The Effectiveness of Alert Sounds for Electric Vehicles Based on Pedestrians' Perception

[EN] One of the largest problems with electric vehicles is that they often go unnoticed by pedestrians due to the absence of noticeable noise generated by electric motors, which is a potential cause of accidents and collisions. Surprisingly, this positive property in terms of reducing the noise pollution is in fact becoming a road safety problem. In addition, with the promotion of electric traction vehicles due to new environmental policies and the current proliferation of personal mobility vehicles, this problem could even be increased in the coming years. Therefore, the future global road regulation has included aspects on noise and warning sounds that electric vehicles must emit in the years to come. However, despite the requirements, no specific signal type or many other features have been established. Only the emission levels have been set (56-75 dB). Consequently, within the framework of this problem, this article evaluates the acoustic characteristics of the sound that should he emitted by electric vehicles so that pedestrians can easily detect them and the optimal sound pressure level they should emit to not unnecessarily raise noise pollution levels, concluding that the emission limits established are excessive in certain scenarios and that optimal warning sounds must be focused on electronically imitating combustion engine noises.This work was supported by the Universitat Politecnica de Valencia through its Internal Project Equipos de deteccion, regulacion e informacion en el sector de los sistemas inteligentes de transporte (ITS)-(20170764). The Associate Editor for this article was S. Hamdar.Mocholí Belenguer, F.; Martinez-Millana, A.; Castells, F.; Mocholí Salcedo, A. (2022). The Effectiveness of Alert Sounds for Electric Vehicles Based on Pedestrians' Perception. IEEE Transactions on Intelligent Transportation Systems. 23(4):2956-2965. https://doi.org/10.1109/TITS.2020.30254992956296523

Vehicle modeling for the analysis of the response of detectors based on inductive loops

