Motion-based technology to support motor skills screening in developing children: A scoping review

Background. Acquiring motor skills is fundamental for children's development since it is linked to cognitive development. However, access to early detection of motor development delays is limited. Aim. This review explores the use and potential of motion-based technology (MBT) as a complement to support and increase access to motor screening in developing children. Methods. Six databases were searched following the PRISMA guidelines to search, select, and assess relevant works where MBT recognised the execution of children's motor skills. Results. 164 studies were analysed to understand the type of MBT used, the motor skills detected, the purpose of using MBT and the age group targeted. Conclusions. There is a gap in the literature aiming to integrate MBT in motor skills development screening and assessment processes. Depth sensors are the prevailing technology offering the largest detection range for children from age 2. Nonetheless, the motor skills detected by MBT represent about half of the motor skills usually observed to screen and assess motor development. Overall, research in this field is underexplored. The use of multimodal approaches, combining various motion-based sensors, may support professionals in the health domain and increase access to early detection programmes.Funding for open access charge: Universidad de Málaga / CBUA

Smart toys designed for detecting developmental delays

In this paper,we describe the design considerations and implementation of a smart toy system,a technology for supporting the automatic recording and analysis for detecting developmental delays recognition when children play using the smart toy. To achieve this goal,we take advantage of the current commercial sensor features (reliability,low consumption,easy integration,etc.) to develop a series of sensor-based low-cost devices. Specifically,our prototype system consists of a tower of cubes augmented with wireless sensing capabilities and a mobile computing platform that collect the information sent from the cubes allowing the later analysis by childhood development professionals in order to verify a normal behaviour or to detect a potential disorder. This paper presents the requirements of the toy and discusses our choices in toy design,technology used,selected sensors,process to gather data from the sensors and generate information that will help in the decision-making and communication of the information to the collector system. In addition,we also describe the play activities the system supportsAuthors would like to thank the National Programme for Research, Development and Innovation, oriented to Societal Challenges, of the Spanish Ministry for Economy and Competitiveness that supported the results of this paper through EDUCERE project (TIN2013-47803-C2-2-R), and to Universidad de Alcala that supported them through EDUSENS project (CCG2014/EXP-008

Bag Toss Game based on Internet of Education Things (IoET) for the Development of Fine Motor Stimulation in Children 5-6 Years Old

The development of a child's motor skills starts when a child is 0 months old to 6 years old. In general, the development of motor skills divided into fine motor development and gross motor development. Fine motor development is a development that involves small muscles to follow certain movements. An example of a game activity to help stimulate small muscle development is the Bag Toss game. This game helps stimulate fine motor development by increasing eye coordination with the hand. In addition to the types of activities that boost fine motor development, it also requires the ability to monitor, record, and process the results of children's activities to assess and analyze the status of a child's fine motor development. In this study, we developed the Bag Toss game system that connected to the Internet of Things (IoT) platform. Bag Toss game has linked with a sensor that will record children's play activities. The results of recording data will be sent to the IoT platform to be processed and presented through the internet network. The implementation of IoT for educational purposes is known as the Internet of Educational Things (IoET). The system built will be tested in terms of functionality, reading accuracy and child assessment. The functionality of the system works 100% according to predetermined component functions, as well as for 100% successful reading accuracy for the scenario of throwing distances of 1 meter and 1.5 meters. In addition, the average delay time for every hole is 0.62 seconds. The delay value can still be tolerated and does not interfere with the game when the child assessment is conducted. The child assessment involved 4 children, the results obtained that 3 children are in the Well Development (BSH) stage and 1 child in Very Well Development (BSB) stage

