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 sensor-augmented toys can efficiently predict the fine MABC-2 scores for children in elementary school. Selecting the game type (focusing on speed or precision) and data type (sensor or game data) is more important for determining the performance than selecting the machine learning classifier or level of difficulty.
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Smart tangible toys, designed for hand manipulation, can transform fine motor skills assessment into enjoyable activities which are engaging for children to play (partially) unsupervised. Such toys can support school teachers and parents for early detection of deficiencies in motor skills development of children, as well as objectively monitor the progress of skills development over time. To make a game enjoyable for children with different skills level, these smart toys could offer an adaptive game play. In this paper we describe the design and deployment of a digital board game, equipped with sensors, which we use to explore the potential of using smart toys for fine motor skills assessment in children.
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When children are not ready to write, assessment of fine motor coordination may be indicated. The purpose of this study was to evaluate which fine motor test, the Nine-Hole Peg Test (9-HPT) or the newly developed Timed Test of In-Hand Manipulation (Timed-TIHM), correlates best with handwriting readiness as measured by the Writing Readiness Inventory Tool In Context-Task Performance (WRITIC-TP). From the 119 participating children, 43 were poor performers. Convergent validity of the 9-HPT and Timed-TIHM with WRITIC-TP was determined, and test-retest reliability of the Timed-TIHM was examined in 59 children. The results showed that correlations of the 9-HPT and Timed-TIHM with the WRITIC-TP were similar (rs = -0.40). The 9-HPT and the complex rotation subtask of the Timed-TIHM had a low correlation with the WRITIC-TP in poor performers (rs = -0.30 and -0.32 respectively). Test-retest reliability of the Timed-TIHM was significant (Intraclass Correlation Coefficient = 0.71). Neither of these two fine motor tests is appeared superior. They both relate to different aspects of fine motor performance. One of the limitations of the methodology was unequal numbers of children in subgroups. It is recommended that further research is indicated to evaluate the relation between development of fine motor coordination and handwriting proficiency, on the Timed-TIHM in different age groups.
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