Algorithms for Psycho-Motor Training and Performance Using Wearable Technologies

Dear Colleagues,

This Special Issue of Algorithms aims to compile original research on algorithms for sensing, processing and/or supporting psycho-motor training. Current advances on wearable technologies (such as intelligent bracelets, watches, t-shirts, etc.) and the interconnection capabilities provided by the Internet of Things can facilitate quantifying and describing the activity of a person while carrying out learning tasks that require consolidating motor tasks into memory through repetition towards accurate movements (e.g., playing a musical instrument, practicing a sport technique, using sign language, etc.). In fact, wearable devices can be used for sensing cognitive, affective, and physical activity. Thus, algorithms can provide support for embodied cognition, affection, and motor skills through varied feedback provided through diverse types of actuators that can take advantage of ambient intelligence (to get to the learner through the most appropriate sensorial channels in a low intrusive way) and/or embodiment scaffolding (e.g., exoskeletons). In this context, this Special Issue invites proposals that provide algorithmic solutions to address some of the existing challenges in terms of the modeling of the physical interaction of the person and the provision of the required personalized support during the physical training. Some of these challenges relate to wearable sensor data analysis, mining, aggregating, modeling, visualizing, sharing, securing, querying, etc., in a distributed way. The mapping of algorithms input/output to interconnectivity standards in order to take advantage of available distributed processing infrastructures would also be needed. The benefits (or not) of Big Data processing algorithms on human movements data sensed needs also to be explored.

Dr. Olga C. Santos
Guest Editor

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