Enhancing volleyball training: empowering athletes and coaches through advanced sensing and analysis.

Modern sensing technologies and data analysis methods usher in a new era for sports training and practice. Hidden insights can be uncovered and interactive training environments can be created by means of data analysis. We present a system to support volleyball training which makes use of Inertial Measurement Units, a pressure sensitive display floor, and machine learning techniques to automatically detect relevant behaviours and provides the user with the appropriate information. While working with trainers and amateur athletes, we also explore potential applications that are driven by automatic action recognition, that contribute various requirements to the platform. The first application is an automatic video-tagging protocol that marks key events (captured on video) based on the automatic recognition of volleyball-specific actions with an unweighted average recall of 78.71% in the 10-fold cross-validation setting with convolution neural network and 73.84% in leave-one-subject-out cross-validation setting with active data representation method using wearable sensors, as an exemplification of how dashboard and retrieval systems would work with the platform. In the context of action recognition, we have evaluated statistical functions and their transformation using active data representation besides raw signal of IMUs sensor. The second application is the "bump-set-spike" trainer, which uses automatic action recognition to provide real-time feedback about performance to steer player behaviour in volleyball, as an example of rich learning environments enabled by live action detection. In addition to describing these applications, we detail the system components and architecture and discuss the implications that our system might have for sports in general and for volleyball in particular.

Correction: Nasri et al. A Novel Data-Driven Approach to Examine Children's Movements and Social Behaviour in Schoolyard Environments. Children 2022, 9, 1177.

The authors request the following corrections because the changes made according to the second round of the review process were not included in the original publication [...].

A Novel Data-Driven Approach to Examine Children's Movements and Social Behaviour in Schoolyard Environments.

(1) Many children in schoolyards are excluded from social interactions with peers on a daily basis. For these excluded children, schoolyard environments often contain features that hinder, rather than facilitate, their participation. These features may include lack of appropriate play equipment, overcrowded areas, or insufficient supervision. These can generate negative situations, especially for children with special needs-such as attention deficit or autism-which includes 10% of children worldwide. All children need to be able to participate in their social environment in order to engage in social learning and development. For children living with a condition that limits access to social learning, barriers to schoolyard participation can further inhibit this. Given that much physical development also occurs as a result of schoolyard play, excluded children may also be at risk for reduced physical development. (2) However, empirically examining schoolyard environments in order to understand existing obstacles to participation requires huge amounts of detailed, precise information about play behaviour, movement, and social interactions of children in a given environment from different layers around the child (physical, social, and cultural). Recruiting this information has typically been exceedingly difficult and too expensive. In this preliminary study, we present a novel sensor data-driven approach for gathering information on social interactions and apply it, in light of schoolyard affordances and individual effectivities, to examine to what extent the schoolyard environment affects children's movements and social behaviours. We collected and analysed sensor data from 150 children (aged 5-15 years) at two primary special education schools in the Netherlands using a global positioning system tracker, proximity tags, and Multi-Motion Receivers to measure locations, face-to-face interactions, and activities. Results show strong potential for this data-driven approach to examine the triad of physical, social, and cultural affordances in schoolyards. (3) First, we found strong potential in using our sensor data-driven approach for collecting data from individuals and their interactions with the schoolyard environment. Second, using this approach, we identified and discussed three schoolyard affordances (physical, social, and cultural) in our sample data. Third, we discussed factors that significantly impact children's movement and social behaviours in schoolyards: schoolyard capacity, social use of space, and individual differences. Better knowledge on the impact of these factors could help identify limitations in existing schoolyard designs and inform school officials, policymakers, supervisory authorities, and designers about current problems and practical solutions. This data-driven approach could play a crucial role in collecting information that will help identify factors involved in children's effective movements and social behaviour.

Differences in Spontaneous Interactions of Autistic Children in an Interaction With an Adult and Humanoid Robot.

Robots are promising tools for promoting engagement of autistic children in interventions and thereby increasing the amount of learning opportunities. However, designing deliberate robot behavior aimed at engaging autistic children remains challenging. Our current understanding of what interactions with a robot, or facilitated by a robot, are particularly motivating to autistic children is limited to qualitative reports with small sample sizes. Translating insights from these reports to design is difficult due to the large individual differences among autistic children in their needs, interests, and abilities. To address these issues, we conducted a descriptive study and report on an analysis of how 31 autistic children spontaneously interacted with a humanoid robot and an adult within the context of a robot-assisted intervention, as well as which individual characteristics were associated with the observed interactions. For this analysis, we used video recordings of autistic children engaged in a robot-assisted intervention that were recorded as part of the DE-ENIGMA database. The results showed that the autistic children frequently engaged in exploratory and functional interactions with the robot spontaneously, as well as in interactions with the adult that were elicited by the robot. In particular, we observed autistic children frequently initiating interactions aimed at making the robot do a certain action. Autistic children with stronger language ability, social functioning, and fewer autism spectrum-related symptoms, initiated more functional interactions with the robot and more robot-elicited interactions with the adult. We conclude that the children's individual characteristics, in particular the child's language ability, can be indicative of which types of interaction they are more likely to find interesting. Taking these into account for the design of deliberate robot behavior, coupled with providing more autonomy over the robot's behavior to the autistic children, appears promising for promoting engagement and facilitating more learning opportunities.