Toward Predicting Infant Developmental Outcomes From Day-Long Inertial Motion Recordings.

As improvements in medicine lower infant mortality rates, more infants with neuromotor challenges survive past birth. The motor, social, and cognitive development of these infants are closely interrelated, and challenges in any of these areas can lead to developmental differences. Thus, analyzing one of these domains - the motion of young infants - can yield insights on developmental progress to help identify individuals who would benefit most from early interventions. In the presented data collection, we gathered day-long inertial motion recordings from N = 12 typically developing (TD) infants and N = 24 infants who were classified as at risk for developmental delays (AR) due to complications at or before birth. As a first research step, we used simple machine learning methods (decision trees, k-nearest neighbors, and support vector machines) to classify infants as TD or AR based on their movement recordings and demographic data. Our next aim was to predict future outcomes for the AR infants using the same simple classifiers trained from the same movement recordings and demographic data. We achieved a 94.4% overall accuracy in classifying infants as TD or AR, and an 89.5% overall accuracy predicting future outcomes for the AR infants. The addition of inertial data was much more important to producing accurate future predictions than identification of current status. This work is an important step toward helping stakeholders to monitor the developmental progress of AR infants and identify infants who may be at the greatest risk for ongoing developmental challenges.

Planning, execution and monitoring of physical rehabilitation therapies with a robotic architecture.

Traditional methods of rehabilitation require continuous attention of therapists during the therapy sessions. This is a hard and expensive task in terms of time and effort. In many cases, the therapeutic objectives cannot be achieved due to the overwork or the difficulty for therapists to plan accurate sessions according to the medical criteria. For this purpose, a wide range of studies is opened in order to research new ways of rehabilitation, as in the field of social robotics. This work presents the current state of the THERAPIST project. Our main goal is to develop a cognitive architecture which provides a robot with enough autonomy to carry out an upper-limb rehabilitation therapy for patients with physical impairments, such as Cerebral Palsy and Obstetric Brachial Plexus Palsy.