Luis-Felipe Rodríguez’s research while affiliated with Sonora Institute of Technology and other places

Background and objective: This paper presents Alzheed, a mobile application for monitoring patients with Alzheimer's disease at day centers as well as a set of design recommendations for the development of healthcare mobile applications. The Alzheed project was conducted at Day Center "Dorita de Ojeda" that is focused on the care of patients with Alzheimer's disease. Materials and methods: A software design methodology based on participatory design was employed for the design of Alzheed. This methodology is both iterative and incremental and consists of two main iterative stages: evaluation of low-fidelity prototypes and evaluation of high-fidelity prototypes. Low-fidelity prototypes were evaluated by 11 day center's healthcare professionals (involved in the design of Alzheed), whereas high-fidelity prototypes were evaluated using a questionnaire based on the technology acceptance model (TAM) by the same healthcare professionals plus 30 senior psychology undergraduate students uninvolved in the design of Alzheed. Results: Healthcare professional participants perceived Alzheed as extremely likely to be useful and extremely likely to be usable, whereas senior psychology undergraduate students perceived Alzheed as quite likely to be useful and quite likely to be usable. Particularly, the median and mode of the TAM questionnaire were 7 (extremely likely) for healthcare professionals and 6 (quite likely) for psychology students (for both constructs: perceived usefulness and perceived ease of use). One-sample Wilcoxon signed-rank tests were performed to confirm the significance of the median for each construct. Conclusions: From the experience of designing Alzheed, it can be concluded that co-designing with healthcare professionals leads to (i) fostering group endorsement, which prevents resistance to change and (ii) helps to meet the needs of both healthcare professionals and patients, guaranteeing the usefulness of the application. In addition, evaluation of mobile healthcare applications by users involved and uninvolved in the application's design process helps to improve the ease of use of the application.

In this paper we present a mechanism to model the influence of agents’ internal and external factors on the emotional evaluation of stimuli in computational models of emotions. We propose the modification of configurable appraisal dimensions (such as desirability and pleasure) based on influencing factors. As part of the presented mechanism, we introduce influencing models to define the relationship between a given influencing factor and a given set of configurable appraisal dimensions utilized in the emotional evaluation phase. Influencing models translate factors’ influences (on the emotional evaluation) into fuzzy logic adjustments (e.g., a shift in the limits of fuzzy membership functions), which allow biasing the emotional evaluation of stimuli. We implemented a proof-of-concept computational model of emotions based on real-world data about individuals’ emotions. The obtained empirical evidence indicates that the proposed mechanism can properly affect the emotional evaluation of stimuli while preserving the overall behavior of the model of emotions.

Autonomous Agents (AAs) capable of exhibiting emotional behaviors have contributed to the development of natural human machine interactions in several application domains. In order to provide AAs with emotional mechanisms, their underlying architecture must implement an Artificial Emotion System (AES), a computational model that imitates specific facets of human emotions. Although several AES have been reported in related literature, their design is generally supported on several emotion theories, leading researchers to model and integrate isolated emotion components and mechanisms into the architectures of AES. This theoretical foundation of AES contributes to ambiguities in the analysis and comparison of their underlying architectures, which demands the definition of standards, design guidelines, and integrative frameworks. In this paper, we present a psychologically inspired theoretical framework designed to serve as a platform for the unification of AES’ components, the comparison of AES, and the design and implementation of AES in AAs. We analyze common emotion-related requirements of AES, emotion components involved in the design of this type of computational model, and emotion theories that drive the design of most AES. The validation of this framework demonstrates its compatibility with current AES and its feasibility as a model for unifying multiple emotional theories.

In software industry, expertise is fundamental to the timely ending of projects, since tasks are solved in a more efficient manner. The expertise of an organization exists in people or artifacts and it is not easy to identify when required because this information does not reside in a repository to facilitate its management in terms of storage, consultation and distribution. This causes uncertainty among members of the organization to determine the appropriate expertise to solve a project activity. The aim of this paper is to present a multi-agent system that supports the expertise location in software development. Using a knowledge flow methodology the barriers that prevent the flow of knowledge and interaction in software development activities were identified and using this information the requirements were elicited. The architecture provides information concerning the location of the appropriate expertise to solve a problem posed by a user making use of the artifacts and experts available in the organization.

In mobile sensing, modern phones allow scientists obtain the information about the participants and their surroundings. At times, obtaining raw sensor data from mobile devices demands their collection through sensing campaigns. Often, processing these data requires data processing components in the mobile device. Some of the data processing components pertain to mathematical functions that can be reused to form other functions. These types of functions are usually crafted at a design stage by the programmers. In this work, we present a novel way in which components can be coupled at the design of the sensing campaign, without the need to redeploy the app. That is, scientists can couple two existing data processing components into a new, high-level component. The results of this paper can facilitate code re-use, code maintenance, and flexibility to a mobile sensing campaign.

Capturing large amounts of data in mobile sensing campaigns poses serious challenges related to data quality, particularly for advancing research. Data quality processes are usually carried out by researchers after careful scrutiny of data. In this paper, we present a platform for quality assurance in mobile sensing campaigns. The platform aims at working with any mobile sensing platform, and it is capable of analyzing and discriminating raw sensor data. In addition, this platform can provide a reputation for the participants based on the quality of the data provided.