↓ Skip to main content

An automated approach to measuring child movement and location in the early childhood classroom

Overview of attention for article published in Behavior Research Methods, June 2017
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
124 Mendeley
Title
An automated approach to measuring child movement and location in the early childhood classroom
Published in
Behavior Research Methods, June 2017
DOI 10.3758/s13428-017-0912-8
Pubmed ID
Authors

Dwight W. Irvin, Stephen A. Crutchfield, Charles R. Greenwood, William D. Kearns, Jay Buzhardt

Abstract

Children's movement is an important issue in child development and outcome in early childhood research, intervention, and practice. Digital sensor technologies offer improvements in naturalistic movement measurement and analysis. We conducted validity and feasibility testing of a real-time, indoor mapping and location system (Ubisense, Inc.) within a preschool classroom. Real-time indoor mapping has several implications with respect to efficiently and conveniently: (a) determining the activity areas where children are spending the most and least time per day (e.g., music); and (b) mapping a focal child's atypical real-time movements (e.g., lapping behavior). We calibrated the accuracy of Ubisense point-by-point location estimates (i.e., X and Y coordinates) against laser rangefinder measurements using several stationary points and atypical movement patterns as reference standards. Our results indicate that activity areas occupied and atypical movement patterns could be plotted with an accuracy of 30.48 cm (1 ft) using a Ubisense transponder tag attached to the participating child's shirt. The accuracy parallels findings of other researchers employing Ubisense to study atypical movement patterns in individuals at risk for dementia in an assisted living facility. The feasibility of Ubisense was tested in an approximately 90-min assessment of two children, one typically developing and one with Down syndrome, during natural classroom activities, and the results proved positive. Implications for employing Ubisense in early childhood classrooms as a data-based decision-making tool to support children's development and its potential integration with other wearable sensor technologies are discussed.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 124 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 124 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 15%
Researcher 16 13%
Student > Master 15 12%
Student > Bachelor 8 6%
Student > Doctoral Student 7 6%
Other 15 12%
Unknown 44 35%
Readers by discipline Count As %
Psychology 17 14%
Social Sciences 10 8%
Medicine and Dentistry 9 7%
Nursing and Health Professions 8 6%
Computer Science 6 5%
Other 27 22%
Unknown 47 38%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2018.
All research outputs
#20,660,571
of 25,382,440 outputs
Outputs from Behavior Research Methods
#1,981
of 2,526 outputs
Outputs of similar age
#255,184
of 331,454 outputs
Outputs of similar age from Behavior Research Methods
#34
of 38 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,526 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 331,454 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 38 others from the same source and published within six weeks on either side of this one. This one is in the 10th percentile – i.e., 10% of its contemporaries scored the same or lower than it.