DeustoTeka Examinando por Autor "Bahillo, Alfonso"

Examinando por Autor "Bahillo, Alfonso"

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A 3D ray launching time-frequency channel modeling approach for UWB ranging applications
(Institute of Electrical and Electronics Engineers Inc., 2020-05-21) Otim, Timothy; López Iturri, Peio; Azpilicueta Fernández de las Heras, Leyre; Bahillo, Alfonso; Díez Blanco, Luis Enrique; Falcone, Francisco
Ultrawideband (UWB) has the ability to achieve decimetre level of ranging accuracy, hence, its wider usage nowadays in the field of positioning. In spite of the attractiveness of UWB, its performance is strongly dependent on the propagation channel. In this paper, an analysis of the the UWB channel for ranging applications using an inhouse developed 3D Ray launching (3D RL) algorithm is presented. A parametric study has been performed considering variations of cuboid size resolution of the simulation mesh, in order to analyze convergence impact on estimation accuracy, focusing on Radio frequency (RF) power levels as well as time domain characterization. The RF power results have been used to model the path-loss, small scale fading, and the power delay profile so as to obtain the statistics of the multipath channel as well as time of flight (TOF) estimation values. The results show that the 3D RL is a valuable tool to test UWB systems for ranging applications with a mean accuracy of up to 10 cm in multipath conditions considering complex scatterer distributions within the complete volume of the scenarios under test.
Automatic classification of the physical surface in sound uroflowmetry using machine learning methods
(Springer Science and Business Media Deutschland GmbH, 2024) Álvarez Arteaga, Marcos Lázaro; Arjona Aguilera, Laura; Iglesias Martínez, Miguel E.; Bahillo, Alfonso
This work constitutes the first approach for automatically classifying the surface that the voiding flow impacts in non-invasive sound uroflowmetry tests using machine learning. Often, the voiding flow impacts the toilet walls (traditionally made of ceramic) instead of the water in the toilet. This may cause a reduction in the strength of the recorded audio signal, leading to a decrease in the amplitude of the extracted envelope. As a result, just from analysing the envelope, it is impossible to tell if that reduction in the envelope amplitude is due to a reduction in the voiding flow or an impact on the toilet wall. In this work, we study the classification of sound uroflowmetry data in male subjects depending on the surface that the urine impacts within the toilet: the three classes are water, ceramic and silence (where silence refers to an interruption of the voiding flow). We explore three frequency bands to study the feasibility of removing the human-speech band (below 8 kHz) to preserve user privacy. Regarding the classification task, three machine learning algorithms were evaluated: the support vector machine, random forest and k-nearest neighbours. These algorithms obtained accuracies of 96%, 99.46% and 99.05%, respectively. The algorithms were trained on a novel dataset consisting of audio signals recorded in four standard Spanish toilets. The dataset consists of 6481 1-s audio signals labelled as silence, voiding on ceramics and voiding on water. The obtained results represent a step forward in evaluating sound uroflowmetry tests without requiring patients to always aim the voiding flow at the water. We open the door for future studies that attempt to estimate the flow parameters and reconstruct the signal envelope based on the surface that the urine hits in the toilet
Effects of the body wearable sensor position on the UWB localization accuracy
(MDPI AG, 2019-11-14) Otim, Timothy ; Díez Blanco, Luis Enrique; Bahillo, Alfonso ; López Iturri, Peio ; Falcone, Francisco
Over the years, several Ultrawideband (UWB) localization systems have been proposed and evaluated for accurate estimation of the position for pedestrians. However, most of them are evaluated for a particular wearable sensor position; hence, the accuracy obtained is subject to a given wearable sensor position. This paper is focused on studying the effects of body wearable sensor positions i.e., chest, arm, ankle, wrist, thigh, forehead, and hand, on the localization accuracy. According to our results, the forehead and the chest provide the best and worst body sensor location for tracking a pedestrian, respectively. With the wearable sensor at the forehead and chest position, errors lower than 0.35 m (90th percentile) and 4 m can be obtained, respectively. The reason for such a contrast in the performance lies in the fact that, in non-line-of-sight (NLOS) situations, the chest generates the highest multipath of any part of the human body. Thus, the large errors obtained arise due to the signal arriving at the target wearable sensor by multiple reflections from interacting objects in the environment rather than by direct line-of-sight (LOS) or creeping wave propagation mechanism.
