Individual classification of elementary school children's physical activity. A time-efficient group-based approach to reference measurements.

Autor/inn/en	Kühnhausen, Jan; Dirk, Judith; Schmiedek, Florian
Titel	Individual classification of elementary school children's physical activity. A time-efficient group-based approach to reference measurements.
Quelle	In: Behavior research methods, 49 (2017) 2, S. 685-697 PDF als Volltext Link als defekt meldenVerfügbarkeit
Sprache	englisch
Dokumenttyp	online; Zeitschriftenaufsatz
ISSN	1554-3528
DOI	10.3758/s13428-016-0724-2
Schlagwörter	Schuljahr 03; Schuljahr 04; Schüler; Aktivität; Modellbildung; Mustererkennung; Messung; Bewegung (Motorische); Gruppe (Soz)
Abstract	The objective measurement of physical activity using accelerometers is becoming increasingly popular. There is little consensus, however, about how to analyze acceleration data. One promising approach is the use of reference measurements in which the subjects conduct specific activities. This makes it possible to identify data patterns that indicate these activities for each subject. The drawback of this approach is its rather high cost, in terms of both time and money. We propose a new approach in which a group of children conduct the reference measurements at the same time. We trained support vector machine models on the accelerometer data of 70 children (ages 8-11 years) to predict their activities during those reference measurements. We correctly classified activities with an accuracy of 96.9 % when fitting the individual models for each subject, and 87.5 % when fitting general models for all subjects. The obtained accuracies were comparable to results reported in previous reference measurement studies, in which each subject was measured individually. They were higher than the accuracies obtained by the traditional approach, which transfers accelerometer data to counts and classifies those on the basis of predefined cut points. We concluded that our approach can yield a valuable contribution, particularly to studies with larger samples. (DIPF/Orig.).
Erfasst von	DIPF \| Leibniz-Institut für Bildungsforschung und Bildungsinformation, Frankfurt am Main
Update	2023/1