用户行为模式的分类室内导航问题
Aleksandra Borsuk1, Andrzej Chybicki1, Michał Zieliński1
1Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, ul Narutowicza 11/12, 80-233 Gdańsk, Poland.
Sensors (Basel, Switzerland)
|August 14, 2025
概括
本研究介绍了基于行为的室内导航,通过将用户操作与路线匹配来估计位置. 它使用智能手机传感器和LSTM模型进行准确的活动识别,提供了具有成本效益的解决方案.
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
- 人与计算机的交互
背景情况:
- 由于传统全球定位系统 (GPS) 的局限性,室内导航具有挑战性.
- 现有的室内定位系统往往需要大量的基础设施.
- 需要具有成本效益和可扩展的室内导航解决方案.
研究的目的:
- 为室内导航提出一种基于行为的新型本地化方法.
- 开发一个通过将观察到的行为与预定义的路线匹配来估计用户位置的系统.
- 为了验证实时活动识别对导航的有效性.
主要方法:
- 利用智能手机传感器收集速度数据.
- 开发了一个基于长短期记忆 (LSTM) 的模型,用于用户行为分类 (站立,行走,楼梯,电梯).
- 在一秒钟时间窗口内进行的分类活动和用于导航估计的完整序列.
主要成果:
- 在分类单个用户活动时达到75%的准确性. 在一秒的间隔内.
- 在使用多数投票进行全序列活动分类时达到98.6%的准确性.
- 证明了基于行为本地化在室内导航中的可行性.
结论:
- 实时活动识别是室内导航系统的可行基础.
- 基于行为的本地化为依赖于基础设施的系统提供了具有成本效益的替代方案.
- 拟议的方法适用于特定的场景,如指导人员在复杂的建筑.
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