Children’s Fitness and Quality of Movement

Introduction: Movement is essential to life and plays a key role in development throughout childhood. Movement can be assessed by its quantity and quality. Movement is important to measure as it can aid early intervention. Current research suggests that global levels of fitness are declining, with a lack of research surrounding children’s natural fitness levels as they get older. Quantity of movement is commonly studied, however quality is becoming increasingly popular. A clear understanding of the methods of technology used to measure quality of movement is important as understanding this area will aid in designing appropriate interventions.Methods: This thesis comprises of two experimental studies. Study one is a repeated measures design using previously collected Swanlinx data to investigate how components of children’s fitness change over a one-year period. Study two is a scoping review investigating the measurement of quality of movement with technology in the form of MEM’s devices, while aiming to gain clarity on the definition of quality.Results: Study one revealed that children’s fitness levels increase across a one-year period, in all components of fitness, except sit and reach. Boys performed significantly better in all fitness components, apart from sit and reach. Study two demonstrated the broad field that is included under the term of quality, showing clarity is needed in this area. A large number of devices, movements and populations are being observed, with multiple definitions of quality which is dependent on the metrics collected.Conclusion: Study one concludes that children’s fitness levels increase over one-year, with boys performing better than girls. This can be used to understand children’s natural fitness levels and aid future interventions in participation. Study two concludes that there are multiple ways to assess quality of movement however a clear definition of the quality should be stated, aiding comparison of quality

An INNOVATIVE USE of TECHNOLOGY and ASSOCIATIVE LEARNING to ASSESS PRONE MOTOR LEARNING and DESIGN INTERVENTIONS to ENHANCE MOTOR DEVELOPMENT in INFANTS

Since the introduction of the American Academy of Pediatrics Back to Sleep Campaign infants have not met the recommendation to “incorporate supervised, awake “prone play” in their infant’s daily routine to support motor development and minimize the risk of plagiocephaly”. Interventions are needed to increase infants’ tolerance for prone position and prone playtime to reduce the risk of plagiocephaly and motor delays. Associative learning is the ability to understand causal relationship between events. Operant conditioning is a form of associative learning that occurs by associating a behavior with positive or negative consequences. Operant conditions has been utilized to encourage behaviors such as kicking, reaching and sucking in infants by associating these behaviors with positive reinforcement. This dissertation is a compilation of three papers that each represent a study used to investigate a potential play based interventions to encourage prone motor skills in infants. The first paper describes a series of experiment used to develop the Prone Play Activity Center (PPAC) and experimental protocols used in the other studies. The purpose of the second study was to determine the feasibility of a clinical trial comparing usual care (low tech) to a high-tech intervention based on the principles of operant conditioning to increase tolerance for prone and improve prone motor skills. Ten infants participated in the study where parents of infants in the high tech intervention group (n=5) used the PPAC for 3 weeks to practice prone play. Findings from this study suggested the proposed intervention is feasible with some modifications for a future large-scale clinical trial. The purpose of the third study evaluated the ability of 3-6 months old infants to demonstrate AL in prone and remember the association learned a day later. Findings from this study suggested that a majority of infants demonstrated AL in prone with poor retention of the association, 24 hours later. Taken together these 3 papers provide preliminary evidence that a clinical trial of an intervention is feasible and that associative learning could be used to reinforce specific prone motor behaviors in the majority of infants

