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In recent times, diagnosing and treating different health issues have improved greatly with the help of technology, with an example being cognitive health issues. Despite this, there is still a difference between how the technology is working towards it and the actual potential that can be achieved. In this paper, we propose a mobile application with an affective avatar, encompassed in the area of serious games, which will obtain information related to the interactions performed by the users. There are a total of 50 users, of neurotypical and nonneurotypical backgrounds, with the latter being people with Down syndrome and intellectual disability. Based on collected data from the different users interacting with the avatar in a mobile device, we analyzed the results to obtain a ground truth about prototypic empathic interactions and feed those interactions to a learning algorithm to support the diagnosis process and therapy treatment of empathy and socialization issues.

This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the Extended Body-Angles Algorithm, which is able to analyze activities using only a single key sample. This system allows to select, for each considered activity, which are its relevant joints, which makes it possible to monitor the body of the user selecting only a subset of the same. But this feature of the system has both advantages and disadvantages. As a consequence, in the past we had some difficulties with the recognition of activities that only have a small subset of the joints of the body as relevant. The goal of this work, therefore, is to analyze the effect produced by the application of several strategies on the results of an activity recognition system based on skeletal tracking joint oriented devices. Strategies that we applied with the purpose of improve the recognition rates of the activities with a small subset of relevant joints. Through the results of this work, we aim to give the scientific community some first indications about which considered strategy is better.

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