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Electroencephalography (EEG) signals to detect motor imagery have been used to help patients with low mobility. However, the regular brain computer interfaces (BCI) capturing the EEG signals usually require intrusive devices and cables linked to machines. Recently, some commercial low-intrusive BCI headbands have appeared, but with less electrodes than the regular BCIs. Some works have proved the ability of the headbands to detect basic motor imagery. However, all of these works have focused on the accuracy of the detection, using session sizes larger than 10 s, in order to improve the accuracy. These session sizes prevent actuators using the headbands to interact with the user within an adequate response time. In this work, we explore the reduction of time-response in a low-intrusive device with only 4 electrodes using deep learning to detect right/left hand motion imagery. The obtained model is able to lower the detection time while maintaining an acceptable accuracy in the detection. Our findings report an accuracy above 83.8% for response time of 2 s overcoming the related works with both low- and high-intrusive devices. Hence, our low-intrusive and low-cost solution could be used in an interactive system with a reduced response time of 2 s. ; Spanish Ministry of Economy and Competitiveness (Agencia Estatal de Investigacion-AEI) TIN2016-79484-R ; European Union (EU) TIN2016-79484-R ; Spanish Government PID2019-109644RB-I00/AEI/10.13039/501100011033 FPU18/00287

The Request-Response (RR) paradigm is widely used in ubiquitous systems to exchange information in a secure, reliable and timely manner. Nonetheless, there is also an emerging need for adopting the Publish-Subscribe (PubSub) paradigm in this kind of systems, due to the advantages that this paradigm offers in supporting mobility by means of asynchronous, non-blocking and one-to-many message distribution semantics for event notification. This paper analyzes the strengths and weaknesses of both the RR and PubSub paradigms to support communications in ubiquitous systems and proposes an abstract communication model in order to enable their seamless integration. Thus, developers will be focused on communication semantics and the required quality properties, rather than be concerned about specific communication mechanisms. The aim is to provide developers with abstractions intended to decrease the complexity of integrating different communication paradigms commonly needed in ubiquitous systems. The proposal has been applied to implement a middleware and a real home automation system to show its applicability and benefits. ; This research work is funded by the Project P10-TIC-6600 granted by the Andalusian Regional Government, and the Project 20F2/36 granted by CEI-BioTIC Granada. This work has also been partially supported by the "Contrato-Programa, Facultad de Educacin y Humanidades de Ceuta 2010-2012" of the University of Granada.

The population in developed countries is aging and this fact results in high elderly health costs, as well as a decrease in the number of active working members to support these costs. This could lead to a collapse of the current systems. One of the first insights of the decline in elderly people is frailty, which could be decelerated if it is detected at an early stage. Nowadays, health professionals measure frailty manually through questionnaires and tests of strength or gait focused on the physical dimension. Sensors are increasingly used to measure and monitor different e-health indicators while the user is performing Basic Activities of Daily Life (BADL). In this paper, we present a system based on microservices architecture, which collects sensory data while the older adults perform Instrumental ADLs (IADLs) in combination with BADLs. IADLs involve physical dimension, but also cognitive and social dimensions. With the sensory data we built a machine learning model to assess frailty status which outperforms the previous works that only used BADLs. Our model is accurate, ecological, non-intrusive, flexible and can help health professionals to automatically detect frailty. ; Ministry of Economy and Competitiveness from Spain MINECO/FEDER MAT2017-85999P ; European Union (EU) MINECO/FEDER MAT2017-85999P ; Regional Government of Andalusia Research Fund from Spain A-BIO-157-UGR-18