Flow prediction in sound-based uroflowmetry
(Nature Research, 2025-01-03) Álvarez Arteaga, Marcos Lázaro; Arjona Aguilera, Laura; Jojoa Acosta, Mario Fernando; Bahillo, Alfonso
Sound-based uroflowmetry (SU) offers a non-invasive alternative to traditional uroflowmetry (UF) for evaluating lower urinary tract dysfunctions, enabling home-based testing and reducing the need for clinic visits. This study compares SU and UF in estimating urine flow rate and voided volume in 50 male volunteers (aged 18–60), with UF results from a Minze uroflowmeter as the reference standard. Audio signals recorded during voiding were segmented and machine learning algorithms (gradient boosting, random forest, and support vector machine) estimated flow parameters from three devices: Ultramic384k, Mi A1 smartphone, and Oppo smartwatch. The mean absolute error for flow rate estimation were 2.6, 2.5 and 2.9 ml/s, with R2 values of 84%, 83%, and 79%, respectively. Analysis of the Ultramic384k’s frequency range showed that the 0–8 kHz band contained 83% of significant components, suggesting higher sampling frequencies are unnecessary. A 1000 ms segment size was optimal for balancing computational efficiency and accuracy. Lin’s concordance coefficients for urine flow and voided volume using the smartwatch (0–8 kHz, 1000 ms) were 0.9 and 0.85, respectively, demonstrating that SU is a reliable, cost-effective alternative to UF for estimating key uroflowmetry parameters, with added patient convenience
Measuring disability inclusion performance in cities using Disability Inclusion Evaluation Tool (DIETool)
(MDPI, 2020-02-13) Rebernik, Natasa; Szajczyk, Marek; Bahillo, Alfonso; Marušić, Barbara Goličnik
Cities are exposed to a growing complexity, diversity and rapid socio-technical developments. One of the greatest challenges is as of how to become fully inclusive to fit the needs of all their citizens, including those with disabilities. Inclusive city, both in theory and practice, still lacks attention. Even in the context of ambitious contemporary concepts, such as smart and sustainable city, the question remains: Do smart and sustainable cities consider inclusiveness of all their inhabitants? Among numerous evaluation systems that measure city's smartness, sustainability or quality of life, those tackling inclusion are very rare. Specifically, disability inclusion is hardly covered. This may be one of the reasons why cities struggle with applying disability inclusion to practice in a holistic and integrative way. This paper proposes a Disability Inclusion Evaluation Tool (DIETool) and Disability Inclusion Performance Index (DIPI), designed to guide cities through a maze of accessibility and disability inclusion related requirements set within the political, legislative and standardization frameworks. The testing in two European cities shows that the tool is beneficial for providing diagnosis as to how disability friendly a city is, and as such offers an opportunity for designing informed corrective measures towards disability inclusive city design.
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.
A protocol for microclimate-related street assessment and the potential of detailed environmental data for better consideration of microclimatology in urban planning
(MDPI, 2023-05-18) Ravnikar, Ziva ; Bahillo, Alfonso; Marušić, Barbara Goličnik
This paper presents a warning that there is a need for better consideration of microclimatology in urban planning, particularly when addressing microclimate-related human comfort in designing outdoor public spaces. This paper develops a protocol for microclimate-related street assessment, considering simultaneous dynamic environmental components data gathering and better understanding of microclimatic conditions when commuting by bicycle. The development of new information and communication technologies (ICTs) has the potential for overcoming the gap between microclimatology and urban planning, since ICT tools can produce a variety of soft data related to environmental quality and microclimate conditions in outdoor spaces. Further, the interpretation of data in terms of their applicability values for urban planning needs to be well addressed. Accordingly, this paper tests one particular ICT tool, a prototype developed for microclimate data collection along cycling paths. Data collection was performed in two European cities: Bilbao (Spain) and Ljubljana (Slovenia), where the main objective was the development of a protocol for microclimate-related street assessment and exploration of the potential of the collected data for urban planning. The results suggest that the collected data enabled sufficient interpretation of detailed environmental data and led to a better consideration of microclimatology and the urban planning of cycling lanes. The paper contributes to urban planning by presenting a protocol and providing fine-grained localised data with precise spatial and temporal resolutions. The data collected are interpreted through human comfort parameters and can be linked with rates/levels of comfort. As the collected data are geopositioned, they can be presented on a map and provide links between environmental conditions within a spatial context.
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.
