Various technological applications for body posture correction have been proposed in order to improve handwriting or facilitate its learning for children, under the assumption that a better posture promotes better handwriting. However, very little research investigates the correlation between body posture quality and handwriting quality. Moreover, investigating this correlation typically necessitates the expertise of human observers, leading to high costs, slow progress, and potential subjectivity issues. Consequently, this method may not be suitable for educational environments that require prompt feedback and interventions. In this paper, we present a fully-automated pipeline for the real-time assessment of body posture quality, which builds upon validated scales from ergonomics, which relies on red green blue depth (RGB-D) camera data to compute the rapid entire body assessment (REBA)/rapid upper limb assessment (RULA) body posture scores. Together with a state-of-the-art tool for the automated, real-time assessment of handwriting quality, we applied our pipeline in an experiment at school involving 31 children, to quantitatively and objectively investigate (i) the correlation between body posture quality scores and handwriting quality measures, as well as (ii) the impact that interventions aimed at improving the children's body posture have on their handwriting quality. Our findings (i) demonstrate the correlations between specific postural element quality assessment scores (e.g., neck score) and handwriting dimensions (e.g., static features), and (ii) indicate that interventions aiming to improve body posture quality also have an immediate, significant positive effect on handwriting quality.

Pronunciation is a critical yet challenging aspect of language acquisition, especially for children. In this work, we propose a new teachable agent system designed to support children's pronunciation learning through a Learning-by-Teaching paradigm to address this, featuring the Phoneme-level Mispronunciation Projection (PMP) method. This method enhances learning by having the agent reproduce or exaggerate children's mispronunciations, encouraging their correction and practice. We implemented a prototype system featuring both virtual and physical embodiments of teachable agents, allowing us to explore their effectiveness in motivating children. In a pilot study with 27 children aged 7-9, we evaluated the PMP method's perceived performance, children's motivation to teach, and the influence of agent embodiment (virtual vs. physical robots). Results demonstrate the effectiveness of PMP in supporting pronunciation learning and highlight the physical robot's advantages in fostering engagement. This work also offers initial design implications for future teachable agents to support children's pronunciation skill learning.

Persuasive social robots have the ability to influence human behaviour through social interaction, which makes them a valuable technological solution for all applications aiming to support a person's behaviour change. The recently proposed Child-Robot Relational Norm Intervention (CRNI) model introduces a new approach for persuasive social robotics, leveraging children's reluctance to disturb robots to promote behaviour change. Unlike traditional methods that rely on direct feedback or reminders, CRNI encourages children to self-monitor and self-correct improper behaviour, by making the robot express mild distress whenever the child exhibits the incorrect behaviour. This paper proposes the first implementation of the CRNI approach in a real HRI context and evaluates its effectiveness in improving children's handwriting posture. The evaluation includes two user studies: (i) a multi-session study with five children investigating the long-term impact of the approach, (ii) a controlled study with 29 children comparing CRNI to direct robot reminders. The results indicate that the CRNI model leads to more sustained posture correction compared to direct interventions. More broadly, our findings suggest that relational norm-based approaches can offer an effective yet less intrusive method for fostering positive behaviours in children.

Persuasive social robots employ their social influence to modulate children’s behaviours in child-robot interaction. In this work, we introduce the Child-Robot Relational Norm Intervention (CRNI) model, leveraging the passive role of social robots and children’s reluctance to inconvenience others to influence children’s behaviours. Unlike traditional persuasive strategies that employ robots in active roles, CRNI utilizes an indirect approach by generating a disturbance for the robot in response to improper child behaviours, thereby motivating behaviour change through the avoidance of norm violations. The feasibility of CRNI is explored with a focus on improving children’s handwriting posture. To this end, as a preliminary work, we conducted two participatory design workshops with 12 children and 1 teacher to identify effective disturbances that can promote posture correction.

Children’s retention of a proper body posture while interacting with educational tablet applications is important for both their physical health and task performance. In this work, we propose a new approach to unobtrusively induce postural changes in children by applying a slowly deforming visual stimulus appearing on the tablet screen. To preliminarily validate our approach we designed a reading-and-writing tablet application for children, during which 8 different slow visual stimuli would be provided, and monitored the children’s posture via a vision-based automated posture tracking system. Results from 10 children aged 6-11 suggest that the proposed approach is suitable for unobtrusively changing children’s postures and will stand as the basis for the future design of an adaptive unobtrusive posture regulation system.

Handwriting practising, as any other repetitive task, often leads the practiser to an overconcentration state where their performance might be affected by postural and mental fatigue. Short breaks to perform unrelated activities, especially relaxation exercises, have shown to be a simple alternative to soften or postpone this phenomenon. Therefore, in this paper we are investigating the immediate effects of different types of short-duration relaxation exercises in the handwriting and posture of children aged from 8 to 10 in handwriting training. We divided 40 children in two groups performing the sessions, guided by a social robot, with small exercises of mindfulness or stretching in the middle of their training. Additionally, we analysed participants’ perceptions towards the robot leading these interactions. Results showed improvements in participants’ handwriting quality and posture maintenance regardless of the condition. Additionally, more positive feedback about the pause was reported from individuals in the mindfulness condition.

Performing breaks during long periods of mentally demanding activities is a simple and effective solution to briefly rest the brain and regain concentration. Furthermore, the way we use this break time may have different impacts on the task outcomes. Social assistive robots are commonly used to coach users and boost their motivation, but little is explored about their capability of guiding and supervising effective pauses focused on performance gain in cognitive tasks. This study investigated the effects of two different types of breaks, stretching/breathing exercises versus free-time, taking place in a Human-Robot Interaction of short-term memory games. The stretching was autonomously guided and supervised by a robot using machine learning methods to recognise the poses. Results showed that the type of break affects the performance differently according to the type of task, both types of breaks decreased participants’ stress levels. However, the stretching/breathing intervention had a higher significant reduction in their stress and was also reported as the preferred one by the participants. No correlation between stress and performance was found.

Handwriting is an important skill in children development as well as in education that take years to be mastered. While keyboards and similar technological devices increasingly become more popular, new tools and engaging approaches are also required to keep children motivated in the learning process of this fundamental fine motor skill. In this paper, we present the iReCheck platform, an innovative multimodal interactive system capable of providing personalised handwriting training through a social robot companion and serious games in a tablet, easily adoptable by teachers and therapists in their daily educational practices. Results from studies in multiple contexts, such as schools, clinics, and hospitals are showing the potential application of our proposal not only to investigate handwriting itself but also in strongly correlated aspects, such as inner-motivation and student’s posture.

Handwriting learning is a long and complex process that takes about ten years to be fully mastered. Nearly one-third of all children aged 4-12 experiences handwriting difficulties and, sadly, most of them are left to fight them on their own, due to the scarcity of tools for the detection and remediation of such difficulties. Building on state-of-the-art digital solutions for automated handwriting assessment and the training of specific handwriting-related skills, in this article we discuss requirements, rationale, and architecture of a system for handwriting training, which relies on a social robot as a mediator agent, offering personalized training and suggestions. The system is envisioned to operate autonomously and to support long-term interactions via personalization. Preliminary validation of the system in an experiment with 31 children showed its potential not only for autonomously guiding handwriting training sessions, but also for its inclusion in the teachers' practice.