Tracking daily paths in home contexts with RSSI fingerprinting based on UWB through deep learning models

2025-09-07

AI Summary

To address the degradation in localization accuracy of Ultra-Wideband (UWB) time-of-flight (TOF) and time-difference-of-arrival (TDOA) systems in home environments—caused by wall attenuation and occlusion—this paper proposes an RSSI fingerprinting-based deep learning approach for continuous path tracking. We introduce a novel hybrid CNN-LSTM architecture that jointly exploits spatial feature extraction (via convolutional layers) and temporal dependency modeling (via long short-term memory units), coupled with an optimized sliding-time-window strategy to enhance robustness in dynamic indoor navigation. Experimental evaluation in realistic multi-room residential settings demonstrates an average absolute positioning error of approximately 50 cm—substantially outperforming conventional UWB ranging methods. Moreover, the framework enables fine-grained human activity recognition without requiring on-body sensors or camera-based monitoring. This work establishes a new paradigm for privacy-preserving, low-cost intelligent home sensing.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

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.

Problem

• Tracking daily paths in home environments using UWB technology
• Addressing signal precision issues caused by walls and obstacles
• Comparing deep learning models for accurate location estimation

Innovation

• UWB RSSI fingerprinting for home tracking
• Hybrid CNN+LSTM model for location accuracy
• Temporal window optimization for activity recognition