The aim of this research is to carry out a systematic review of the use of technological gaming platforms with serious games in the upper limb rehabilitation of patients with neuromotor disorders. Through a systematic review, the first two authors defined the inclusion criteria and extracted the data, resulting in 38 studies collected from B-On, PubMed and Medline. Ninety-two per cent of the selected articles were published since 2010. This review documents 35 different gaming platforms types. Twenty-one of the 38 articles included in this review conducted a clinical trial and of those only eight report improvements in the target population following the use of the games and platforms. This review concludes that a new paradigm is emerging in the rehabilitation field, characterized by the systematic use of technological gaming platforms with serious games in/for rehabilitation. The use of this approach seems to be beneficial. However, to facilitate the full integration of these platforms, it is necessary to conduct more research in this area, explore new approaches and carry out in-depth clinical studies into the benefits of these platforms. Implications for rehabilitation This review states that the use serious games and gaming platforms for upper limb rehabilitation are starting a new paradigm in the rehabilitation. For a full integration of this technologies in the rehabilitation field more studies are needed.

Background: Remote ischemic conditioning (RIC) is a procedure applied in a limb for triggering endogenous protective pathways in distant organs, namely brain or heart. The underlying mechanisms of RIC are still not fully understood, and it is hypothesized they are mediated either by humoral factors, immune cells and/or the autonomic nervous system. Herein, heart rate variability (HRV) was used to evaluate the electrophysiological processes occurring in the heart during RIC and, in turn to assess the role of autonomic nervous system. Methods: Healthy subjects were submitted to RIC protocol and electrocardiography (ECG) was used to evaluate HRV, by assessing the variability of time intervals between two consecutive heart beats. This is a pilot study based on the analysis of 18 ECG from healthy subjects submitted to RIC. HRV was characterized in three domains (time, frequency and non-linear features) that can be correlated with the autonomic nervous system function. Results: RIC procedure increased significantly the non-linear parameter SD2, which is associated with long term HRV. This effect was observed in all subjects and in the senior (> 60 years-old) subset analysis. SD2 increase suggests an activation of both parasympathetic and sympathetic nervous system, namely via fast vagal response (parasympathetic) and the slow sympathetic response to the baroreceptors stimulation. Conclusions: RIC procedure modulates both parasympathetic and sympathetic autonomic nervous system. Furthermore, this modulation is more pronounced in the senior subset of subjects. Therefore, the autonomic nervous system regulation could be one of the mechanisms for RIC therapeutic effectiveness.

Occupational disorders considerably impact workers’ quality of life and organizational productivity, and even affect mortality worldwide. Such health issues are related to mental health and ergonomics risk factors. In particular, mental health may be affected by cognitive strain caused by unexpected interruptions and other attention compromising factors. Risk factors assessment associated with cognitive strain in office environments, namely related to attention states, still suffers from the lack of scientifically validated tools. In this work, we aim to develop a series of classification models that can classify attention during pre-defined cognitive tasks based on the acquisition of biosignals to create a ground truth of attention. Biosignals, such as electrocardiography, electroencephalography, and functional near-infrared spectroscopy, were acquired from eight subjects during standard cognitive tasks inducing attention. Individually tuned machine learning models trained with those biosignals allowed us to successfully detect attention on the individual level, with results in the range of 70–80%. The electroencephalogram and electrocardiogram were revealed to be the most appropriate sensors in this context, and the combination of multiple sensors demonstrated the importance of using multiple sources. These models prove to be relevant for the development of attention identification tools by providing ground truth to determine which human–computer interaction variables have strong associations with attention.