DeustoTeka
DeustoTeka recoge la producción científica del personal docente e investigador de la Universidad de Deusto. Su propósito es reunir, archivar, preservar y aumentar la visibilidad en acceso abierto de los resultados de investigación.
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Towards mobile edge computing: taxonomy, challenges, applications and future realms
(Institute of Electrical and Electronics Engineers Inc., 2020-09-25) Qadir, Junaid; Sainz de Abajo, Beatriz; Khan, Anwar; García-Zapirain, Begoña; Torre Díez, Isabel de la; Mahmood, Hasan
The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-The-Art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.
Innovation in traditional sport: applying the delphi method to strategic designs in basque hand-ball
(Frontiers Media SA, 2026-01-30) Sánchez Mencia, Eneko; González Santamaría, Xabier; Aurrekoetxea Casaus, Maite
Introduction: This study explores the potential of prospective qualitative methods, specifically the combination of the Delphi technique and scenario planning, to guide innovation processes in traditional sports, using the professional modality of Basque hand-ball as a case study. Methods: Employing a three-round design, the research gathered evaluations from 23 experts embedded within the sport's ecosystem (including players, referees, coaches, organizers, and media representatives) regarding a series of structured proposals linked to key elements of the game (e.g., scoring systems, service, equipment, rest times). Results: Findings reveal that the most viable innovations are not necessarily the most disruptive, but rather those that balance spectacle, competitive fairness, and symbolic fidelity to the internal logic of the sport. Proposals such as the “short games” system, time limits between serves, and neutral selection of equipment received strong support and have already been incorporated into the current configuration of the professional league. Discussion: Furthermore, perceived viability varied according to stakeholder profiles, underscoring the relevance of participatory and deliberative approaches in designing culturally legitimate reforms.
Assessment of organisational innovation: an analytical framework for higher education institutions.
(Multidisciplinary Digital Publishing Institute (MDPI), 2026-02) Peña Lang, María Begoña ; Villa Sánchez, Aurelio
This study analyses the degree of organisational innovation (OI) in Spanish universities and its relationship with institutional competitiveness, proposing a robust analytical framework for its assessment. A mixed, sequential and explanatory design was used, integrating a documentary analysis of R&D indicators, semi-structured interviews with 15 university managers and the validation of an OI questionnaire applied to 387 engineering students and graduates. Qualitative data were analysed with ATLAS.ti 9 and quantitative data were analysed using confirmatory factor analysis and structural equation modelling (SEM) in AMOS v.27, obtaining satisfactory fit indices (CFI = 0.970; RMSEA = 0.051). The results reveal moderate development of OI (Organisational Innovation), with significant differences between institutions according to their level of digitisation, strategic policies and organisational culture. Creativity emerged as the main predictor of key competencies such as active learning and technological design, while excessive institutional openness had negative effects on self-management.
