Examinando por Autor "Paddy Junior, Asiimwe"

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  • Miniatura
    Ítem
    ML-driven user activity-based GNSS activation for power optimization in resource-constrained environments
    (Institute of Electrical and Electronics Engineers Inc., 2025-08-11) Paddy Junior, Asiimwe; Díez Blanco, Luis Enrique; Bahillo, Alfonso; Eyobu, Odongo Steven
    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.
  • Miniatura
    Ítem
    Remote pedestrian localization systems for resource-constrained environments: a systematic review
    (Institute of Electrical and Electronics Engineers Inc., 2023-04-13) Paddy Junior, Asiimwe; Díez Blanco, Luis Enrique ; Bahillo, Alfonso ; Eyobu, O.S.
    The steady increase in the number of elderly citizens represents a likelihood of an increased burden on the family, government, healthcare, and social services since many of these elderly people cannot live independently without assistance from a caregiver. As such, there is an increase in demand for services in terms of technologies to address the urgent needs of the aging population. Remote monitoring, which is based on non-invasive, non-intrusive, and wearable sensors, actuators, and communication and information technologies, offers efficient solutions that bridge the gaps between healthcare and where elderly people really want to live every day. The rate at which such platforms have been adopted is extremely low in low-developed countries and rural areas, one of the main reasons being the lack or scarcity of some resources that these systems take for granted. In other words, these systems are designed for developed countries but are very much needed in resource-constrained environments as well. This study provides an in-depth, state-of-the-art systematic review of the current outdoor remote pedestrian localization systems to identify their suitability for resource-constrained environments. After checking 35 survey papers from the last ten years to the best of our knowledge, this is the first survey that investigates the suitability of existing pedestrian localization systems for a resource-constrained environment. This study is based on PRISMA guidelines to provide a replicable work and report the studies' main findings. A total of 37 works published between 2012, and January 2023 have been identified, analyzed, and key information that described the devices and tools used, communication technologies, position estimate technologies, methods, techniques and algorithms, and resource optimization strategies currently used by the localization systems was extracted to help us answer our question. The results indicate they are not fit for a resource-constrained environment as most assume the availability of infrastructures such as Wi-Fi, Internet, cellular networks, and digital literacy, among others, for their systems to operate properly, which are limited or not available in the resource-constrained environment described in this review.