人的智能家居安全性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 人类活动识别 (HAR) 对于健康,娱乐和体育领域的应用至关重要.
- 智能家居为老年人,尤其是神经退行性疾病患者的护理提供了潜在的潜力.
- 现有的HAR数据集,就像CASAS数据集一样,为室内活动识别提供了有价值的数据.
研究的目的:
- 使用半监督集体学习开发一个先进的HAR模型.
- 利用基于距离的聚类来加强活动识别.
- 提高 HAR 系统在智能家居环境中的准确性和有效性.
主要方法:
- 基于半监督集体学习的新型模型被开发出来.
- 基于距离的集群分析被用来识别不同的活动集群.
- 监督技术被用于随后对已识别的集群进行分类.
主要成果:
- 拟议的模型在活动识别方面显示出有希望的结果.
- 与最先进的方法相比,质量指标分析表明了有利的结果.
- 综合框架显示了HAR应用的巨大潜力.
结论:
- 半监督集体学习模型为人类活动识别提供了一个强大的方法.
- 这种方法可以显著提高智能家居在老年人护理和安全方面的能力.
- 进一步的研究可以建立在这个框架上,用于更复杂的HAR系统.
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