BackgroundEDUCERE (“Ubiquitous Detection Ecosystem to Care and Early Stimulation for Children with Developmental Disorders”) is an ecosystem for ubiquitous detection, care, and early stimulation of children with developmental disorders. The objectives of this Spanish government-funded research and development project are to investigate, develop, and evaluate innovative solutions to detect changes in psychomotor development through the natural interaction of children with toys and everyday objects, and perform stimulation and early attention activities in real environments such as home and school. Thirty multidisciplinary professionals and three nursery schools worked in the EDUCERE project between 2014 and 2017 and they obtained satisfactory results. Related to EDUCERE, we found studies based on providing networks of connected smart objects and the interaction between toys and social networks.ObjectiveThis research includes the design, implementation, and validation of an EDUCERE smart toy aimed to automatically detect delays in psychomotor development. The results from initial tests led to enhancing the effectiveness of the original design and deployment. The smart toy, based on stackable cubes, has a data collector module and a smart system for detection of developmental delays, called the EDUCERE developmental delay screening system (DDSS).MethodsThe pilot study involved 65 toddlers aged between 23 and 37 months (mean=29.02, SD 3.81) who built a tower with five stackable cubes, designed by following the EDUCERE smart toy model. As toddlers made the tower, sensors in the cubes sent data to a collector module through a wireless connection. All trials were video-recorded for further analysis by child development experts. After watching the videos, experts scored the performance of the trials to compare and fine-tune the interpretation of the data automatically gathered by the toy-embedded sensors.ResultsJudges were highly reliable in an interrater agreement analysis (intraclass correlation 0.961, 95% CI 0.937-0.967), suggesting that the process was successful to separate different levels of performance. A factor analysis of collected data showed that three factors, trembling, speed, and accuracy, accounted for 76.79% of the total variance, but only two of them were predictors of performance in a regression analysis: accuracy (P=.001) and speed (P=.002). The other factor, trembling (P=.79), did not have a significant effect on this dependent variable.ConclusionsThe EDUCERE DDSS is ready to use the regression equation obtained for the dependent variable “performance” as an algorithm for the automatic detection of psychomotor developmental delays. The results of the factor analysis are valuable to simplify the design of the smart toy by taking into account only the significant variables in the collector module. The fine-tuning of the toy process module will be carried out by following the specifications resulting from the analysis of the data to improve the efficiency and effectiveness of the product.

Sensors, combined with intelligent systems, can enhance the quality of the Telecare services deployed at home, improving the capability for detection of risk situations and the users care. However, there are no specific studies that evaluate this kind of Telecare systems by professionals that work in a Telecare center. This paper shows the results of an assessment of the current satisfaction and future expectations of the Telecare professionals when using advanced Telecare solutions deployed at home. The study has been conducted through structured interviews with 24 Telecare experts working in the Telecare center of the Spanish Red Cross for attending alarm calls for elderly people. The interviews had the support of the TALISMAN Telecare system that is a next-generation Telecare service deployed in the accessible digital home of Universidad Politécnica de Madrid. All participants showed overall satisfaction with TALISMAN and their intention to use it. Results showed that perceived usefulness is an important influencing factor to the intention to use it and the Quality of the Information is a key factor in the perceived usefulness. TALISMAN, as an example of a next-generation Telecare system, is seen as a tool with high potential for improving the care of elderly people at home.

No abstract

No abstract

Information and Communication Technologies together appropriate reasoning tools can add value to current telecare systems, including smart tele-monitoring solutions to enhance the capability for identifying risk situations at home. Cooperation between Home Area Networks (HAN) and Body Area Networks (BAN) at home can provide smart systems to support effective health solutions for ageing people living alone, improving service quality and security to the users and relatives. This paper details the development of a reasoning platform to monitor situations of the person at home, and react in risk situations that demanding care support of remote careers. The system integrates BAN and HAN with intelligent agents, whose behavior is defined by ontologies and rules. The system manages environmental and user data to proactively detect risk situations, and dynamically adjust its behavior to trigger the adequate problem solve mechanisms. A development methodology was also adapted to sustain knowledge acquisition process from experts in all stages of the development process and to create the reasoning logic adapted to the users in telecare scenario. Thus, the platform is flexible and easily customizable, according hardware conditions and user profile. 