[EN] Magnetic loops are one of the most popular and used traffic sensors because of their widely extended technology and simple mode of operation. Nevertheless, very simple models have been traditionally used to simulate the effect of the passage of vehicles on these loops. In general, vehicles have been considered simple rectangular metal plates located parallel to the ground plane at a certain height close to the vehicle chassis. However, with such a simple model, it is not possible to carry out a rigorous study to assess the performance of different models of vehicles with the aim of obtaining basic parameters such as the vehicle type, its speed or its direction in traffic. For this reason and because computer simulation and analysis have emerged as a priority in intelligent transportation systems (ITS), this paper aims to present a more complex vehicle model capable of characterizing vehicles as multiple metal plates of different sizes and heights, which will provide better results in virtual simulation environments. This type of modeling will be useful when reproducing the actual behavior of systems installed on roads based on inductive loops and will also facilitate vehicle classification and the extraction of basic traffic parameters.This research has been funded by the Universitat Politecnica de Valencia through its internal project `Detection, regulation and information equipment in the sector of intelligent transport systems (ITS). New models and tests of compatibility and verification of operation ' (20170764), which has been carried out at the ITACA Institute. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Mocholí-Belenguer, F.; Martinez-Millana, A.; Mocholí Salcedo, A.; Milián Sánchez, V. (2019). Vehicle modeling for the analysis of the response of detectors based on inductive loops. PLoS ONE. 14(9):1-28. https://doi.org/10.1371/journal.pone.0218631S128149Anderson, R. L. (1970). Electromagnetic loop vehicle detectors. IEEE Transactions on Vehicular Technology, 19(1), 23-30. doi:10.1109/t-vt.1970.23428Prucha MJ, and View M. Inductive loop vehicle presence detector. U.S. Patent 3 576 525, Apr. 27, 1971.Koerner RJ, and Park C. Inductive loop vehicle detector. U.S. Patent 3 989 932, Nov. 2, 1976.Patrick HM, and Raymond JL. Vehicle presence loop detector. U.S. Patent 4 472 706, Sep. 18, 1984.Clark MAG. Induction loop vehicle detector. U.S. Patent 4 568 937, Feb. 4, 1986.Liu, K., Jia, J., Zuo, Z., & Ando, R. (2018). Heterogeneity in the effectiveness of cooperative crossing collision prevention systems. Transportation Research Part C: Emerging Technologies, 87, 1-10. doi:10.1016/j.trc.2017.12.013Peng, Y., Jiang, Y., Lu, J., & Zou, Y. (2018). Examining the effect of adverse weather on road transportation using weather and traffic sensors. PLOS ONE, 13(10), e0205409. doi:10.1371/journal.pone.0205409Liu, K., Cui, M.-Y., Cao, P., & Wang, J.-B. (2016). Iterative Bayesian Estimation of Travel Times on Urban Arterials: Fusing Loop Detector and Probe Vehicle Data. PLOS ONE, 11(6), e0158123. doi:10.1371/journal.pone.0158123Ki, Y.-K., & Baik, D.-K. (2006). Vehicle-Classification Algorithm for Single-Loop Detectors Using Neural Networks. IEEE Transactions on Vehicular Technology, 55(6), 1704-1711. doi:10.1109/tvt.2006.883726Zheng, Z., Wang, C., Wang, P., Xiong, Y., Zhang, F., & Lv, Y. (2018). Framework for fusing traffic information from social and physical transportation data. PLOS ONE, 13(8), e0201531. doi:10.1371/journal.pone.0201531Pursula M and Kosonen I. Microprocessor and PC-based vehicle classification equipments using induction loops. Proceedings of the IEEE Second International Conference on Road Traffic Monitoring and Control; pp. 24–28, 1989.Gajda J, Sroka R, Stencel M, Wajda A, and Zeglen T. A vehicle classification based on inductive loop detectors. Proceedings of the IEEE Instrumentation and Measurement Technology Conference, Budapest; pp. 460–464, 2001.Nihan, N. L. (2000). Evaluation of forced flows on freeways with single-loop detectors. Journal of Advanced Transportation, 34(2), 269-296. doi:10.1002/atr.5670340206Ametha J, Tumer S, and Darbha S. Formulation of a new methodology to identify erroneous paired loop detectors. Proceedings of the IEEE Intelligent Transportation Systems, Oakland; pp. 591–596, 2001.Ki, Y.-K., & Baik, D.-K. (2006). Model for Accurate Speed Measurement Using Double-Loop Detectors. IEEE Transactions on Vehicular Technology, 55(4), 1094-1101. doi:10.1109/tvt.2006.877462Tang, J., Zou, Y., Ash, J., Zhang, S., Liu, F., & Wang, Y. (2016). Travel Time Estimation Using Freeway Point Detector Data Based on Evolving Fuzzy Neural Inference System. PLOS ONE, 11(2), e0147263. doi:10.1371/journal.pone.0147263Tok A, Hernandez SV, and Ritchie SG. Accurate individual vehicle speeds from single inductive loop signatures. Proceedings of 88th Annual Meeting of the Transportation Research Board, National Research Council, Washington, D.C, USA, 2009, paper 09–3512.Hilliard SR. Vehicle speed estimation using inductive vehicle detection systems. United States Patent 6999886, Feb. 2003.Gajda, J., Piwowar, P., Sroka, R., Stencel, M., & Zeglen, T. (2012). Application of inductive loops as wheel detectors. Transportation Research Part C: Emerging Technologies, 21(1), 57-66. doi:10.1016/j.trc.2011.08.010Marszalek Z, Sroka R, Zeglen T. Inductive loop for vehicle axle detection from first concepts to the system based on changes in the sensor impedance components. Proceedings of 20th international conference on methods and models in automation and robotics, 24–27, August 2015, Miedzyzdroje, Poland, pp 765–769.Arroyo Núñez JH, Mocholí Salcedo A, Barrales Guadarrama R, and Arroyo Nuñez A. Communication between magnetic loops. Proceedings of 16th World Road Meeting, Lisbon, Portugal, May 2010.Gajda J and Burnos P. Identification of the spatial impulse response of inductive loop detectors. IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015, pp. 1997–2002.Klein LA, Gibson DRP, and Mills MK. Traffic Detector Handbook. FHWAHRT-06-108. Federal Highway Administration, U.S. Department of Transportation 2006.Mills MK. Inductive loop system equivalent circuit model. Proceedings of the 39th Vehicular Technology Conference, May 1989, pp. 689–700.Mills MK. Self-Inductance Formulas for Multi- Turn Rectangular Loops Used with Vehicle Detectors. 33rd IEEE VTG Conference Record, May 1983, pp. 64–73.Mocholi-Salcedo, A., Arroyo-Nunez, J. H., Milian-Sanchez, V. M., Palomo-Anaya, M. J., & Arroyo-Nunez, A. (2017). Magnetic Field Generated by the Loops Used in Traffic Control Systems. IEEE Transactions on Intelligent Transportation Systems, 18(8), 2126-2136. doi:10.1109/tits.2016.2632972Mocholí Belenguer, F., Mocholí Salcedo, A., Guill Ibañez, A., & Milián Sánchez, V. (2019). Advantages offered by the double magnetic loops versus the conventional single ones. PLOS ONE, 14(2), e0211626. doi:10.1371/journal.pone.0211626Chen, F., Chen, S., & Ma, X. (2018). Analysis of hourly crash likelihood using unbalanced panel data mixed logit model and real-time driving environmental big data. Journal of Safety Research, 65, 153-159. doi:10.1016/j.jsr.2018.02.010Ma, X., Chen, S., & Chen, F. (2017). Multivariate space-time modeling of crash frequencies by injury severity levels. Analytic Methods in Accident Research, 15, 29-40. doi:10.1016/j.amar.2017.06.00

