Autism is a neurodevelopmental disorder in which the available therapies target the
improvement of social skills, in order to ensure a high quality of life for the child.
The use of Social Assistive Robots offers new therapeutic possibilities in which robots
can act as therapy enhancers. IOGIOCO project emerges in this framework: it aims at
the development of a Robot- Assisted Therapy protocol for the treatment of Autism
Spectrum Disorder, through gesture training. The definition of these gestures and
their recognition by the robot are parameters that directly affect the engagement
of the children. However, the design of a protocol becomes harder in a highly unconstrained
environment. Therefore, the current work aims at expanding the gesture set and improving
the gesture recognition algorithm available in the IOGIOCO platform. More specifically,
total body gestures have been added to the available upper limbs movements, and a
custom Activity Detection method has been developed, which allows the identification
of the time window in which a gesture is performed. The insertion of this method on
a recognition algorithm based on a ResNet, a particular kind of Convolutional Neural
Network, improved its F1-score from 57% obtained with the previously-available version,
in a dataset of ASD children, to 76%, demonstrating the effectiveness of the Activity
Detection method. Furthermore, the expansion of the interaction possibilities to total
body movements was positively evaluated by the clinical staff, increasing the engagement
of patients and the set of possible trained skills. Therefore, the results of the
current work are encouraging. To reinforce the conclusions drawn, the proposed algorithm
should be tested in real time on several autistic children within a complete Randomized
Clinical Trial, also to study the effectiveness of this type of treatment. From the
technical point of view, further improvements of the developed methodology should
tackle the remained issues, such as further increasing the recognition capability,
especially in the transitions from sitting to standing, that proved to be a hard task
for the developed method.
