RT info:eu-repo/semantics/article
T1 ML-Driven User Activity-Based GNSS Activation for Power Optimization in Resource-Constrained Environments
A1 Paddy Junior, Asiimwe
A1 Enrique Díez, Luis
A1 Bahillo Martínez, Alfonso
A1 Steven Eyobu, Odongo
A1 Bahillo, Alfonso
K1 Global Navigation Satellite System (GNSS) activation
K1 inertial sensors
K1 localization
K1 machine learning
K1 positioning
K1 power optimization
K1 resource-constrained environment
K1 tracking
K1 user activity
AB The aging population represents an increasing burden on healthcare systems, which is shifting policies from institutionalization to aging in the community. Remote monitoring offers efficient solutions that bridge the gaps between healthcare and where elderly people really want to live every day. However, the adoption of such systems remains low, especially in resource-constrained environments like underdeveloped regions and rural areas, due to the lack of resources often taken for granted in system design. Location is one of the main types of information to monitor, as it provides information about behavior and physical activity. Global Navigation Satellite System (GNSS) is the de facto technology, and although its high-power consumption aligns poorly with battery-powered devices, it is still the best choice for accurate and reliable remote localization of pedestrians. Deciding when to turn on/off the GNSS receiver based on context is a key strategy for power optimization, the two main types of contexts being the user’s position and activity. However, existing methods in the literature are not suitable for resource-constrained environments because they require the installation of beacons, which entail additional cost and power consumption, or assume the availability of external signals that are not met in such environments, or are based on simple user activity detection. This work proposes a new GNSS activation method based on detecting the specific walking activity for changing locations. In resource-constrained rural environments, people typically spend most of their time outdoors near their houses, where it is not necessary to activate the GNSS so frequently to monitor them. Restricting the GNSS activation to the moments in which they are moving to a different location could be enough and would reduce the power consumption. Four machine learning (ML) classification models [long short-term memory (LSTM), extreme gradient boosting (XGBoost), support vector machine (SVM), and random forest (RF)] have been implemented and evaluated using a smartwatch’s inertial sensor data. The best model, XGBoost, was exported to a custom-designed embedded system and evaluated in real-world tests. It demonstrated over 40% power savings compared to conventional motion-based methods.
PB IEEE
SN 0018-9456
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/80134
UL https://uvadoc.uva.es/handle/10324/80134
LA eng
NO IEEE Transactions on Instrumentation and Measurement, 2025, vol. 74, n. 9531220, pp. 1-20.
NO Producción Científica
DS UVaDOC
RD 28-nov-2025