1. Assessing Children's Fine Motor Skills With Sensor-Augmented Toys: Machine Learning Approach
- Author
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Ben Schouten, Annette Brons, Antoine W. de Schipper, Ben Kröse, Sander Bakkes, Huub M. Toussaint, Svetlana Mironcika, Lectoraat Digital Life, Lectoraat Bewegingswetenschappen, Kenniscentrum Bewegen, Sport en Voeding, Hogeschool van Amsterdam, Lectoraat Civic Interaction Design, Sub Multimedia, Multimedia, and Systemic Change
- Subjects
Support Vector Machine ,020205 medical informatics ,business.operation ,Motor development ,Computer science ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Toys ,Motor skills ,Health Informatics ,02 engineering and technology ,Machine learning ,computer.software_genre ,Outcome (game theory) ,Machine Learning ,03 medical and health sciences ,Movement assessment ,0302 clinical medicine ,Classifier (linguistics) ,0202 electrical engineering, electronic engineering, information engineering ,Movement ABC (MABC) ,Humans ,030212 general & internal medicine ,Child ,Children ,Motor function ,Statistical hypothesis testing ,Playful ,Original Paper ,Learning classifier system ,business.industry ,Game ,Fine motor skill ,Gamification ,Support vector machine ,Roadrunner ,Logistic Models ,Artificial intelligence ,Motor skill assessment ,Public aspects of medicine ,RA1-1270 ,business ,F1 score ,Fine motor function ,computer - 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 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.
- Published
- 2021