Efficient remote pedestrian localization techniques for resource-constrained environments
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2025-07-24
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Universidad de Deusto
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This thesis explores resource-efficient pedestrian localization techniques tailored for outdoor remote resource-constrained environments, specifically focusing on enhancing remote monitoring for elderly people. With a global trend toward aging populations, particularly in underdeveloped regions, and a policy shift from institutional care to community-based aging, it is estimated that by 2050, two-thirds of the world’s population over 60 will reside in low- and middle-income resource-constrained countries. This trend is likely to place a higher burden on healthcare, family, and social services since many of these elderly people cannot live independently without assistance from a caregiver.
Remote monitoring systems offer promising solutions to bridge the gap between elderly individuals’ needs and available healthcare services, but their adoption is still limited in environments that lack basic infrastructure like stable power and communication networks. Therefore, this study begins by clearly defining ”resource-constrained environments”and systematically reviews existing outdoor remote pedestrian localization systems, evaluating their suitability for these environments. Global Navigation Satellite System (GNSS) technology is highlighted as the most viable option for remote, accurate, long-range, infrastructure-free outdoor localization, but its high power consumption presents challenges when integrated into battery-powered, wearable IoT devices.
This research proposes two methods for efficient GNSS activation specifically tailored to resource-constrained environments to make GNSS feasible. Both approaches aim to minimize unnecessary GNSS activation, optimizing power consumption while ensuring reliable user localization.
The first method is a position-based GNSS activation approach using a Pedestrian Dead-Reckoning (PDR) system. By leveraging a predefined geofence (safe zone) around the user’s home, GNSS activation is minimized, turning on only when the user leaves the safe zone.
Two implementations of the PDR system were evaluated: accelerationbased
and pitch-based. The proposed PDR-based activation method was validated through extensive experimental evaluation, demonstrating significant improvements in power efficiency—up to 90% compared to acceleration-based methods both inside and outside the geofence without requiring costly infrastructure such as beacons.
The second approach employs machine learning (ML) to drive GNSS activation based on user activity. By leveraging data from inertial sensors, the ML-based system differentiates between everyday ”at-home” activities and ”walking away” from home. The model activates GNSS only when it detects the specific user activity of ”walking away” from home. Four machine learning models were evaluated—Long Short- Term Memory (LSTM), XGBoost, Support Vector Machine (SVM), and Random Forest (RF)—with XGBoost being selected for implementation due to its strong balance between accuracy and computational efficiency. This method proved effective in significantly reducing power consumption, achieving over 40% savings compared to the acceleration based method.
Experimental validation demonstrated that the proposed PDR-based and
ML-driven GNSS activation methods are suitable for deployment in realworld,
resource-constrained environments. By selectively activating GNSS, these methods effectively extended battery life, allowing wearable systems to support reliable remote monitoring for longer periods.
The proposed solutions successfully addressed the goal of developing resource-efficient localization systems for aging populations, proving to be scalable and cost-effective while enhancing safety for elderly individuals and reducing the burden on caregivers. The targeted power optimizations make these systems feasible for current use and adaptable for future improvements, providing practical, energy-efficient remote monitoring solutions that help elderly individuals age safely in their communities with minimal reliance on resource-intensive infrastructure.
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Descripción
Materias
Matemáticas
Ciencia de los ordenadores
Sistemas de navegación y telemetría del espacio
Ciencias de la Tierra y del Espacio
Geografía
Teoría de la localización
Ciencia de los ordenadores
Sistemas de navegación y telemetría del espacio
Ciencias de la Tierra y del Espacio
Geografía
Teoría de la localización