Analysis of Accelerometer-Derived Interpersonal Spatial Proximities: A Calibration, Simulation, and Validation Study

Autor/inn/en	McCullough, Aston K.; Keller, Bryan S.; Qiu, Shumin; Garber, Carol Ewing
Titel	Analysis of Accelerometer-Derived Interpersonal Spatial Proximities: A Calibration, Simulation, and Validation Study
Quelle	In: Measurement in Physical Education and Exercise Science, 22 (2018) 3, S.275-286 (12 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (McCullough, Aston K.) ORCID (Garber, Carol Ewing)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1091-367X
DOI	10.1080/1091367X.2018.1437039
Schlagwörter	Measurement Equipment; Proximity; Simulation; Prediction; Computation; Radio; Statistical Analysis; Questionnaires; Physical Activity Level; Young Children; Parents; Urban Areas; Observation; Measurement Techniques + Suchen Sie Ihr Suchwort? Messinstrument; Lebensnähe; Simulation program; Simulationsprogramm; Vorhersage; