用UWB信号指纹+深度学习，实现居家路径厘米级追踪
Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models
- 基于UWB的RSSI信号指纹构建定位模型
- 混合CNN+LSTM模型误差低至50厘米
- 适合智能家居与老人行为监测场景
人机活动识别领域因物联网设备技术进步而快速发展，尤其在个人设备方面。本研究探讨利用超宽带（UWB）技术结合深度学习模型，在家庭环境中追踪住户移动路径。UWB通过飞行时间与到达时间差估算位置，但受墙体和障碍物影响，精度下降。为此，我们提出一种基于接收信号强度指示（RSSI）的指纹定位方法，数据来自两个住宅单元（60平方米和100平方米）中住户日常活动期间的采集。比较了卷积神经网络（CNN）、长短期记忆网络（LSTM）及混合CNN+LSTM模型的表现，并评估蓝牙技术作为对比。同时考察未来、过去或两者结合的时间窗口类型与时长的影响。结果表明，混合模型平均绝对误差接近50厘米，显著优于其他模型，展现出在住宅环境日常活动识别中的高精度定位能力。
原文摘要 · Abstract (English)
The field of human activity recognition has evolved significantly, driven largely by advancements in Internet of Things (IoT) device technology, particularly in personal devices. This study investigates the use of ultra-wideband (UWB) technology for tracking inhabitant paths in home environments using deep learning models. UWB technology estimates user locations via time-of-flight and time-difference-of-arrival methods, which are significantly affected by the presence of walls and obstacles in real environments, reducing their precision. To address these challenges, we propose a fingerprinting-based approach utilizing received signal strength indicator (RSSI) data collected from inhabitants in two flats (60 m2 and 100 m2) while performing daily activities. We compare the performance of convolutional neural network (CNN), long short-term memory (LSTM), and hybrid CNN+LSTM models, as well as the use of Bluetooth technology. Additionally, we evaluate the impact of the type and duration of the temporal window (future, past, or a combination of both). Our results demonstrate a mean absolute error close to 50 cm, highlighting the superiority of the hybrid model in providing accurate location estimates, thus facilitating its application in daily human activity recognition in residential settings.
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