Benefits of a Low-Cost Walking Device in Children with Cerebral Palsy: A Qualitative Study.

Children with Cerebral Palsy (CP) participate less regularly in physical and social activities. Support walkers allow mobility for infants who need aid. The aim of this study is to explore the benefits of a low-cost walking device in children with CP. A qualitative study using semi-structured, face-to-face interviews was conducted. Eight participants (two parents, two educational professionals, and four physical therapists) who live or work with children with CP that use a low-cost walking device were questioned to examine the benefits of the practice. Thematic analysis denoted three key factors about the benefits: emotional welfare, physical wellbeing, and social enjoyment. To conclude, the use of a support walker in children with CP makes them feel happier, improves their self-confidence and autonomy, and promotes participation.

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities.

In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders' involvement in the environment during everyday living activities. In order to support the children and youth with both the sequencing and learning of everyday living tasks, our robotic platform incorporates a sophisticated online action detection module that is capable of monitoring the acts performed by users. We explain all the technical details that allow many applications to be introduced to support individuals with functional diversity. We present this work as a proof of concept, which will enable an assessment of the impact that the developed technology may have on the collective of children and youth with neurodevelopmental disorders in the near future.

A "salt and pepper" noise reduction scheme for digital images based on Support Vector Machines classification and regression.

We present a new impulse noise removal technique based on Support Vector Machines (SVM). Both classification and regression were used to reduce the "salt and pepper" noise found in digital images. Classification enables identification of noisy pixels, while regression provides a means to determine reconstruction values. The training vectors necessary for the SVM were generated synthetically in order to maintain control over quality and complexity. A modified median filter based on a previous noise detection stage and a regression-based filter are presented and compared to other well-known state-of-the-art noise reduction algorithms. The results show that the filters proposed achieved good results, outperforming other state-of-the-art algorithms for low and medium noise ratios, and were comparable for very highly corrupted images.