AI and wearables for early detection of cognitive impairment and dementia: systematic review
(JMIR Publications Inc., 2026) Cejudo Taramona, Ander
; Arrojo Magro, Markel
; Martín Andonegui, Cristina ; Almeida, Aitor
Background: Traditional cognitive screening relies on episodic clinical assessments and may miss early changes preceding cognitive impairment and dementia. Wearable and mobile health technologies enable continuous monitoring of sleep, physical activity, and circadian rhythms, generating digital biomarkers that may support scalable early detection and prevention. However, current evidence remains fragmented across devices, analytic approaches, and cognitive outcomes. Objective: This study synthesizes and critically evaluates recent evidence on wearable devices for early detection and prevention of cognitive impairment and dementia, focusing on device categories, cognitive outcomes, analytic approaches, and prevention relevance. Methods: We searched PubMed, Scopus, ACM Digital Library, and SpringerLink for peer-reviewed studies published between January 2020 and December 1, 2025. Eligible studies included human participants with a mean age ≥50 years, continuous wearable-derived data collected for ≥24 hours, and validated cognitive outcomes; reviews, protocols, smartphone-only studies, and pharmacological interventions were excluded. Two reviewers independently screened studies, extracted data, and assessed risk of bias using the Appraisal Tool for Cross-Sectional Studies, Newcastle-Ottawa Scale, Cochrane Risk of Bias tool, and Quality Assessment of Diagnostic Accuracy Studies-2. Owing to substantial heterogeneity in devices, outcomes, and analytic methods, quantitative meta-analysis was not feasible; a structured narrative synthesis was conducted in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidance. This study was not prospectively registered. Results: We included 49 studies, with sample sizes ranging from 14 to 91,948 participants (>200,000 total) and a median sample size of 145. Most used research-grade actigraphy (43/49, 87.8%), while fewer used commercial wearables (7/49, 14.3%). Cognitive outcomes most frequently relied on global screening instruments, including the Mini-Mental State Examination (18/49, 36.7%), followed by ICD-10 (International Statistical Classification of Diseases, Tenth Revision)–based clinical diagnoses (7/49, 14.3%) and the Montreal Cognitive Assessment (7/49, 14.3%). Analytic approaches were predominantly statistical (36/49, 73.5%), with fewer studies applying machine learning (7/49, 14.3%) or deep learning methods (6/49, 12.2%). Statistical analyses linked disrupted sleep, circadian rhythm fragmentation, and irregular activity patterns to worse cognitive outcomes, with modest-to-moderate effect sizes. Machine learning and deep learning approaches reported classification performance with area under the curve values between approximately 0.70 and 0.95. Approximately one-quarter of the studies (13/49, 26.5%) addressed early detection or prevention through longitudinal risk estimation or predictive modeling. Key limitations included small sample sizes, short monitoring durations, and limited external validation. Conclusions: Wearable-derived behavioral markers show promise for early risk stratification. This review advances the field by shifting from descriptive associations toward a digital phenotyping framework evaluating artificial intelligence–driven prediction in the preclinical window. Unlike prior reviews focused on established dementia, it differentiates direct predictive evidence from indirect correlational findings and critically assesses methodological maturity. Continuous, passive monitoring may enable scalable detection of subtle behavioral changes, supporting earlier and more personalized risk reduction strategies.
Mobile health apps for medical emergencies: systematic review
(JMIR Publications Inc., 2020-03-02) Plaza Roncero, Alejandro; Marques, Gonçalo; Sainz de Abajo, Beatriz; Martín Rodríguez, Francisco; Pozo Vegas, Carlos del; García-Zapirain, Begoña; Torre Díez, Isabel de la
Background: Mobile health apps are used to improve the quality of health care. These apps are changing the current scenario in health care, and their numbers are increasing. Objective: We wanted to perform an analysis of the current status of mobile health technologies and apps for medical emergencies. We aimed to synthesize the existing body of knowledge to provide relevant insights for this topic. Moreover, we wanted to identify common threads and gaps to support new challenging, interesting, and relevant research directions. Methods: We reviewed the main relevant papers and apps available in the literature. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used in this review. The search criteria were adopted using systematic methods to select papers and apps. On one hand, a bibliographic review was carried out in different search databases to collect papers related to each application in the health emergency field using defined criteria. On the other hand, a review of mobile apps in two virtual storage platforms (Google Play Store and Apple App Store) was carried out. The Google Play Store and Apple App Store are related to the Android and iOS operating systems, respectively. Results: In the literature review, 28 papers in the field of medical emergency were included. These studies were collected and selected according to established criteria. Moreover, we proposed a taxonomy using six groups of applications. In total, 324 mobile apps were found, with 192 identified in the Google Play Store and 132 identified in the Apple App Store. Conclusions: We found that all apps in the Google Play Store were free, and 73 apps in the Apple App Store were paid, with the price ranging from US $0.89 to US $5.99. Moreover, 39% (11/28) of the included studies were related to warning systems for emergency services and 21% (6/28) were associated with disaster management apps.