Research Article
Detecting delays in motor skill development of children through data analysis of a smart play device
@INPROCEEDINGS{10.1145/3154862.3154867, author={J\o{}rg Sander and Antoine de Schipper and Annette Brons and Svetlana Mironcika and Huub Toussaint and Ben Schouten and Ben Kr\o{}se}, title={Detecting delays in motor skill development of children through data analysis of a smart play device}, proceedings={11th EAI International Conference on Pervasive Computing Technologies for Healthcare}, publisher={ACM}, proceedings_a={PERVASIVEHEALTH}, year={2018}, month={1}, keywords={games for health motor skill assessment machine learning feature extraction}, doi={10.1145/3154862.3154867} }- Jörg Sander
Antoine de Schipper
Annette Brons
Svetlana Mironcika
Huub Toussaint
Ben Schouten
Ben Kröse
Year: 2018
Detecting delays in motor skill development of children through data analysis of a smart play device
PERVASIVEHEALTH
ACM
DOI: 10.1145/3154862.3154867
Abstract
This paper describes experiments with a game device that was used for early detection of delays in motor skill development in primary school children. Children play a game by bi-manual manipulation of the device which continuously collects accelerometer data and game state data. Features of the data are used to discriminate between normal children and children with delays. This study focused on the feature selection. Three features were compared: mean squared jerk (time domain); power spectral entropy (fourier domain) and cosine similarity measure (quality of game play). The discriminatory power of the features was tested in an experiment where 28 children played games of different levels of difficulty. The results show that jerk and cosine similarity have reasonable discriminatory power to detect fine-grained motor skill development delays especially when taking the game level into account. Duration of a game level needs to be at least 30 seconds in order to achieve good classification results.