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Divya Thakur¹, Praveen Lalwani¹

¹VIT Bhopal University, Sehore, Madhya Pradesh, India.

Biomedical physics & engineering express

|December 21, 2023

概括

像Random Forest这样的机器学习模型显著提高了人类活动识别 (HAR). 这项研究比较了10个模型,发现了对身体活动准确分类的最佳表现者.

科学领域:

计算机科学计算机科学
机器人技术机器人技术机器人技术
生物医学工程生物医学工程

背景情况:

人类活动识别 (HAR) 传统上依赖于域专业知识来提取特征.
机器学习为HAR模型提供从原始感官数据中自动提取功能.
对各种应用而言,精确的身体活动分类至关重要.

研究的目的:

为了比较10个不同的机器学习模型对人类活动识别的有效性.
为活动识别任务确定可靠和精确的分类模型.
在基准人类行走数据集上评估机器学习模型性能.

主要方法:

进行了对10个机器学习模型的比较分析.
使用了三个基准公共人类行走数据集.
模型性能使用八个评估参数进行评估.

主要成果:

随机森林,额外树和光梯度增强机在所有八个参数中都表现出卓越的性能.
这些模型在选定的HAR数据集上表现优于其他模型.
机器学习显著提高了HAR系统的性能.

结论:

机器学习模型,特别是随机森林,额外树和光梯度增强机,对于HAR非常有效.
这项研究为选择HAR数据集的最佳模型提供了基础.
这些发现可以帮助识别双脚机器人系统的行走模式.

关键词:

人类活动识别人类活动识别这是一个双脚机器人.人类的步态是人类的步态机器学习是机器学习.

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