Out-of-hospital emergency departments receive multiple types of requests daily. Their management requires a balance to be found between available resources and the actual needs of the requesting party. Those regarding suicidal behaviour, which are resource heavy, are few in number in terms of the bulk of requests, and detecting them correctly is therefore important. Previous research, using machine learning algorithms to analyse suicide, has typically focused on discovering insights to be used by medical personnel. This proposal extends its use in two directions: knowledge that can be used by non-exclusively medical staff, such as telephone operators, and the models that have been incorporated into a software prototype to help in the decision-making of an emergency department. In addition, previous research has often included a range of information from different sources that are not available when processing an emergency call request, for example, data that is only obtained at the end of the intervention. A full-scale data mining process has been performed using data from the out-of-hospital emergency service in Malaga (Spain). Sensitivity has been the primary goal to avoid missing cases requiring special attention, but this objective has been pursued without overlooking a good trade-off with specificity. The best models can offer such a compromise between sensitivity and specificity, and show more than 80% in both metrics simultaneously. The experts validate that the modelling phase showed that the algorithms have automatically identified already known situations. This lays the groundwork for further iterations with a promising outlook.

Abstract Down syndrome is considered the most common genetic cause of intellectual disability. People with intellectual disabilities have deficits in intellectual functioning such as attention, reasoning and academic learning. According to the American Association on Intellectual and Developmental Disabilities and the Centers for Disease Control and Prevention, intellectual disability encompasses only a person’s intellectual functioning; however, it can co-exist with other developmental disabilities, such as Down syndrome. People with Down syndrome may have intellectual disabilities in the mild to moderate range. Because of this, they exhibit some cognitive and emotional characteristics of intellectual disability. Among the emotional characteristics of these populations are difficulties in recognizing some emotions, which triggers anxiety and depression problems. The literature proposes using serious video games to support different skills of people with Down syndrome and intellectual disabilities. Some of these works have shown how these serious video games support the development of different skills of these populations. However, they have not been designed to support their emotional awareness. Likewise, technology is aimed at supporting emotion recognition and identification, but most of these have been aimed at other populations, such as people with autism. This paper presents the evaluation process of the design of Emotion4Down: a serious video game to support emotional awareness. We conducted this evaluation in three stages involving people with intellectual disabilities and Down syndrome.

Among the characteristics of people with Down syndrome is the difficulty in identifying and expressing their own emotions. This difficulty stems from the lack of development of their emotional awareness which can cause them anxiety, and depression when they are exposed to significant situations in their lives, and they do not have the skills to express their emotions.

Children with autism have fine-motor coordination problems, which have a significant impact on the execution of activities of daily living. To practice their fine-motor coordination skills, they attend occupational therapy, which involves repetitive motor activities. However, sometimes traditional therapy can be perceived by children as tedious and boring. On the other hand, games-based therapy has shown to be effective and more fun. In this sense, gesture-based video games, digital games with gestural interaction as a game controller, might have the potential to support the fine-motor coordination skills of children with autism. In this paper, we present the design of a gesture-based video game to support the fine-motor coordination skills of children with autism, with the goal to contribute to the proper execution of activities of daily living, required for achieving independent living; and the process followed in the design.

Child obesity and being overweight is a health problem of increasing occurrence. Sedentary lifestyles and lack of exercise affect young children’s motor skills development, hence their quality of life. This study presents the design and evaluation of Hunting Relics, a collaborative exergame to promote collective exertion in young children. The system runs on an interactive floor and was designed using an iterative user-centered methodology in combination with the Capability, Opportunity, and Motivation to generate Behavior (COM-B) model. To evaluate the impact of our design choices, two deployment studies were conducted. Results show that Hunting Relics augments existing exercise routines, keeps young children engaged for the long term, and persuades them to discover new collaborative practices to support exercising. A discussion follows about how the COM-B model is a useful framework to design exergames for young children and the impact of Hunting Relics from an educational, technical, and clinical perspective.

As the number of older adults increases, and with it the demand for dedicated care, geriatric residences face a shortage of caregivers, who themselves experience work overload, stress, and burden. We conducted a long-term field study in three geriatric residences to understand the work conditions of caregivers with the aim of developing technologies to assist them in their work and help them deal with their burdens. From this study, we obtained relevant requirements and insights to design, implement, and evaluate two prototypes for supporting caregivers’ tasks (e.g., electronic recording and automatic notifications) in order to validate the feasibility of their implementation in situ and their technical requirements. The evaluation in situ of the prototypes was conducted for a period of 4 weeks. The results of the evaluation, together with the data collected from 6 months of use, motivated the design of a predictive schedule, which was iteratively improved and evaluated in participative sessions with caregivers. PRESENCE, the predictive schedule we propose, triggers real-time alerts of risky situations (e.g., falls, entering off-limits areas such as the infirmary or the kitchen) and informs caregivers of routine tasks that need to be performed (e.g., medication administration, diaper change, etc.). Moreover, PRESENCE helps caregivers to record caring tasks (such as diaper changes or medication) and well-being assessments (such as the mood) that are difficult to automate. This facilitates caregiver's shift handover and can help to train new caregivers by suggesting routine tasks and by sending reminders and timely information about residents. It can be seen as a tool to reduce the workload of caregivers and medical staff. Instead of trying to substitute the caregiver with an automatic caring system, as proposed by others, we propose our predictive schedule system that blends caregiver assessments and measurements from sensors. We show the feasibility of predicting caregiver tasks and a formative evaluation with caregivers that provides preliminary evidence of its utility.

Motor coordination problems are common in different developmental disorders including autism and dyspraxia. Gross and fine motor coordination skills are critical to the appropriate motor coordination development that is relevant to support individuals' independence. Exergames are a good tool to help children practice motor skills as they find them engaging. In this work, we present how FroggyBobby an exergame designed for practicing gross motor coordination skills, can be extended to combine gross and fine motor exercises for supporting children with motor problems to practice self-care activities that require motor coordination.

Healthy older adults experience physical and sensory performance decrease, reducing their interest in exercising and leading to a sedentary lifestyle. This increases natural decay, diseases, isolation and risk of falls and eventually death. Active-play video gaming, also referred as exergaming, might motivate positive changes in health behavior. However, such games need to take into account the physical characteristics of players and can also be used to foster their social ties. In this paper, we present a long-term intervention, using Action Research, aimed at developing insights for the design of full-body motion-control exergames for older adults experiencing age-related changes addressing problems raised by their motion capacity. The results of our study establish which physical characteristics need to be monitored to adapt gameplay, and other elements of social gameplay and spectator-centered design. Based on our study we present guidelines for the design of full-body interaction in exergames for older adults.

Knowledge is a key asset in software engineering. Facilitating access to the knowledge that software engineers require for the task at hand can therefore bring many benefits. To accomplish this, it is important to understand how knowledge flows through the organization, to identify problems that may hinder a suitable flow, and to define strategies with which to address them. Process modeling has proved to be a useful technique for analyzing knowledge flows. Traditional process modeling languages do not, however, provide primitives to explicitly represent the knowledge involved in the processes within the models. In this paper, we illustrate how the Software Process Engineering Metamodel (SPEM) can be adapted, to be used as a process modeling language for analyzing knowledge flows in software processes. We have extended SPEM to represent knowledge and its sources in process models in an explicit way. We also discuss the experiences obtained from using this extension in a software organization and the lesson we have learned from it.