Human activity recognition (HAR) has become a key technology for improving the quality of life for individuals with special needs, such as older adults and children with Down Syndrome (CwDS). This study presents a novel application of HAR systems to detect directive behaviors in parent-child interactions, focusing on parents of CwDS during educational activities. Using video data, we developed two models: a Convolutional Neural Network (CNN) and a hybrid CNN-LSTM model, to recognize subtle cues such as physical proximity and verbal interactions. The CNN3D model achieved over 90% accuracy in detecting approach behaviors and around 65% for verbal expressions. The CNN-LSTM model outperformed CNN3D in classifying verbal expressions, achieving over 68% accuracy. These results highlight the potential of deep learning classifiers for analyzing subtle parent-child interactions, offering valuable insights into parent-child dynamics and contributing to the development of assistive tools for studying educational settings.

In this work, we aim at identifying the speaker in interactions between mothers and children with Down syndrome (DS) using audio. We collected audio from a session in which children with DS solved puzzles, and their mothers were by their side. We generated a dataset by manually annotating human speech activity and non-speech. We used machine learning to perform four experiments, including individual and generalized models achieving on average F1-scores of 0.74 (five-class model) and 0.84 (two-class) for individual models and 0.69 (five-class) and 0.82 (two-class) for the generalized models. Our results can be helpful to behavioral researchers, therapists, and those interested in better understanding how mother–child interactions unfold in naturalistic settings.

Abstract Stress is a reaction of the body to external challenges, whether physical or psychological. In sports, there are stress factors that affect the athlete's performance, especially in team sports that involve short, high-intensity exercise cycles followed by short recovery periods, such as American football. The lack of stress regulation mechanisms can be detrimental to the individual and collective performance of athletes. Biofeedback systems have shown promising clinical results in regulating stress for sports competitions. However, there is a lack of scientific evidence to support their efficacy, and technologies, such as virtual reality videogames, have not been extensively explored. In this article, we present the development and pilot testing of Virtual Autonomic Nervous System (VANS), a virtual reality videogame using biofeedback that supports stress management training in athletes. VANS uses an optical heart rate sensor and aims at keeping the heart rate below a given threshold to control features within the game. We evaluated the usability and user experience of VANS through a 1-week deployment study with 10 American footballers. Our results show that VANS outperformed a commercial videogame used for biofeedback training and considerably reduced stress in our participants. Therefore, VANS could provide stress management training for future matches and competitions. Finally, we reflect on aspects of our design and discuss future directions of our work.

Abstract Occupational stress refers to job‐related uneasiness and anxiety, which affect people's emotional or physical health. Although occupational stress has been studied in several industries, it has remained largely unexplored in software developers, particularly in emerging economies such as Mexico. In this work, we propose a set of measures for supporting the assessment of occupational stress in software developers, which concurs with the types of tasks performed by software developers daily. For this, we first identified several stressors found in the literature. Then, we carried out 10 semi‐structured interviews with novice software developers to further understand stressors at the workplace. Afterwards, we conducted a study with 30 novice software developers for associating stress with workplace measures. From our work, we identified the workload, mental work fatigue, and work distraction as relevant measures associated with a certain level of stress in software developers. Our results suggest that some indicators can be used to monitor measures that are associated with occupational stress in software developers.

Software Engineering is a discipline that encompasses processes associated with the development of interactive systems. The perceived quality of an interactive system is heavily influenced by the user interface design, which may result in many challenges. One such challenge is design‐level requirements analysis. The success of the software system is mostly dependent on how well users’ requirements have been understood and translated into appropriate functionalities. During the interactive system design process, it is common to find recurring problems in human–computer interactions, for which reusing solutions is highly feasible. Interaction design patterns seek to support designers in decision making during the design of interactive systems. Due to the design task tends to be subjective and prone to errors. This work aims at presenting and evaluating an interaction design patterns recommendation model based on design‐level requirements classification, through the application of supervised machine learning algorithms. To compare the performance of four classification algorithms, a study was carried out, in which the linear support vector machine was the most suitable to this problem. The results of this work can be used for implementing frameworks that can better support designers’ decision making when designing user interfaces.

In this article, we examine how collective notions of belonging and imagination become a fertile terrain upon which transnational websites can sustain certain social practices across national boundaries that would be otherwise difficult. Drawing on field work carried out in the United States and Mexico, and using transnational imagination as our analytical lens, we observed three phenomena that are closely related to the use of a transnational website by a migrant community. First, the transnational website under study was a place for a collective imaginary rather than just for the circulation of news. Also, through transnational imagination, migrants can make claims about their status in their community of origin. Moreover, the website is instrumental in harmonizing the various views of the homelands' realities. Finally, the website can inspire us to look beyond dyadic forms of communication.

This paper presents the InCense research toolkit to facilitate researchers with little or no technical background to implement a sensing application for mobile phones. To reach this end, InCense provides a GUI and an interactive ontology to enable users to define the configuration of the sensing application, i.e. what sensing components to add, and the flow of the sensing session. We illustrate the ease of use of the InCense platform through a scenario in which both opportunistic and participatory sensing paradigms are used.

We describe the use of a technique aimed at allowing users to realistically experience novel mobile applications in a naturalistic environment as part of the formative evaluation of the system. This technique, that we have named naturalistic enactment, is appropriate for application scenarios that are not easily replicated in a laboratory setting and might cause risks to users or other stakeholders if used at a prototyping stage, as is often the case in the healthcare domain. We present and discuss the use of this technique after the recent introduction of a mobile application aimed at assisting geriatric nurses in attending emergency calls. The technique provided nurses with the experience of operating the mobile application in a naturalistic environment (e.g., high ecological validity), thus allowing them to identify adoption issues that they would have missed otherwise.

Information and communication technologies (ICTs) can be instrumental to the sense of community connectedness of migrants: being aware of the daily occurrences in their home communities. Whereas formerly, migrant workers were using the postal service and telephone as the preferred channels of communication, nowadays the penetration of the Internet is likely to change the panorama. Focusing mainly on phone-based communication, this work analyzes the notion of community connectedness from the perspective of people from the municipality of Juchipila, Mexico now living in the USA. We highlight three communication cycles that can influence the sense of community connectedness over time. Finally, this work concludes by discussing the role played by ICTs in the level of community connectedness.

The nature of a context-aware application in hospital work demands a reliable and accurate location system. The activity for which this location information is needed determines to a great extent the relevancy of this contextual variable, since a minor error in delivering patient-based information can be critical. In this correspondence, we present an enhanced technique to infer the location of users in a hospital setting based on the strength of radio-frequency signals received by mobile devices that are used to train a neural network. The approach uses the neighbors surrounding the location to be estimated to track users continuously. This neighborhood eases the training and is used to simulate previous time instant guesses to reduce the location estimation error and alleviate the hopping trajectories of users. The results obtained by using this approach are in the order of 1.3 m for the average distance error during continuous motion.