scholarly journals Assessing children’s fine motor skills with sensor augmented toys: A machine learning approach (Preprint)

10.2196/24237 ◽  
2020 ◽  
Author(s):  
Annette Brons ◽  
Antoine de Schipper ◽  
Svetlana Mironcika ◽  
Huub Toussaint ◽  
Ben Schouten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Motor Skills ◽  
Fine Motor ◽  
Learning Approach ◽  
Fine Motor Skills ◽  
Machine Learning Approach ◽  
Augmented Toys

scholarly journals Assessing children's fine motor skills with sensor augmented toys: Machine learning approach (Preprint)

2020 ◽  
Author(s):  
Annette Brons ◽  
Antoine de Schipper ◽  
Svetlana Mironcika ◽  
Huub Toussaint ◽  
Ben Schouten ◽  
...  
Keyword(s):  
Machine Learning ◽  
Elementary School ◽  
Motor Skills ◽  
Motor Skill ◽  
Classification Model ◽  
Fine Motor ◽  
Fine Motor Skills ◽  
Fine Motor Skill ◽  
Game Type ◽  
Augmented Toys

BACKGROUND Five to ten percent 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. 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 (fine MABC) and 2) to 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=97, age=7.8±0.7) performed the fine motor skill part of the fine MABC and played two games with the 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 (LoD’s). While playing, both sensor and game data were collected. Four machine learning classifiers were trained with this data to predict the fine MABC 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 LoD’s and types of data for the classifier and game that performed best on accuracy and F1-score. RESULTS The highest achieved mean accuracy (0.76) was achieved with a DT classifier that was trained on both sensor and game data obtained from playing the easiest and hardest level of the roadrunner game. Significant differences in performance were found in accuracy scores between data obtained from the roadrunner and maze game (DT: P=.01; KNN: P=.02; LR: P=.04; SVM: P=.04). No significant differences in performance were found in accuracy scores between the best performing classifier and the other three 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 the best performing LoD (combination of the easiest and hardest level) achieved with the DT classifier trained with sensor and game data obtained from the roadrunner game was only significantly better than the combination of the easiest and middle level (P=.046). CONCLUSIONS The results show that sensor augmented toys can do a good job in predicting the fine MABC score 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 the performance than selecting the machine learning classifier or LoD.

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

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