HealthSit: Designing Posture-Based Interaction to Promote Exercise during Fitness Breaks

This research was motivated by a desire to help office workers change their sedentary behavior because a prolonged sedentary posture increases the risks of developing musculoskeletal injuries and chronic diseases, thus threatening their physical and psychological well-being. Regular breaks involving low-effort physical activities are effective in reducing the adverse impacts of inactive behaviors. In this article, we present the design of a posture-based interactive system called HealthSit, which was developed to promote a short lower-back stretching exercise during work breaks. Through a within-subject study involving 30 office workers, the effectiveness of HealthSit in facilitating the stretching exercise was examined by making comparisons between an interaction-aided, a guided, and a self-directed exercise mode. We also used HealthSit as a research probe to investigate the interactivity of the system in enhancing user experience and the psychological benefits of the fitness breaks. Compared with the other two modes, the interaction-aided exercise mode significantly improved the quality of the stretching exercise and enhanced motivation and emotional state. These results confirm the effectiveness of HealthSit in supporting fitness breaks as a new workplace technology. Based on our study, a set of design implications have been derived for technology-assisted fitness work breaks.

Synthèse multimodèle d'un régulateur polynomial robuste

Yves Henri Philippe Piguet

Two new approaches are proposed for the multimodel synthesis of a two-degrees-of-freedom polynomial controller : the multimodel pole placement and the multiobjective optimisation. Using several models enables taking into account the parametric variations of the controlled system without introducing conservativeness, be they due to modelling mismatch or changes in the operating conditions. The resulting controller is robust with respect to stability and performances. Multimodel pole placement consists in specifying the closed-loop pole position corresponding to the whole set of models. It is shown that only an approximate pole placement is possible. The distances between the desired and the actual poles are weighted to penalize more the error on the poles which have the most effect on the stability and the performances, and minimized with the least-squares method. An iterative approach is proposed for improving the performances, and an interactive computer-aided design program is developed that enables the user ﬁnding easily which compromises are possible between performances and robustness. In multiobjective optimisation, one considers a set of performance criteria which should be made as close as possible to goal values, also for several models. This results in a minimax problem. Taking into account the H∞ norm of the sensitivity guarantees robust stability. Many different objectives can be included, such as limits on the time- and frequency-domain responses. The result is a compromise between all the speciﬁcations, which is often the purpose of the design. Depending on the optimisation method used, local minima may prevent from reaching the global optimum ; however, this method can signiﬁcantly improve an existing solution. Both approaches are applied to the design of a controller for a ßexible arm. This system exhibits poorly damped modes and large parametric variations. The speciﬁcations concern robust stability and performances. Multimodel pole placement is used to obtain a controller which satisﬁes all of them. Then multiobjective optimisation enables improving the whole set of performance criteria, or more speciﬁcally reducing the amplitude of the control signal.

EPFL1997

Design and User Perception Issues for Personality-Engaged Recommender Systems

Rong Hu

Recommender systems have emerged, as an intelligent information filtering tool, to help users effectively identify information items of interest from a set of overwhelming choices and provide personalized services. Most recommendation technologies typically rely on ratings or item attributes to generate recommendations. Studies show that personality influences people's decision making processes and interests. However, little research has ventured into the area of incorporating personality into recommender systems. The utilization of personality characteristics in recommender systems and the exploration of user perceptions of personality-engaged recommendation technologies are the two main concerns of this thesis. In all of our studies, the five factor model (FFM), one of the most widely employed personality models, was adopted to establish user personality profiles. Firstly, we have aimed at designing effective personality-engaged recommender systems, with the emphasis on how to integrate personality into the recommendation generation framework, and how to utilize personality information to address the problems that exist in current recommender systems. We implemented a personality-based music recommender system prototype based on the findings from prior psychological studies. This system builds user personality profiles by means of personality quizzes, and it accordingly predicts their musical preferences and makes music recommendations. This system prototype was employed in our later user study investigating user perceptions of this novel recommendation technology. Moreover, we investigated how to incorporate personality into collaborative filtering recommender systems with the purpose of alleviating new user and dataset sparsity problems. In contrast to the traditional rating-based collaborative filtering method, the proposed three variants which take personality characteristics into account significantly improve the prediction accuracy in both cold-start scenarios (i.e., new user & dataset sparsity). Furthermore, in order to generalize the personality-engaged recommendation technology to other item domains, we used matrix factorization methods to automatically discover the links between personality traits and items. Consequently, recommendations were generated based on the discovered relationships. The empirical results show that the proposed methods achieve superior performances compared to other tested methods. In particular, the proposed method can produce highly accurate recommendations, even though no rating is available. It has been demonstrated that the proposed method can be applied to design effective gift recommender systems where rating information is hard to obtain. Secondly, we have conducted two user studies with the aim of investigating user perceptions of personality-engaged recommender systems. One compares a personality-quiz based movie recommender system with a baseline rating-based recommender system, and identifies the factors which lead to user acceptance to the personality-based system. The results show that the perceived accuracy in the two systems is not significantly different. However, users expended significantly less effort, both perceived cognitive effort and actual completion time, to establish their initial preference profiles in the personality quiz-based system than in the rating-based system. Additionally, users expressed stronger intentions to reuse the personality quiz-based system and introduce it to their friends. The other user study investigates the influence of contexts on the user perceptions of the personality-engaged recommender systems. We have examined two contextual factors. One is users' usage goals, finding items for the active user himself/herself or a friend as gifts. The other is the level of user domain knowledge. Our in-depth user studies show that while users perceived that the recommended items for their friends were more accurate, they enjoyed more using the personality-based recommender to find items for themselves than for their friends. Additionally, it has been found that the domain knowledge has a significant impact on user perceptions of the system. The results show that novice users, who are not knowledgeable about music, appreciated the personality-based recommender more than musical experts did. In the end, a set of design guidelines is derived from all of the experimental results. They should be helpful for designing satisfying and effective personality-engaged recommender systems.

EPFL2012

Evaluating recommender systems from the user's perspective: survey of the state of the art

Li Chen, Rong Hu, Pearl Pu Faltings

A recommender system is a Web technology that proactively suggests items of interest to users based on their objective behavior or explicitly stated preferences. Evaluations of recommender systems (RS) have traditionally focused on the performance of algorithms. However, many researchers have recently started investigating system effectiveness and evaluation criteria from users' perspectives. In this paper, we survey the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS's ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms finally, we examine how these system design features influence users' adoption of the technology. We summarize existing work concerning three crucial interaction activities between the user and the system: the initial preference elicitation process, the preference refinement process, and the presentation of the system's recommendation results. Additionally, we will also cover recent evaluation frameworks that measure a recommender system's overall perceptive qualities and how these qualities influence users' behavioral intentions. The key results are summarized in a set of design guidelines that can provide useful suggestions to scholars and practitioners concerning the design and development of effective recommender systems. The survey also lays groundwork for researchers to pursue future topics that have not been covered by existing methods.