优化设备上活动推理与星级操作的准确性-效率权衡
IEEE journal of biomedical and health informatics
|October 10, 2025
概括
这项研究引入了一个新的双分支高阶交互 (DbHoi) 块,用于边缘设备上的轻量级人类活动识别 (HAR). DbHoi通过有效捕获复杂的传感器数据模式而提高性能,而不会增加计算成本.
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:
- 轻量级卷积神经网络 (CNN) 在边缘设备上的基于传感器的人类活动识别 (HAR) 中表现出色,这是由于其快速推断.
- 有限的卷积内核范围限制了在时间序列传感器数据中捕获远程依赖关系,阻碍了性能.
- 自我注意力机制改善了远程依赖处理,但增加了计算成本,影响了实时 HAR.
研究的目的:
- 为移动 HAR 引入一种新的学习范式和一个计算效率高的双分支高顺序交互 (DbHoi) 块.
- 为了使轻量级的CNN能够从传感器数据中捕获高维,非线性特征,而无需显著的计算开销.
- 为了提高HAR在资源有限的边缘设备上的性能.
主要方法:
- 开发了一个新的"星际行动"学习范式.
- 提出了轻量级的双分支高阶交互 (DbHoi) 块.
- 在低维特征空间中运行,类似于多项式内核技巧,以管理计算复杂性.
- 在UCI-HAR,UniMiB-SHAR和OPPORTUNITY基准上进行了实验.
主要成果:
- 拟议的DbHoi区块的表现始终优于已有的轻量级网络,如MobileNet,ShuffleNet和GhostNet.
- DbHoi有效地从原始传感器输入中捕获复杂的高维特征,计算成本低.
- 在设备上的延迟分析证实了DbHoi在现实世界HAR应用中的实际效率.
结论:
- DbHoi 块为边缘设备上的基于传感器的 HAR 提供了一个计算友好和有效的解决方案.
- "星运算"范式提供了一种隐式学习复杂特征的方法,而不需要额外的计算负担.
- DbHoi在现实世界的人类活动识别部署方面表现出显著的实用价值.
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