通过深度度度度计合奏学习的非接触跨人活动识别
Chen Ye1, Siyuan Xu1, Zhengran He2
1School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China.
Bioengineering (Basel, Switzerland)
|November 27, 2024
概括
这项研究引入了一种新的基于Wi-Fi的人类活动识别方法,该方法适用于未知的个人. 这种新的方法显著提高了跨人活动识别 (CPAR) 任务的准确性.
科学领域:
- 人与计算机的互动.
- 机器学习用于信号处理.
- 智能家居技术是一项智能家居技术.
背景情况:
- 人类活动识别对于智能家居至关重要,特别是对于老年人监控和入侵检测.
- 无线网络传感提供非接触式,保护隐私的活动识别,但现有的深度学习模型难以识别未知个体.
- 当前的方法往往需要对新用户进行再培训,这限制了它们在现实世界中的适用性.
研究的目的:
- 开发一种使用Wi-Fi通道状态信息 (CSI) 的新型跨人活动识别 (CPAR) 方法,具有增强的概括功能.
- 为了提高基于Wi-Fi的人类活动识别对未知个人的识别精度和可行性.
- 解决现有的深度学习模型在处理看不见的主题方面的局限性.
主要方法:
- 使用来自Wi-Fi设备的通道状态信息 (CSI) 来进行活动识别.
- 采用基于注意力的双向长期短期记忆 (ABLSTM) 深度神经网络.
- 引入了快照组合技术,以训练多个基准分类器,以提高概括性.
- 整合了具有中心损失的计量学习来增强特征歧视,创建了SE-ABLSTM-C模型.
主要成果:
- 拟议的SE-ABLSTM-C方法显示了对跨人活动识别的识别准确度的显著改进.
- 在七个不同的活动类别中实现了应用级别的识别准确度.
- 快照组合和中心损失有效地提高了基于Wi-Fi的活动识别系统的概括性和可行性.
结论:
- 基于Wi-Fi的SE-ABLSTM-C方法为基于Wi-Fi的人类活动识别提供了强大的解决方案,特别是对于未知的个人.
- 这种方法增强了智能家居系统的隐私保护和非接触式传感能力.
- 开发的方法显示了在老年人监控和安全方面的现实应用的巨大潜力.
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