Predicting re-finding activity and difficulty

In this study, we address the problem of identifying if users are attempting to re-find information and estimating the level of difficulty of the re- finding task. We propose to consider the task information (e.g. multiple queries and click information) rather than only queries. Our resultant prediction models are shown to be significantly more accurate (by 2%) than the current state of the art. While past research assumes that previous search history of the user is available to the prediction model, we examine if re-finding detection is possible without access to this information. Our evaluation indicates that such detection is possible, but more challenging. We further describe the first predictive model in detecting re-finding difficulty, showing it to be significantly better than existing approaches for detecting general search difficulty

User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms

[EN] Establishing the best layout configuration for software-generated interfaces and control panels is a complex problem when they include many controls and indicators. Several methods have been developed for arranging the interface elements; however, the results are usually conceptual designs that must be manually adjusted to obtain layouts valid for real situations. Based on these considerations, in this work we propose a new automatized procedure to obtain optimal layouts for software-based interfaces. Eye-tracking and mouse-tracking data collected during the use of the interface is used to obtain the best configuration for its elements. The solutions are generated using a slicing-trees based genetic algorithm. This algorithm is able to obtain really applicable configurations that respect the geometrical restrictions of elements in the interface. Results show that this procedure increases effectiveness, efficiency and satisfaction of the users when they interact with the obtained interfaces.This work was supported by the Programa estatal de investigacion, desarrollo e innovacion orientada a los retos de la sociedad of the Government of Spain under Grant DPI 2016-79042-R.Diego-Mas, JA.; Garzon Leal, D.; Poveda Bautista, R.; Alcaide Marzal, J. (2019). User-interfaces layout optimization using eye-tracking, mouse movements and genetic algorithms. Applied Ergonomics. 78:197-209. https://doi.org/10.1016/j.apergo.2019.03.004S1972097

Nuevas tecnologías aplicadas a la ergonomía ocupacional. Empleo de sensores RGBD y EyeTracking en la mejora ergonómica de puestos de trabajo

Tesis por compendio[ES] A mediados del siglo XX inició en el continente europeo, especialmente en Francia y Bélgica, una nueva disciplina denominada Ergonomía Centrada en la Actividad. Esta disciplina está enfocada en el análisis del trabajo con el fin de optimizar las condiciones laborales. El propósito de la intervención ergonómica es mejorar componentes que interactúan en el sistema o en la actividad del trabajo (las personas, la organización, la tecnología y el ambiente), interrelacionando aspectos de salud, seguridad, productividad y calidad. En investigaciones anteriores a la presente el doctorando identificó la necesidad de mejora de las herramientas empleadas por los profesionales para la evaluación de factores de riesgo ergonómico, motivando así el desarrollo de la presente investigación. Inicialmente se analizaron métodos generales empleados por los diseñadores para establecer aquellos susceptibles de mejora con la introducción de nuevas tecnologías. En una segunda fase se identificaron y analizaron dispositivos tecnológicos orientados al rastreo de la actividad humana aplicables en el ámbito de las metodologías de la ergonomía ocupacional para los sectores productivos. Como resultado se concluyó que los sensores RGB-D y el Eye-Tracking (rastreo ocular) son dispositivos aplicables para ayudar a mejorar las condiciones en los puestos de trabajo, el primero para distribuir las áreas de trabajo y el segundo para la mejora de las interfaces del usuario. En esta TD se desarrollaron técnicas y métodos para el empleo de estos dispositivos logrando el diseño ergonómico de puestos de trabajo con aplicación práctica (artículos 2 y 3). Durante la realización de esta TD, bajo las directrices del Programa de Doctorado en Tecnologías para la Salud y el Bienestar de la Universidad Politécnica de Valencia, se publicaron tres artículos en revistas que durante el año de su publicación estuvieron indexadas en el primer cuartil de su categoría en el Journal Citation Report, las cuáles sustentan los resultados de la investigación. En ellos se evidenció cómo los avances tecnológicos implementados en Ergonomía producen cambios importantes en el diseño de los puestos de trabajo, y minimizan los tiempos y los movimientos que se requieren en las diferentes actividades laborales, garantizando así una ubicación óptima del recurso humano en los sistemas de producción y generando a su vez estrategias que disminuyen los Trastornos Músculo Esqueléticos (TMEs).[CA] A mitjan segle XX es va desenvolupar en el continent europeu, especialment a França i Bèlgica, una nova disciplina denominada Ergonomia Centrada en l'Activitat. Aquesta disciplina està enfocada en l'anàlisi del treball amb la finalitat d'optimitzar les condicions laborals. El propòsit de la intervenció ergonòmica és millorar els components que interactuen en el sistema o en l'activitat del treball (les persones, l'organització, la tecnologia i l'ambient), interrelacionant els aspectes de salut, seguretat, productivitat i qualitat. En investigacions anteriors a la present el doctorand va identificar la necessitat de millora de les eines emprades pels professionals per a l'avaluació de factors de risc ergonòmic, motivant així el desenvolupament de la present investigació. Inicialment es van analitzar els mètodes habituals empleats pels ergónoms per a establir aquells susceptibles de millora amb la introducció de noves tecnologies. En una segona fase es va identificar i analitzar els dispositius tecnològics orientats al monitoratge de l'activitat humana aplicables en l'àmbit de les metodologies de l'ergonomia ocupacional per als sectors productius. Com a resultat es va concloure que els sensors RGB-D i el Eye-Tracking són dispositius aplicables per a ajudar a millorar les condicions en els llocs de treball, el primer per a distribuir les àrees de treball i el segon per a la millora de les interfícies de l'usuari. En aquesta TD es van desenvolupar tècniques i procediments per a l'ús d'aquests dispositius aconseguint el disseny ergonòmic de llocs de treball amb aplicació pràctica (articles 2 i 3). Durant la realització d'aquesta TD, sota les directrius del Programa de Doctorat en Tecnologies per a la Salut i el Benestar de la Universitat Politècnica de València, es van publicar tres articles en revistes, que durant l'any de la seua publicació, van estar indexades en el primer quartil de la seua categoria en el Journal Citation Report, les quals sustenten els resultats de la investigació. En ells es va evidenciar com els avanços tecnològics implementats en Ergonomia produeixen canvis importants en el disseny dels llocs de treball, minimitzen els temps i els moviments que es requereixen en les diferents activitats laborals, garantint així una ubicació òptima del recurs humà en els sistemes de producció i generant al seu torn estratègies que disminueixen els Trastorns Músculesquelètics.[EN] Around the middle of XX century in Europe, especially in France and Belgium, a new discipline named Activity Focused Ergonomics was developed. This concept is based on the task analysis aiming to optimize working conditions. The purpose of the ergonomic intervention is to improve the components interacting within the system or the work activities (people, organization, technology and environment), correlating all health, safety, productivity and quality facts. In previous research, the ergomists identified the improvement need of those tools used by professionals to assess ergonomic risk factors, enhancing therefore the development of this present research. In the beginning, regular methods used by ergonomics specilists were analized in order to define those that could be improved with the introduction of new technologies. In a second phase, technological devices aimed to monitorize human activity, as well as those applicable for the occupational ergonomics methodologies in productive sectors were identified and analyzed. The conclusion that came as a result was that RGB-D sensors and Eye-Tracking are actual workstations conditions' improving devices, the first one is used to better organize working areas and the second one to optimize the user's interfaces. This Doctoral Thesis develops techniques and procedures to correctly use these devices obtaining workstations ergonomic designs with practical applications (articles 2 and 3). Along the development of this Doctoral Thesis, under the Polytechnic University of Valencia Doctoral School for Health and Wellbeing Technologies program directions, three articles were published in magazines which, throughout their publication year were indexed within its cathegory's first quartile at the Journal Citation Report, supporting the research results. The articles evidenced how the technological progress implemented in Ergonomics produce important changes in the workstations design and minimize times and movements required in different working activities, ensuring human resources' optimal location within the production systems and also developing Musculoskeletal Disorders reduction strategies.Garzón Leal, DC. (2020). Nuevas tecnologías aplicadas a la ergonomía ocupacional. Empleo de sensores RGBD y EyeTracking en la mejora ergonómica de puestos de trabajo [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156052TESISCompendi

