Parent involvement in children’s homework has emerged as an important component of their education. However, this involvement can generate tension between parents and children, particularly when coupled with a heavy workload, potentially exacerbating stress levels in children. Some children desire to express and share their stress with their parents. However, limited emotional regulation abilities and a lack of stress awareness often hinder effective stress management and communication by children. To address this, we designed a stress biofeedback system named Stress Diffuser, comprising a wearable biosensor with interactive embodied devices, that display the child’s stress during homework sessions. The system intends to manage children’s stress by enhancing awareness among both the child and the parent, and influencing their educational strategies and the way of interaction. We conducted experiments with eight families, including primary school children aged eight to eleven and their parents. The results of our quantitative and qualitative analysis indicated that all participants enhanced their stress awareness while using Stress Diffuser during homework. Most parents adjusted their supervisory strategies and interaction styles in response to their children’s stress data. The study sheds light on children’s needs for a designed agent to communicate their stress and regulate their emotions. Furthermore, the study explores the use of interactive biofeedback technology in homework settings with parent involvement. It reveals a favorable influence on facilitating stress management in children and on enhancing the quality of supervision and interaction between children and parents.

Stress-related health problems in children have increased in recent years, resulting in significant negative physical and mental impacts on children’s daily lives. This systematic review explores the potential of embodied technologies, such as robots, smart wearables, and the Internet of Things (IoT), as tools for managing stress in children. The goal of this systematic review is to identify the design opportunities of embodied technologies in stress management, by looking for answers in terms of different technologies, users, issues, and challenges addressed in the 91 selected papers. Through the frequency and thematic analysis, we identified six main challenges and eight design opportunities for embodied technologies. Where there are gaps and opportunities in research, we propose to focus on connectivity and active sensing through connected objects, by exploring the potential of the Internet of Robotic Things (IoRT) as an M-health solution for providing real-time and personalized stress detection and interventions for children in various daily life settings.

Recommender systems had been proposed to help people to find the interested items, such as recommending products to a buyer; identifying movies or music that a user will find interest, etc. However, the existing recommendation approaches mainly focus on capturing user-item interaction patterns for prediction, and ignore the user's side information such as visit frequency and duration. In this paper, we study the side information aided website recommendation problem that using the browsing history of a set of users and their side information to predict the websites that will be of interest to a certain user. We propose a novel recommendation approach called SI-VAE that incorporates side information with the variational autoencoders (VAEs) model for top-k recommendation. The proposed method takes both user-website interaction information and side information as input, and adopts an encoder/decoder model to generate user's interested websites from partial observations. The model of SI-VAE is implemented as a neural network, and trained with a multinomial likelihood objective function to form the ranking of user-website interaction probabilities. We conduct extensive experiments on two real-world datasets, which show that the proposed model outperforms the baselines in a number of performance metrics in website recommendation.