Evaluating the Social Media Performance of Hospitals in Spain: A Longitudinal and Comparative Study

[EN] BACKGROUND: Social media is changing the way in which citizens and health professionals communicate. Previous studies have assessed the use of Health 2.0 by hospitals, showing clear evidence of growth in recent years. In order to understand if this happens in Spain, it is necessary to assess the performance of health care institutions on the Internet social media using quantitative indicators. OBJECTIVES: The study aimed to analyze how hospitals in Spain perform on the Internet and social media networks by determining quantitative indicators in 3 different dimensions: presence, use, and impact and assess these indicators on the 3 most commonly used social media - Facebook, Twitter, YouTube. Further, we aimed to find out if there was a difference between private and public hospitals in their use of the aforementioned social networks. METHODS: The evolution of presence, use, and impact metrics is studied over the period 2011- 2015. The population studied accounts for all the hospitals listed in the National Hospitals Catalog (NHC). The percentage of hospitals having Facebook, Twitter, and YouTube profiles has been used to show the presence and evolution of hospitals on social media during this time. Usage was assessed by analyzing the content published on each social network. Impact evaluation was measured by analyzing the trend of subscribers for each social network. Statistical analysis was performed using a lognormal transformation and also using a nonparametric distribution, with the aim of comparing t student and Wilcoxon independence tests for the observed variables. RESULTS: From the 787 hospitals identified, 69.9% (550/787) had an institutional webpage and 34.2% (269/787) had at least one profile in one of the social networks (Facebook, Twitter, and YouTube) in December 2015. Hospitals' Internet presence has increased by more than 450.0% (787/172) and social media presence has increased ten times since 2011. Twitter is the preferred social network for public hospitals, whereas private hospitals showed better performance on Facebook and YouTube. The two-sided Wilcoxon test and t student test at a CI of 95% show that the use of Twitter distribution is higher (P<.001) for private and public hospitals in Spain, whereas other variables show a nonsignificant different distribution. CONCLUSIONS: The Internet presence of Spanish hospitals is high; however, their presence on the 3 main social networks is still not as high compared to that of hospitals in the United States and Western Europe. Public hospitals are found to be more active on Twitter, whereas private hospitals show better performance on Facebook and YouTube. This study suggests that hospitals, both public and private, should devote more effort to and be more aware of social media, with a clear strategy as to how they can foment new relationships with patients and citizens.The authors wish to acknowledge the ITACA Institute (Universitat Politècnica de València) for making possible the publication of this paper through the Excellence Support program for the publication in high-impact international journals.S11119

Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements

[EN] There are currently different types of commercial optical disdrometers to measure the rainfall intensity, of which many are commonly used for monitoring road conditions. Having information about the amount of rain, the composition of the precipitation particles and visibility are essential to avoid accidents, which requires intelligent systems that warn drivers and redirect traffic. However, few studies related to Intelligent Transport Systems (ITS) have been performed regarding why these devices are not optimized for this type of applications. Therefore, this paper analyzes and evaluates the operating mode of these equipment through their theoretical model, which will allow the design of prototypes of disdrometers with different characteristics. In addition, this model will be implemented in a simulation program, through which an exhaustive study analyzing how the type of precipitation and its intensity affect the measures provided by the model will be conducted. In this way, the results will help optimize its operation to be thus used in urban settings, which will allow obtaining more accurate real-time information, better traffic management, and a reduction in the number of accidents.This research has been funded by the Universitat Politecnica de Valencia through its internal project "Equipos de deteccion, regulacion e informacion en el sector de los sistemas inteligentes de transporte (ITS). Nuevos modelos y ensayos de compatibilidad y verificacion de funcionamiento", which has been carried out at the ITACA Institute.Mocholí-Belenguer, F.; Martinez-Millana, A.; Mocholí Salcedo, A.; Milián Sánchez, V.; Palomo-Anaya, MJ. (2020). Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements. Symmetry (Basel). 12(2):1-19. https://doi.org/10.3390/sym12020303S119122Frasson, R. P. de M., da Cunha, L. K., & Krajewski, W. F. (2011). Assessment of the Thies optical disdrometer performance. Atmospheric Research, 101(1-2), 237-255. doi:10.1016/j.atmosres.2011.02.014Krajewski, W. F., Kruger, A., Caracciolo, C., Golé, P., Barthes, L., Creutin, J.-D., … Vinson, J.-P. (2006). DEVEX-disdrometer evaluation experiment: Basic results and implications for hydrologic studies. Advances in Water Resources, 29(2), 311-325. doi:10.1016/j.advwatres.2005.03.018Peng, Y., Jiang, Y., Lu, J., & Zou, Y. (2018). Examining the effect of adverse weather on road transportation using weather and traffic sensors. PLOS ONE, 13(10), e0205409. doi:10.1371/journal.pone.0205409Rakha, H., Arafeh, M., & Park, S. (2012). Modeling Inclement Weather Impacts on Traffic Stream Behavior. International Journal of Transportation Science and Technology, 1(1), 25-47. doi:10.1260/2046-0430.1.1.25Malin, F., Norros, I., & Innamaa, S. (2019). Accident risk of road and weather conditions on different road types. Accident Analysis & Prevention, 122, 181-188. doi:10.1016/j.aap.2018.10.014Lolli, S., Di Girolamo, P., Demoz, B., Li, X., & Welton, E. J. (2017). Rain Evaporation Rate Estimates from Dual-Wavelength Lidar Measurements and Intercomparison against a Model Analytical Solution. Journal of Atmospheric and Oceanic Technology, 34(4), 829-839. doi:10.1175/jtech-d-16-0146.1Grossklaus, M., Uhlig, K., & Hasse, L. (1998). An Optical Disdrometer for Use in High Wind Speeds. Journal of Atmospheric and Oceanic Technology, 15(4), 1051-1059. doi:10.1175/1520-0426(1998)0152.0.co;2Hauser, D., Amayenc, P., Nutten, B., & Waldteufel, P. (1984). A New Optical Instrument for Simultaneous Measurement of Raindrop Diameter and Fall Speed Distributions. Journal of Atmospheric and Oceanic Technology, 1(3), 256-269. doi:10.1175/1520-0426(1984)0012.0.co;2Kathiravelu, G., Lucke, T., & Nichols, P. (2016). Rain Drop Measurement Techniques: A Review. Water, 8(1), 29. doi:10.3390/w8010029Illingworth, A. J., & Stevens, C. J. (1987). An Optical Disdrometer for the Measurement of Raindrop Size Spectra in Windy Conditions. Journal of Atmospheric and Oceanic Technology, 4(3), 411-421. doi:10.1175/1520-0426(1987)0042.0.co;2Atlas, D., & Ulbrich, C. W. (1977). Path- and Area-Integrated Rainfall Measurement by Microwave Attenuation in the 1–3 cm Band. Journal of Applied Meteorology, 16(12), 1322-1331. doi:10.1175/1520-0450(1977)0162.0.co;2Foote, G. B., & Du Toit, P. S. (1969). Terminal Velocity of Raindrops Aloft. Journal of Applied Meteorology, 8(2), 249-253. doi:10.1175/1520-0450(1969)0082.0.co;2Pruppacher, H. R., & Pitter, R. L. (1971). A Semi-Empirical Determination of the Shape of Cloud and Rain Drops. Journal of the Atmospheric Sciences, 28(1), 86-94. doi:10.1175/1520-0469(1971)0282.0.co;2Hasse, L., Grossklaus, M., Uhlig, K., & Timm, P. (1998). A Ship Rain Gauge for Use in High Wind Speeds. Journal of Atmospheric and Oceanic Technology, 15(2), 380-386. doi:10.1175/1520-0426(1998)0152.0.co;2Marshall, J. S., & Palmer, W. M. K. (1948). THE DISTRIBUTION OF RAINDROPS WITH SIZE. Journal of Meteorology, 5(4), 165-166. doi:10.1175/1520-0469(1948)0052.0.co;2Ulbrich, C. W. (1983). Natural Variations in the Analytical Form of the Raindrop Size Distribution. Journal of Climate and Applied Meteorology, 22(10), 1764-1775. doi:10.1175/1520-0450(1983)0222.0.co;2Ulbrich, C. W. (1985). The Effects of Drop Size Distribution Truncation on Rainfall Integral Parameters and Empirical Relations. Journal of Climate and Applied Meteorology, 24(6), 580-590. doi:10.1175/1520-0450(1985)0242.0.co;2Gertzman, H. S., & Atlas, D. (1977). Sampling errors in the measurement of rain and hail parameters. Journal of Geophysical Research, 82(31), 4955-4966. doi:10.1029/jc082i031p04955Brawn, D., & Upton, G. (2008). Estimation of an atmospheric gamma drop size distribution using disdrometer data. Atmospheric Research, 87(1), 66-79. doi:10.1016/j.atmosres.2007.07.006Zhang, G., Vivekanandan, J., Brandes, E. A., Meneghini, R., & Kozu, T. (2003). The Shape–Slope Relation in Observed Gamma Raindrop Size Distributions: Statistical Error or Useful Information? Journal of Atmospheric and Oceanic Technology, 20(8), 1106-1119. doi:10.1175/1520-0426(2003)0202.0.co;2Peng, Y., Abdel-Aty, M., Lee, J., & Zou, Y. (2018). Analysis of the Impact of Fog-Related Reduced Visibility on Traffic Parameters. Journal of Transportation Engineering, Part A: Systems, 144(2), 04017077. doi:10.1061/jtepbs.0000094Alves de Souza, B., da Silva Rocha Paz, I., Ichiba, A., Willinger, B., Gires, A., Amorim, J. C. C., … Schertzer, D. (2018). Multi-hydro hydrological modelling of a complex peri-urban catchment with storage basins comparing C-band and X-band radar rainfall data. Hydrological Sciences Journal, 63(11), 1619-1635. doi:10.1080/02626667.2018.1520390Tabary, P., Boumahmoud, A.-A., Andrieu, H., Thompson, R. J., Illingworth, A. J., Bouar, E. L., & Testud, J. (2011). Evaluation of two «integrated» polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band. Journal of Hydrology, 405(3-4), 248-260. doi:10.1016/j.jhydrol.2011.05.02

Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process

[EN] The widespread adoption of real-time location systems is boosting the development of software applications to track persons and assets in hospitals. Among the vast amount of applications, real-time location systems in operating rooms have the advantage of grounding advanced data analysis techniques to improve surgical processes, such as process mining. However, such applications still find entrance barriers in the clinical context. In this paper, we aim to evaluate the preferred features of a process mining-based dashboard deployed in the operating rooms of a hospital equipped with a real-time location system. The dashboard allows to discover and enhance flows of patients based on the location data of patients undergoing an intervention. Analytic hierarchy process was applied to quantify the prioritization of the dashboard features (filtering data, enhancement, node selection, statistics, etc.), distinguishing the priorities that each of the different roles in the operating room service assigned to each feature. The staff in the operating rooms (n = 10) was classified into three groups: Technical, clinical, and managerial staff according to their responsibilities. Results showed different weights for the features in the process mining dashboard for each group, suggesting that a flexible process mining dashboard is needed to boost its potential in the management of clinical interventions in operating rooms. This paper is an extension of a communication presented in the Process-Oriented Data Science for Health Workshop in the Business Process Management Conference 2018.This project received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 812386.Martinez-Millana, A.; Lizondo, A.; Gatta, R.; Vera, S.; Traver Salcedo, V.; Fernández Llatas, C. (2019). Process Mining Dashboard in Operating Rooms: Analysis of Staff Expectations with Analytic Hierarchy Process. International Journal of Environmental research and Public Health. 16(2):1-14. https://doi.org/10.3390/ijerph16020199S114162Agnoletti, V., Buccioli, M., Padovani, E., Corso, R. M., Perger, P., Piraccini, E., … Gambale, G. (2013). Operating room data management: improving efficiency and safety in a surgical block. BMC Surgery, 13(1). doi:10.1186/1471-2482-13-7Marques, I., Captivo, M. E., & Vaz Pato, M. (2011). An integer programming approach to elective surgery scheduling. OR Spectrum, 34(2), 407-427. doi:10.1007/s00291-011-0279-7Haynes, A. B., Weiser, T. G., Berry, W. R., Lipsitz, S. R., Breizat, A.-H. S., Dellinger, E. P., … Gawande, A. A. (2009). A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population. New England Journal of Medicine, 360(5), 491-499. doi:10.1056/nejmsa0810119Dexter, F., Epstein, R. H., Traub, R. D., Xiao, Y., & Warltier, D. C. (2004). Making Management Decisions on the Day of Surgery Based on Operating Room Efficiency and Patient Waiting Times. Anesthesiology, 101(6), 1444-1453. doi:10.1097/00000542-200412000-00027Fernández-Llatas, C., Meneu, T., Traver, V., & Benedi, J.-M. (2013). Applying Evidence-Based Medicine in Telehealth: An Interactive Pattern Recognition Approximation. International Journal of Environmental Research and Public Health, 10(11), 5671-5682. doi:10.3390/ijerph10115671Westbrook, J. I., & Braithwaite, J. (2010). Will information and communication technology disrupt the health system and deliver on its promise? Medical Journal of Australia, 193(7), 399-400. doi:10.5694/j.1326-5377.2010.tb03968.xFisher, J. A., & Monahan, T. (2012). Evaluation of real-time location systems in their hospital contexts. International Journal of Medical Informatics, 81(10), 705-712. doi:10.1016/j.ijmedinf.2012.07.001Bath, P. A., Pendleton, N., Bracale, M., & Pecchia, L. (2011). Analytic Hierarchy Process (AHP) for Examining Healthcare Professionals’ Assessments of Risk Factors. Methods of Information in Medicine, 50(05), 435-444. doi:10.3414/me10-01-0028Lee, V. S., Kawamoto, K., Hess, R., Park, C., Young, J., Hunter, C., … Pendleton, R. C. (2016). Implementation of a Value-Driven Outcomes Program to Identify High Variability in Clinical Costs and Outcomes and Association With Reduced Cost and Improved Quality. JAMA, 316(10), 1061. doi:10.1001/jama.2016.12226Sloane, E. B., Liberatore, M. J., Nydick, R. L., Luo, W., & Chung, Q. B. (2003). Using the analytic hierarchy process as a clinical engineering tool to facilitate an iterative, multidisciplinary, microeconomic health technology assessment. Computers & Operations Research, 30(10), 1447-1465. doi:10.1016/s0305-0548(02)00187-9Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234-281. doi:10.1016/0022-2496(77)90033-5Bridges, J. F. P., Hauber, A. B., Marshall, D., Lloyd, A., Prosser, L. A., Regier, D. A., … Mauskopf, J. (2011). Conjoint Analysis Applications in Health—a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value in Health, 14(4), 403-413. doi:10.1016/j.jval.2010.11.013Proceedings of the 2011 annual conference on Human factors in computing systems - CHI ’11. (2011). doi:10.1145/1978942Anual Report 2014http://chguv.san.gva.es/documents/10184/81032/Informe_anual2014.pdf/713c6559-0e29-4838-967c-93380c24eff9Ratwani, R. M., Fairbanks, R. J., Hettinger, A. Z., & Benda, N. C. (2015). Electronic health record usability: analysis of the user-centered design processes of eleven electronic health record vendors. Journal of the American Medical Informatics Association, 22(6), 1179-1182. doi:10.1093/jamia/ocv050Van der Aalst, W. M. P., Reijers, H. A., Weijters, A. J. M. M., van Dongen, B. F., Alves de Medeiros, A. K., Song, M., & Verbeek, H. M. W. (2007). Business process mining: An industrial application. Information Systems, 32(5), 713-732. doi:10.1016/j.is.2006.05.00

