一个隐私和能源意识的联邦框架,以承认人类活动
Ahsan Raza Khan1, Habib Ullah Manzoor1,2, Fahad Ayaz1
1James Watt School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了一种混合Spiking-LSTM (S-LSTM) 模型,用于使用可穿戴传感器进行私人和节能的人类活动识别 (HAR). 与传统方法相比,S-LSTM模型实现了更高的精度和效率.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 使用可穿戴传感器进行的人类活动识别 (HAR) 的集中式深度学习面临隐私,通信和效率方面的挑战.
- 现有的模型难以满足持续监控医疗保健的需求.
研究的目的:
- 为保护隐私和节能 HAR 提出一个联合学习框架.
- 开发一种混合的尖端神经网络 (SNN) 和长期短期记忆 (LSTM) 模型 (S-LSTM) 以提高 HAR 的性能.
主要方法:
- 开发了一种混合S-LSTM模型,集成SNN和LSTM,以实现协同事件驱动和序列建模功能.
- 雇员代用渐变学习和反向传播通过时间完全监督端到端的培训.
- 在一个联合学习框架内对两个公共数据集进行了模型评估.
主要成果:
- 在精度和能源效率方面,S-LSTM 模型超过了 LSTM,CNN 和 S-CNN 模型.
- 实现了高准确率:室内HAR场景为97.36%,室外HAR场景为89.69%.
- 与标准LSTM模型相比,在能源效率方面取得了32.30%的改进.
- 通过局部数据微调进行个性化,提高了个人用户准确度高达9%.
结论:
- 拟议的联合学习框架与S-LSTM模型为节能和保护隐私的HAR提供了可行的解决方案.
- 混合S-LSTM架构有效地结合了SNNs和LSTM的优势,实现了卓越的性能.
- 个性化显著提高了个人用户的HAR模型准确性,强调了它在医疗保健应用中的重要性.
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