Assessing Children’s Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach

Abstract

Background Approximately 5%-10% of elementary school children show delayed development of fine motor skills. To address these problems, detection is required. Current assessment tools are time-consuming, require a trained supervisor, and are not motivating for children. Sensor-augmented toys and machine learning have been presented as possible solutions to address this problem. Objective This study examines whether sensor-augmented toys can be used to assess children’s fine motor skills. The objectives were to (1) predict the outcome of the fine motor skill part of the Movement Assessment Battery for Children Second Edition (fine MABC-2) and (2) study the influence of the classification model, game, type of data, and level of difficulty of the game on the prediction. Methods Children in elementary school (n=95, age 7.8 [SD 0.7] years) performed the fine MABC-2 and played 2 games with a sensor-augmented toy called “Futuro Cube.” The game “roadrunner” focused on speed while the game “maze” focused on precision. Each game had several levels of difficulty. While playing, both sensor and game data were collected. Four supervised machine learning classifiers were trained with these data to predict the fine MABC-2 outcome: k-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), and support vector machine (SVM). First, we compared the performances of the games and classifiers. Subsequently, we compared the levels of difficulty and types of data for the classifier and game that performed best on accuracy and F1 score. For all statistical tests, we used α=.05. Results The highest achieved mean accuracy (0.76) was achieved with the DT classifier that was trained on both sensor and game data obtained from playing the easiest and the hardest level of the roadrunner game. Significant differences in performance were found in the accuracy scores between data obtained from the roadrunner and maze games (DT, P =.03; KNN, P =.01; LR, P =.02; SVM, P =.04). No significant differences in performance were found in the accuracy scores between the best performing classifier and the other 3 classifiers for both the roadrunner game (DT vs KNN, P =.42; DT vs LR, P =.35; DT vs SVM, P =.08) and the maze game (DT vs KNN, P =.15; DT vs LR, P =.62; DT vs SVM, P =.26). The accuracy of only the best performing level of difficulty (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was significantly better than the combination of the easiest and middle level ( P =.046). Conclusions The results of our study show that s...

Comprehensive Assessment and Early Prediction of Gross Motor Performance in Toddlers With Graph Convolutional Networks–Based Deep Learning: Development and Validation Study

“…Several previous studies have attempted to predict pediatric development using digital phenotype data, such as detecting developmental disabilities using drag-and-drop data in games [ 41 ], identifying visual impairments using gaze patterns and facial feature data in response to visual stimuli on a smartphone, and measuring fine motor skills in children using sensor-augmented toys [ 42 ]. Suzuki et al [ 23 , 24 ] conducted studies that collected the behavioral videos of 4- to 5-year-old children and extracted skeletal data through OpenPose to evaluate behavioral performance on a per-video basis using a convolutional neural network and autoencoder model.…”

Subtyping intractable functional constipation in children using clinical and laboratory data in a classification model

“…Machine learning (ML) can rapidly process high-dimensional data and identify correlations between many variables, making it very efficient when used for disease prediction, diagnosis, and precision ( 57 , 58 ). Logistic regression, SVM, K Nearest Neighbors, Decision Trees, RF, and Gradient Boosting Machine have been frequently used in pediatric studies ( 59 – 62 ). Specifically, applications of supervised machine learning have been used in studies for disease prediction ( 63 ).…”

Sensor-Based Quantitative Assessment of Children’s Fine Motor Competence: An Instrumented Version of the Placing Bricks Test

“…Children with fine motor development problems have difficulties with learning fine motor skills. They experience, for instance, problems with school tasks such as writing or cutting, or daily life activities such as closing a zipper or tying shoelaces [3]. Fine motor competence has been found to independently predict social and cognitive ability in pre-kindergarten children [1], emphasizing the interconnected development of problem-solving skills with the physical manipulation of the environment, and the role of fine motor skills in social play.…”

“…Today, 5-10% of children in elementary school have developmental motor problems [3], thus, monitoring children's fine motor development is fundamental to investigate underlying neurological disorders and design early effective interventions that can possibly mitigate the impact of motor developmental problems.…”