Identification of re-finding tasks and search difficulty

We address the problem of identifying if users are attempting to re-find information and estimating the level of difficulty of the re-finding task. Identifying re-finding tasks and detecting search difficulties will enable search engines to respond dynamically to the search task being undertaken. To this aim, we conduct user studies and query log analysis to make a better understanding of re-finding tasks and search difficulties. Computing features particularly gathered in our user studies, we generate training sets from query log data, which is used for constructing automatic identification (prediction) models. Using machine learning techniques, our built re-finding identification model, which is the first model at the task level, could significantly outperform the existing query-based identifications. While past research assumes that previous search history of the user is available to the prediction model, we examine if re-finding detection is possible without access to this information. Our evaluation indicates that such detection is possible, but more challenging. We further describe the first predictive model in detecting re-finding difficulty, showing it to be significantly better than existing approaches for detecting general search difficulty. We also analyze important features for both identifications of re-finding and difficulties. Next, we investigate detailed identification of re-finding tasks and difficulties in terms of the type of the vertical document to be re-found. The accuracy of constructed predictive models indicates that re-finding tasks are indeed distinguishable across verticals and in comparison to general search tasks. This illustrates the requirement of adapting existing general search techniques for the re-finding context in terms of presenting vertical-specific results. Despite the overall reduction of accuracy in predictions independent of the original search of the user, it appears that identifying “image re-finding” is less dependent on such past information. Investigating the real-time prediction effectiveness of the models show that predicting ``image'' document re-finding obtains the highest accuracy early in the search. Early predictions would benefit search engines with adaptation of search results during re-finding activities. Furthermore, we study the difficulties in re-finding across verticals given some of the established indications of difficulties in the general web search context. In terms of user effort, re-finding “image” vertical appears to take more effort in terms of number of queries and clicks than other investigated verticals, while re-finding “reference” documents seems to be more time consuming when there is a longer time gap between the re-finding and corresponding original search. Exploring other features suggests that there could be particular difficulty indications for the re-finding context and specific to each vertical. To sum up, this research investigates the issue of effectively supporting users with re-finding search tasks. To this end, we have identified features that allow for more accurate distinction between re-finding and general tasks. This will enable search engines to better adapt search results for the re-finding context and improve the search experience of the users. Moreover, features indicative of similar/different and easy/difficult re-finding tasks can be employed for building balanced test environments, which could address one of the main gaps in the re-finding context