Using computational support in motor ability analysis of individuals with Down syndrome: Literature review

The lack of motor ability is one of the main Down syndrome (DS) effects. However, there are several types of motor disorders that can be attenuated or corrected if they are early identified and properly analyzed.

The aim of our study is to support the local Physical Activity research group, which works with about 25 DS children, by means of computational resources for motor analysis. To that end, we first needed to identify the main computational approaches that support the motor analysis of DS individuals, if they are already connected to intervention programs, and potential opportunities to extend the current state of the art.

We carried out a systematic review that identified 28 papers from the current literature. These papers were then analyzed to answer the research questions defined in our study.

Our main findings were: (1) the temporal distribution of papers shows this area is new and it is starting to create a body of knowledge that in fact supports motor treatments of DS individuals; (2) there is a diversity of studies that consider different research directions such as comparisons of motor features of DS with non-DS individuals, characterization of DS motor features, and approaches for intervention programs to improve DS motor abilities; (3) there are several types of sensing hardware that enables the development of studies from different perspectives; (4) spatial monitoring is performed but only in laboratory conditions; (5); mathematical tools are largely used while strategies based on artificial intelligence for automated analysis are ignored; and (6) proposals for DS post-intervention monitoring are not found in the literature.

DS motor analysis is still a new research area and it is not mature yet. Thus, the use of computational resources is very pragmatic and focused only on mathematical tools that support the numerical analysis of the acquired data. The main proposals for motor analysis are performed in laboratory, so that there are several opportunities to create computational resources to obtain real-time data on the move. The integration of this data with intervention strategies is also a potential area for future researches.