A smartphone-based system for outdoor data gathering using a wireless beacon network and GPS data: from cyber spaces to senseable spaces
(MDPI AG, 2018-05-15) Osaba, Eneko; Pierdicca, Roberto; Malinverni, Eva Savina; Khromova, Anna ; Álvarez Franco, Fernando Javier ; Bahillo, Alfonso
Information and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.
A survey of machine learning in pedestrian localization systems: applications, open issues and challenges
(Institute of Electrical and Electronics Engineers Inc., 2021-08-26) Mirama Pérez, Víctor Fabián ; Díez Blanco, Luis Enrique; Bahillo, Alfonso; Quintero, Víctor
With the popularization of machine learning (ML) techniques and the increased chipset's performance, the application of ML to pedestrian localization systems has received significant attention in the last years. Several survey papers have attempted to provide a state-of-the-art overview, but they usually limit their scope to a particular type of positioning system or technology. In addition, they are written from the point of view of ML techniques and their practice, not from the point of view of the localization system and the specific problems that ML techniques can help to solve. This article is intended to offer a comprehensive state-of-the-art survey of the ML techniques that have been adopted over the last ten years to improve the performance of pedestrian localization systems, addressing the applicability of ML techniques in this domain, along with the main localization strategies. It concludes by indicating the underlying open issues and challenges associated with the existing systems, and possible future directions in which ML techniques could improve the performance of pedestrian localization systems. Among other open issues, most previous authors have focused their attention on position estimation accuracy, which wastes the potential of ML techniques to improve other performance parameters (e.g., response time, computational complexity, robustness, scalability or energy efficiency). This study shows that there is a strong trend towards the application of supervised learning. Consequently, there are many potential research opportunities in the use of other learning types, such as unsupervised and reinforcement learning, to improve the performance of pedestrian localization systems.
Towards sub-meter level UWB indoor localization using body wearable sensors
(Institute of Electrical and Electronics Engineers Inc., 2020-09-29) Otim, Timothy; Bahillo, Alfonso; Díez Blanco, Luis Enrique; López Iturri, Peio; Falcone, Francisco
Thanks to its ability to provide sub-meter level positioning accuracy, Ultrawideband (UWB) has found wide use in several wireless body area network (WBAN) applications such as ambient assisted living, remote patient management and preventive care, among others. In spite of the attractiveness of UWB, it is not possible to achieve this level of accuracy when the human body obstructs the wireless channel, leading to a bias in the Time of Flight (TOF) measurements, and hence a detection of position errors of several meters. In this paper, a study of how a sub-meter level of accuracy can be achieved after compensating for body shadowing is presented. Using a Particle Filter (PF), we apply UWB ranging error models that take into consideration the body shadowing effect and evaluate them through simulations and extensive measurements. The results show a significant reduction in the median position error of up to 75 % and 82 % for simulations and experiments, respectively, leading to the achievement of a sub-meter level of localization accuracy.
Using ICTs for the improvement of public open spaces: the opportunity offered by cyberParks digital tools
(Springer Verlag, 2019) Osaba, Eneko ; Pierdicca, Roberto; Duarte, Tiago; Bahillo, Alfonso; Mateus, Diogo
In the last decade, the potential of mobile devices for augmenting outdoor experience opened up new solutions, whose value is twofold. On one hand, users can experience new forms of interaction with space. On the other, stakeholders can have access to the so-called User Generated Data, that is different types of information related to public spaces that could be used to improve their conception of space. In line with this, several digital tools have been developed and tested within the framework of CyberParks COST Action TU-1306 with the intention of exploring how information and communication technologies (ICTs) can contribute to the improvement of Public Open Spaces (POS). In this way, this chapter aims to study the relationship between ICTs and POS, focused on the opportunities offered by three different digital tools: the WAY-CyberParks, the EthnoAlly, and the CyberCardeto. The main advantages of using these digital tools are: (1) the real-time data gathering, (2) maintaining an updated database, (3) collecting traces of different activities and users’ groups “at the same time and space”, and (4) recording their opinion and preferences, via text, video, sound or pictures. Furthermore, the chapter attempts to analyse distinct types of results produced by their use, based on different study cases where the digital tool has been tested. The data obtained in these places serve to demonstrate the features and type of data gathered. With these case studies, the chapter attempts to highlight the main potential of each platform as related to different stakeholders and users.

Examinando por Autor "Bahillo, Alfonso"

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