Have You Ever Seen a Robot? An Analysis of Children's Drawings Between Technology and Science Fiction.

Technologies have become an essential part of the daily life of our children. Consequently, artifacts that imply the early adoption of abstract thinking affect the imagination of children and young people in relation to the world of technology, now much more than they did in the past. With the emerging importance of robots in many aspects of our everyday lives, the goal of this study is to investigate which mental representations children have about robots. To this end, drawings from 104 children aged between 7 and 12 years old were used as a map of representations, considering the drawings as a proxy capable of evoking learned or emerging mental frameworks. The drawings were analyzed in several steps: they were first labeled using binary descriptors and then classified using clustering methods based on Hamming distances between drawings. Finally, questionnaire items covering children's perceptions about robots were analyzed for each of the resulting cluster separately to identify differences between them. The results show that there are relationships between the way children draw robots and their perception about robots' capabilities as well as their aspirations to pursue a career in science. These findings can provide meaningful insights into how to design educational robots and learning activities for children to learn with and about robots.

A multimodal approach to capture post-stroke temporal dynamics of recovery.

OBJECTIVE: Several training programs have been developed in the past to restore motor functions after stroke. Their efficacy strongly relies on the possibility to assess individual levels of impairment and recovery rate. However, commonly used clinical scales rely mainly on subjective functional assessments and are not able to provide a complete description of patients' neuro-biomechanical status. Therefore, current clinical tests should be integrated with specific physiological measurements, i.e. kinematic, muscular, and brain activities, to obtain a deep understanding of patients' condition and of its evolution through time and rehabilitative intervention. APPROACH: We proposed a multivariate approach for motor control assessment that simultaneously measures kinematic, muscle and brain activity and combines the main physiological variables extracted from these signals using principal component analysis (PCA). We tested it in a group of six sub-acute stroke subjects evaluated extensively before and after a four-week training, using an upper-limb exoskeleton while performing a reaching task, along with brain and muscle measurements. MAIN RESULTS: After training, all subjects exhibited clinical improvements correlating with changes in kinematics, muscle synergies, and spinal maps. Movements were smoother and faster, while muscle synergies increased in numbers and became more similar to those of the healthy controls. These findings were coupled with changes in cortical oscillations depicted by EEG-topographies. When combining these physiological variables using PCA, we found that (i) patients' kinematic and spinal maps parameters improved continuously during the four assessments; (ii) muscle coordination augmented mainly during treatment, and (iii) brain oscillations recovered mostly pre-treatment as a consequence of short-term subacute changes. SIGNIFICANCE: Although these are preliminary results, the proposed approach has the potential of identifying significant biomarkers for patient stratification as well as for the design of more effective rehabilitation protocols.

Motor improvement estimation and task adaptation for personalized robot-aided therapy: a feasibility study.

BACKGROUND: In the past years, robotic systems have become increasingly popular in upper limb rehabilitation. Nevertheless, clinical studies have so far not been able to confirm superior efficacy of robotic therapy over conventional methods. The personalization of robot-aided therapy according to the patients' individual motor deficits has been suggested as a pivotal step to improve the clinical outcome of such approaches. METHODS: Here, we present a model-based approach to personalize robot-aided rehabilitation therapy within training sessions. The proposed method combines the information from different motor performance measures recorded from the robot to continuously estimate patients' motor improvement for a series of point-to-point reaching movements in different directions. Additionally, it comprises a personalization routine to automatically adapt the rehabilitation training. We engineered our approach using an upper-limb exoskeleton. The implementation was tested with 17 healthy subjects, who underwent a motor-adaptation paradigm, and two subacute stroke patients, exhibiting different degrees of motor impairment, who participated in a pilot test undergoing rehabilitative motor training. RESULTS: The results of the exploratory study with healthy subjects showed that the participants divided into fast and slow adapters. The model was able to correctly estimate distinct motor improvement progressions between the two groups of participants while proposing individual training protocols. For the two pilot patients, an analysis of the selected motor performance measures showed that both patients were able to retain the improvements gained during training when reaching movements were reintroduced at a later stage. These results suggest that the automated training adaptation was appropriately timed and specifically tailored to the abilities of each individual. CONCLUSIONS: The results of our exploratory study demonstrated the feasibility of the proposed model-based approach for the personalization of robot-aided rehabilitation therapy. The pilot test with two subacute stroke patients further supported our approach, while providing encouraging results for the applicability in clinical settings. Trial registration This study is registered in ClinicalTrials.gov (NCT02770300, registered 30 March 2016, https://clinicaltrials.gov/ct2/show/NCT02770300).