Are Health Videos from Hospitals, Health Organizations, and Active Users Available to Health Consumers? An Analysis of Diabetes Health Video Ranking in YouTube

Health consumers are increasingly using the Internet to search for health information. The existence of overloaded, inaccurate, obsolete, or simply incorrect health information available on the Internet is a serious obstacle for finding relevant and good-quality data that actually helps patients. Search engines of multimedia Internet platforms are thought to help users to find relevant information according to their search. But, is the information recovered by those search engines from quality sources? Is the health information uploaded from reliable sources, such as hospitals and health organizations, easily available to patients? The availability of videos is directly related to the ranking position in YouTube search. The higher the ranking of the information is, the more accessible it is. The aim of this study is to analyze the ranking evolution of diabetes health videos on YouTube in order to discover how videos from reliable channels, such as hospitals and health organizations, are evolving in the ranking. The analysis was done by tracking the ranking of 2372 videos on a daily basis during a 30-day period using 20 diabetes-related queries. Our conclusions are that the current YouTube algorithm favors the presence of reliable videos in upper rank positions in diabetes-related searches.Fernández Llatas, C.; Traver Salcedo, V.; Borrás Morell, JE.; Martinez-Millana, A.; Karlsen, R. (2017). Are Health Videos from Hospitals, Health Organizations, and Active Users Available to Health Consumers? An Analysis of Diabetes Health Video Ranking in YouTube. Computational and Mathematical Methods in Medicine. 2017:1-9. doi:10.1155/2017/8194940S19201

A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards

[EN] The Internet of Things paradigm in healthcare has boosted the design of new solutions for the promotion of healthy lifestyles and the remote care. Thanks to the effort of academia and industry, there is a wide variety of platforms, systems and commercial products enabling the real-time information exchange of environmental data and people's health status. However, one of the problems of these type of prototypes and solutions is the lack of interoperability and the compromised scalability in large scenarios, which limits its potential to be deployed in real cases of application. In this paper, we propose a health monitoring system based on the integration of rapid prototyping hardware and interoperable software to build system capable of transmitting biomedical data to healthcare professionals. The proposed system involves Internet of Things technologies and interoperablility standards for health information exchange such as the Fast Healthcare Interoperability Resources and a reference framework architecture for Ambient Assisted Living UniversAAL.This research received no external funding. The APC was funded by Research group Information and Communication Technologies against Climate Change (!CTCC) of the Universitat Politecnica de Valencia, Spain.Lemus Zúñiga, LG.; Félix, JM.; Fides Valero, Á.; Benlloch-Dualde, J.; Martinez-Millana, A. (2022). A Proof-of-Concept IoT System for Remote Healthcare Based on Interoperability Standards. Sensors. 22(4):1-17. https://doi.org/10.3390/s2204164611722

Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems

[EN] Google Glass is a wearable sensor presented to facilitate access to information and assist while performing complex tasks. Despite the withdrawal of Google in supporting the product, today there are multiple applications and much research analyzing the potential impact of this technology in different fields of medicine. Google Glass satisfies the need of managing and having rapid access to real-time information in different health care scenarios. Among the most common applications are access to electronic medical records, display monitorizations, decision support and remote consultation in specialties ranging from ophthalmology to surgery and teaching. The device enables a user-friendly hands-free interaction with remote health information systems and broadcasting medical interventions and consultations from a first-person point of view. However, scientific evidence highlights important technical limitations in its use and integration, such as failure in connectivity, poor reception of images and automatic restart of the device. This article presents a technical study on the aforementioned limitations (specifically on the latency, reliability and performance) on two standard communication schemes in order to categorize and identify the sources of the problems. Results have allowed us to obtain a basis to define requirements for medical applications to prevent network, computational and processing failures associated with the use of Google Glass.Authors would like to acknowledge the Laboratory for the Analysis for Human Behavior (www.sabien.upv.es/lach) and the Operative Program FEDER 2007/2013, for providing the necessary materials to undertake the presented research. The work done by A.L. was funded by the Ministry of Economy and Competitiveness: Promoting Youth Employment Program and Implementation of the (PEJ-2014-A-06813) Youth Guarantee 2014. The subsidized activity is part of the National System of Youth Guarantee and are co-financed under the Operational Program for Youth Employment, with financial resources from the Initiative Youth Employment (IYE) and the European Social Fund (ESF) for the period 2014-2020.Martínez Millana, A.; Bayo Montón, JL.; Lizondo García, A.; Fernández Llatas, C.; Traver Salcedo, V. (2016). Evaluation of Google Glass Technical Limitations on Their Integration in Medical Systems. Sensors. 16(2142):1-12. https://doi.org/10.3390/s16122142S112162142Abrahams, E., Ginsburg, G. S., & Silver, M. (2005). The Personalized Medicine Coalition. American Journal of PharmacoGenomics, 5(6), 345-355. doi:10.2165/00129785-200505060-00002Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), e20. doi:10.2196/jmir.3.2.e20The Truth about Google X: An Exclusive Look Behind the Secretive Lab’s Closed Doorshttps://www.fastcompany.com/3028156/united-states-of-innovation/the-google-x-factorGlauser, W. (2013). Doctors among early adopters of Google Glass. Canadian Medical Association Journal, 185(16), 1385-1385. doi:10.1503/cmaj.109-4607Google Glass at Workhttps://developers.google.com/glass/Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012). A review of wearable sensors and systems with application in rehabilitation. Journal of NeuroEngineering and Rehabilitation, 9(1), 21. doi:10.1186/1743-0003-9-21Davis, C. R., & Rosenfield, L. K. (2015). Looking at Plastic Surgery through Google Glass. Plastic and Reconstructive Surgery, 135(3), 918-928. doi:10.1097/prs.0000000000001056Iversen, M. D., Kiami, S., Singh, K., Masiello, I., & von Heideken, J. (2016). Prospective, randomised controlled trial to evaluate the effect of smart glasses on vestibular examination skills. BMJ Innovations, 2(2), 99-105. doi:10.1136/bmjinnov-2015-000094Liebert, C. A., Zayed, M. A., Aalami, O., Tran, J., & Lau, J. N. (2016). Novel Use of Google Glass for Procedural Wireless Vital Sign Monitoring. Surgical Innovation, 23(4), 366-373. doi:10.1177/1553350616630142Longley, C., & Whitaker, D. (2015). Google Glass Glare: disability glare produced by a head-mounted visual display. Ophthalmic and Physiological Optics, 36(2), 167-173. doi:10.1111/opo.12264Trese, M. G. J., Khan, N. W., Branham, K., Conroy, E. B., & Moroi, S. E. (2016). Expansion of Severely Constricted Visual Field Using Google Glass. Ophthalmic Surgery, Lasers and Imaging Retina, 47(5), 486-489. doi:10.3928/23258160-20160419-15Jeroudi, O. M., Christakopoulos, G., Christopoulos, G., Kotsia, A., Kypreos, M. A., Rangan, B. V., … Brilakis, E. S. (2015). Accuracy of Remote Electrocardiogram Interpretation With the Use of Google Glass Technology. The American Journal of Cardiology, 115(3), 374-377. doi:10.1016/j.amjcard.2014.11.008Cicero, M. X., Walsh, B., Solad, Y., Whitfill, T., Paesano, G., Kim, K., … Cone, D. C. (2015). Do You See What I See? Insights from Using Google Glass for Disaster Telemedicine Triage. Prehospital and Disaster Medicine, 30(1), 4-8. doi:10.1017/s1049023x1400140xWu, T. S., Dameff, C. J., & Tully, J. L. (2014). Ultrasound-Guided Central Venous Access Using Google Glass. The Journal of Emergency Medicine, 47(6), 668-675. doi:10.1016/j.jemermed.2014.07.045Lewis, T. L., & Vohra, R. S. (2013). Smartphones make smarter surgeons. British Journal of Surgery, 101(4), 296-297. doi:10.1002/bjs.9328Albrecht, U.-V., von Jan, U., Kuebler, J., Zoeller, C., Lacher, M., Muensterer, O. J., … Hagemeier, L. (2014). Google Glass for Documentation of Medical Findings: Evaluation in Forensic Medicine. Journal of Medical Internet Research, 16(2), e53. doi:10.2196/jmir.3225Waxman, B. P. (2012). Medicine in small doses. ANZ Journal of Surgery, 82(11), 768-768. doi:10.1111/j.1445-2197.2012.06276.xKortuem, G., Bauer, M., & Segall, Z. (1999). Mobile Networks and Applications, 4(1), 49-58. doi:10.1023/a:1019122125996Zou, G., Gan, Y., Chen, Y., Zhang, B., Huang, R., Xu, Y., & Xiang, Y. (2014). Towards automated choreography of Web services using planning in large scale service repositories. Applied Intelligence, 41(2), 383-404. doi:10.1007/s10489-014-0522-4O’Brien, P. D., & Nicol, R. C. (1998). BT Technology Journal, 16(3), 51-59. doi:10.1023/a:1009621729979Muensterer, O. J., Lacher, M., Zoeller, C., Bronstein, M., & Kübler, J. (2014). Google Glass in pediatric surgery: An exploratory study. International Journal of Surgery, 12(4), 281-289. doi:10.1016/j.ijsu.2014.02.003Hwang, A. D., & Peli, E. (2014). An Augmented-Reality Edge Enhancement Application for Google Glass. 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Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews

[EN] Background: Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people & rsquo;s health. It is necessary to assess the current status on the application of AI towards the improvement of people's health in the domains defined by WHO's Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective: To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people's health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods: A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO's PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results: The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N=98) followed by Health Emergencies (N=16) and Better Health and Wellbeing (N=15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7%, N=28). The reviews featured analytics primarily over both public and private data sources (67.44%, N=87). The most used type of data was medical imaging (31.8%, N=41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4%, N=56), in which Support Vector Machine method was predominant (20.9%, N=27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4%, N=47). (...)Martinez-Millana, A.; Saez-Saez, A.; Tornero-Costa, R.; Azzopardi-Muscat, N.; Traver Salcedo, V.; Novillo-Ortiz, D. (2022). Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. International Journal of Medical Informatics. 166:1-12. https://doi.org/10.1016/j.ijmedinf.2022.10485511216

Non-Invasive Blood Glucose Sensor: A Feasibility Study

[EN] Diabetes is a chronic disease characterized by abnormal blood glucose levels which has short and long term complications. Management of diabetes relies on a regular control of blood glucose levels, commonly measured with invasive sensors, which are painful and cause patient discomfort. Scientific community is trying to develop noninvasive monitoring sensors to measure blood glucose continuously. Whereas previous work are focused on single methods and techniques, we present hereby a feasibility study of a non-invasive sensor integrating three different types of techniques: electromagnetic, acoustic speed and near infra-red spectroscopy. Our prototype is subject to different sources of bias, however, the cross-compensation of these three techniques can minimize the low performance of single-technique approaches. The results are promising and show the potential of using combined techniques for non-invasive blood glucose measurement.López Albalat, A.; Sanz Alaman, MB.; Dejoz Diez, MC.; Martinez-Millana, A.; Traver Salcedo, V. (2019). Non-Invasive Blood Glucose Sensor: A Feasibility Study. IEEE. 1179-1182. https://doi.org/10.1109/EMBC.2019.8857261S1179118