Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Neurodevelopmental disorders (NDD) are impairments of the growth and development of the brain and/or central nervous system. In the light of clinical findings on early diagnosis of NDD and prompted by recent advances in hardware and software technologies, several researchers tried to introduce automatic systems to analyse the baby’s movement, even in cribs. Traditional technologies for automatic baby motion analysis leverage contact sensors. Alternatively, remotely acquired video data (e.g., RGB or depth) can be used, with or without active/passive markers positioned on the body. Markerless approaches are easier to set up and maintain (without any human intervention) and they work well on non-collaborative users, making them the most suitable technologies for clinical applications involving children. On the other hand, they require complex computational strategies for extracting knowledge from data, and then, they strongly depend on advances in computer vision and machine learning, which are among the most expanding areas of research. As a consequence, also markerless video-based analysis of movements in children for NDD has been rapidly expanding but, to the best of our knowledge, there is not yet a survey paper providing a broad overview of how recent scientific developments impacted it. This paper tries to fill this gap and it lists specifically designed data acquisition tools and publicly available datasets as well. Besides, it gives a glimpse of the most promising techniques in computer vision, machine learning and pattern recognition which could be profitably exploited for children motion analysis in videos.

Atypical responsivity to social rewards has been observed in young children with or at risk of Autism Spectrum Disorders (ASD). These observations contributed to the hypothesis of reduced social motivation in ASD. In the current study we develop a novel task to test social reward preference using a tablet computer (iPad), where two differently coloured buttons were associated with a social and a nonsocial rewarding image respectively. 63 young children, aged 14–68 months, with and without a diagnosis of ASD took part in the study. The experimental sessions were also recorded on video, using an in-built webcam on the tablet as well as an external camera. Children with ASD were found to show a reduced relative preference for social rewards, indexed by a lower proportion of touches for the button associated with the social reward image. Greater social preference as measured using the tablet-based task was associated with increased use of social communicative behaviour such as eye contact with the experimenter and social smile in response to the social reward image. These results are consistent with earlier findings from eye-tracking studies, and provide novel empirical insights into atypical social reward responsivity in ASD.

Background: The DREAM Project operates within the framework of the national health systems of several sub-Saharan African countries and aims to introduce the essential components of an integrated strategy for the prevention and treatment of HIV/AIDS. The project is intended to serve as a model for a wide-ranging scale-up in the response to the epidemic. This paper aims to show DREAM's challenges and the solutions adopted. One of the solutions is the efficient management of the clinical data regarding the treatment of the patients and epidemiological analyses. Methods: Specific software for the management of the patients' EMR has been created within the DREAM programme in order to deal with the challenges deriving from the context in which DREAM operates. Setting up a computer infrastructure in health centres, providing a power supply, as well as managing the data and the project resources efficiently and reliably, are some of the questions that have been analysed in this study. Results: Over the years this software has proved that it is able to respond to the need for efficient management of the clinical data and organization of the health centres. Today it is used in 10 countries in sub-Saharan Africa by thousands of professionals and by now it has reached its fourth version. The medical files of over 73,000 assisted patients are managed by this software and the data collected with it have become essential for the epidemiological research that is carried out to improve the effectiveness of the therapy. Conclusion: Sub-Saharan Africa is the region hardest hit by HIV and AIDS in the world. However, the resources and responses adopted so far, to confront the epidemic, have at times been rather minimalist. The DREAM project has faced the battle against the epidemic by equipping itself with qualitative standards comparable to Western ones. The experience of DREAM has revealed that it is indeed possible to guarantee levels of excellence in developing countries, also in the sphere of ICT (Information and Communication Technology), thus making the intervention even more effective and contributing to bridging the digital divide.

This paper reports the design and assessment of a neuro-fuzzy model to support clinicians during virtual reality therapy. The implemented model is able to automatically recognize the perceived stress levels of the patients by analyzing physiological and behavioral data during treatment. The model, consisting of a self-organizing map and a fuzzy-rule-based module, was trained unobtrusively recording electrocardiogram, breath rate and activity during stress inoculation provided by the exposure to virtual environments. Twenty nurses were exposed to sessions simulating typical stressful situations experienced at their workplace. Four levels of stress severity were evaluated for each subject by gold standard clinical scales administered by trained personnel. The model's performances were discussed and compared with the main machine learning algorithms. The neuro-fuzzy model shows better performances in terms of stress level classification with 83% of mean recognition rate. RESEARCH HIGHLIGHTS

We have introduced a new hybrid fabrication method for lab-on-a-chip devices through the combination of femtosecond laser micromachining and removable insert micro-injection molding. This method is particularly suited for the fast prototyping of new devices, while maintaining a competitive low cost. To demonstrate the effectiveness of our approach, we designed, fabricated, and tested a completely integrated flow cytometer coupled to a portable media device. The system operation was tested with fluorescent plastic micro-bead solutions ranging from 100 beads/μL to 500 beads/μL. We demonstrated that this hybrid lab-on-a-chip fabrication technology is suitable for producing low-cost and portable biological microsystems and for effectively bridging the gap between new device concepts and their mass production.