Psychophysiological analysis of a pedagogical agent and robotic peer for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by ongoing problems in social interaction and communication, and engagement in repetitive behaviors. According to Centers for Disease Control and Prevention, an estimated 1 in 68 children in the United States has ASD. Mounting evidence shows that many of these individuals display an interest in social interaction with computers and robots and, in general, feel comfortable spending time in such environments. It is known that the subtlety and unpredictability of people’s social behavior are intimidating and confusing for many individuals with ASD. Computerized learning environments and robots, however, prepare a predictable, dependable, and less complicated environment, where the interaction complexity can be adjusted so as to account for these individuals’ needs. The first phase of this dissertation presents an artificial-intelligence-based tutoring system which uses an interactive computer character as a pedagogical agent (PA) that simulates a human tutor teaching sight word reading to individuals with ASD. This phase examines the efficacy of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and an evidence-based instructional procedure referred to as constant time delay (CTD). A concurrent multiple-baseline across-participants design is used to evaluate the efficacy of intervention. Additionally, post-treatment probes are conducted to assess maintenance and generalization. The results suggest that all three participants acquired and maintained new sight words and demonstrated generalized responding. The second phase of this dissertation describes the augmentation of the tutoring system developed in the first phase with an autonomous humanoid robot which serves the instructional role of a peer for the student. In this tutoring paradigm, the robot adopts a peer metaphor, where its function is to act as a peer. With the introduction of the robotic peer (RP), the traditional dyadic interaction in tutoring systems is augmented to a novel triadic interaction in order to enhance the social richness of the tutoring system, and to facilitate learning through peer observation. This phase evaluates the feasibility and effects of using PA-delivered sight word instruction, based on a CTD procedure, within a small-group arrangement including a student with ASD and the robotic peer. A multiple-probe design across word sets, replicated across three participants, is used to evaluate the efficacy of intervention. The findings illustrate that all three participants acquired, maintained, and generalized all the words targeted for instruction. Furthermore, they learned a high percentage (94.44% on average) of the non-target words exclusively instructed to the RP. The data show that not only did the participants learn nontargeted words by observing the instruction to the RP but they also acquired their target words more efficiently and with less errors by the addition of an observational component to the direct instruction. The third and fourth phases of this dissertation focus on physiology-based modeling of the participants’ affective experiences during naturalistic interaction with the developed tutoring system. While computers and robots have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition offer great potential for addressing this issue. The third phase presents a Bluetooth-enabled eyewear – EmotiGO – for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users’ usual activities. This phase presents the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement. The fourth and final phase attempts to model the students’ engagement levels using their physiological signals collected with EmotiGO during naturalistic interaction with the tutoring system developed in the second phase. Several physiological indices are extracted from each of the signals. The students’ engagement levels during the interaction with the tutoring system are rated by two trained coders using the video recordings of the instructional sessions. Supervised pattern recognition algorithms are subsequently used to map the physiological indices to the engagement scores. The results indicate that the trained models are successful at classifying participants’ engagement levels with the mean classification accuracy of 86.50%. These models are an important step toward an intelligent tutoring system that can dynamically adapt its pedagogical strategies to the affective needs of learners with ASD

A Prediction Model for Detecting Developmental Disabilities in Preschool-Age Children Through Digital Biomarker-Driven Deep Learning in Serious Games: Development Study

Background: Early detection of developmental disabilities in children is essential because early intervention can improve the prognosis of children. Meanwhile, a growing body of evidence has indicated a relationship between developmental disability and motor skill, and thus, motor skill is considered in the early diagnosis of developmental disability. However, there are challenges to assessing motor skill in the diagnosis of developmental disorder, such as a lack of specialists and time constraints, and thus it is commonly conducted through informal questions or surveys to parents. Objective: This study sought to evaluate the possibility of using drag-and-drop data as a digital biomarker and to develop a classification model based on drag-and-drop data with which to classify children with developmental disabilities. Methods: We collected drag-and-drop data from children with typical development and developmental disabilities from May 1, 2018, to May 1, 2020, via a mobile application (DoBrain). We used touch coordinates and extracted kinetic variables from these coordinates. A deep learning algorithm was developed to predict potential development disabilities in children. For interpretability of the model results, we identified which coordinates contributed to the classification results by applying gradient-weighted class activation mapping. Results: Of the 370 children in the study, 223 had typical development, and 147 had developmental disabilities. In all games, the number of changes in the acceleration sign based on the direction of progress both in the x- and y-axes showed significant differences between the 2 groups (P0.5). The deep learning convolutional neural network model showed that drag-and-drop data can help diagnose developmental disabilities, with an area under the receiving operating characteristics curve of 0.817. A gradient class activation map, which can interpret the results of a deep learning model, was visualized with the game results for specific children. Conclusions: Through the results of the deep learning model, we confirmed that drag-and-drop data can be a new digital biomarker for the diagnosis of developmental disabilities.ope

From AAL to ambient assisted rehabilitation: a research pilot protocol based on smart objects and biofeedback

