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相关概念视频

Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

213
Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
213

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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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使用机器学习进行个性化智能家居自动化:预测用户活动

Mark M Gad1, Walaa Gad2, Tamer Abdelkader3

  • 1Media Engineering and Technology (MET) Department, German University in Cairo, Cairo 11835, Egypt.

Sensors (Basel, Switzerland)
|October 16, 2025
PubMed
概括

本研究介绍了一种使用机器学习来预测用户活动的智能家居自动化框架,以提高舒适度和能源效率. 边缘光人类活动识别预测器 (EL-HARP) 系统使用负担得起的硬件和梯度增强模型进行个性化实时控制.

关键词:
情境感知系统是情境感知系统.边缘计算是一种边缘计算.梯度增强模型的模型.人类活动的认可 人类活动的认可智能环境是一种智能环境.机器学习是机器学习.个性化个性化个性化智能家居自动化是什么意思

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科学领域:

  • 人工智能的人工智能
  • 智能家居技术 智能家居技术
  • 物联网 (IoT) 的物联网 (IoT) 的物联网.

背景情况:

  • 智能家居自动化旨在通过上下文感知控制来提高乘客舒适度和优化能源消耗.
  • 准确预测用户活动对于主动和个性化的智能家居功能至关重要.

研究的目的:

  • 为智能家居自动化引入个性化的框架,利用机器学习进行用户活动预测.
  • 开发和评估边缘光人类活动识别预测器 (EL-HARP) 模型,用于实时行为预测.

主要方法:

  • 使用了经济实惠的硬件 (Raspberry Pi 5,ESP32-CAM) 和开源软件来进行传感和控制.
  • 使用工程功能和历史数据训练了三种梯度增强模型 (XGBoost,CatBoost,LightGBM).
  • 优化了边缘部署的框架,专注于有效的培训和处理阶级不平衡.

主要成果:

  • 在EL-HARP模型中,LightGBM表现出强大的预测性能,特别是在延长时间特征的情况下.
  • 该框架实现了实时性能和适应个人用户行为模式的适应性.
  • 一个功能性原型验证了系统对情境感知智能家居控制的能力.

结论:

  • 开发的框架为智能家居自动化提供了可扩展,保护隐私和以用户为中心的方法.
  • 这项研究提升了个性化和主动智能生活环境的潜力.
  • EL-HARP模型为基于边缘的智能家居系统中的活动识别提供了强大的解决方案.