AbstractThe progressive miniaturization of electronic devices and their exponential increase in processing, storage and transmission capabilities, represent key factors of the current digital transformation, also sustaining the great development of Ambient Assisted Living (AAL) and the Internet of Things. Although most of the investigations in the recent years focused on remote monitoring and diagnostics, rehabilitation too could be positively affected by the widespread integrated use of these devices. Smart Objects in particular may be among the enablers to new quantitative approaches. In this paper, we present a proof-of-concept and some preliminary results of an innovative pediatric rehabilitation protocol based on Smart Objects and biofeedback, which we administered to a sample of children with unilateral cerebral palsy. The novelty of the approach mainly consists in placing the sensing device into a common toy (a ball in our protocol) and using the information measured by the device to administer multimedia-enriched type of exercises, more engaging if compared to the usual rehabilitation activities used in clinical settings. We also introduce a couple of performance indexes, which could be helpful for a quantitative continuous evaluation of movements during the exercises. Even if the number of children involved and sessions performed are not suitable to assess any change in the subjects' abilities, nor to derive solid statistical inferences, the novel approach resulted very engaging and enjoyable by all the children participating in the study. Moreover, given the almost non-existent literature on the use of Smart Objects in pediatric rehabilitation, the few qualitative/quantitative results here reported may promote the scientific and clinical discussion regarding AAL solutions in a "Computer Assisted Rehabilitation" perspective, towards what can be defined "Pediatric Rehabilitation 2.0"

Social Attribution in Toddlers At Risk for Autism Spectrum Disorder

Autism spectrum disorder (ASD) can now be reliably diagnosed in preverbal toddlers and early diagnosis is becoming more common since the development of early autism screening practices. However, the positive predictive value of widely used screening tools remains low, which leads to a high number of false positive cases requiring further evaluation. Given that access to specialists is limited, there is a pressing need to develop easily accessible and broadly applicable direct measures that will further streamline screening and diagnosis for at risk toddlers. The primary aim of the current study is to examine the utility of a novel, direct measure of social attribution in measuring this skill in toddlers with and without ASD and examining the utility of this measure in reliably identifying ASD in a sample of at risk toddlers with a broad range of verbal and cognitive abilities. Participants include 35 toddlers considered at risk for an ASD (i.e., 15 with ASD, 20 with non-ASD delays; DD) and 22 typically developing (TD) toddlers. Children were presented with two versions of a nonverbal social attribution measure featuring a visual habituation-based violation of expectation paradigm; a live puppet show version previously studied in infant populations and a novel touchscreen adaptation. It was hypothesized that toddlers without a diagnosis of ASD would demonstrate evidence of social attribution whereas children with ASD would demonstrate reduced social attribution. Furthermore, it was predicted that performance would have clinical utility in predicting a diagnosis of ASD and symptom severity. Results indicated that no groups showed gross looking time differences evidencing social attribution, bringing into question whether this paradigm is appropriate for capturing social attribution in this age range. Despite this, toddlers in the TD group demonstrated evidence of social evaluation in the live puppet show task whereas toddlers within the ASD and DD groups did not. Differential habituation characteristics between the DD and TD groups suggest that other factors may have impeded success in the DD group. Future research is warranted to examine whether deficient social evaluation is specific to ASD or characterizes developmental delays more broadly. Findings have implications for future research examining theories of social attribution and informing the use of new technologies in toddler research and clinical tool development

Final report key contents: main results accomplished by the EU-Funded project IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots

This document has the goal of presenting the main scientific and technological achievements of the project IM-CLeVeR. The document is organised as follows: 1. Project executive summary: a brief overview of the project vision, objectives and keywords. 2. Beneficiaries of the project and contacts: list of Teams (partners) of the project, Team Leaders and contacts. 3. Project context and objectives: the vision of the project and its overall objectives 4. Overview of work performed and main results achieved: a one page overview of the main results of the project 5. Overview of main results per partner: a bullet-point list of main results per partners 6. Main achievements in detail, per partner: a throughout explanation of the main results per partner (but including collaboration work), with also reference to the main publications supporting them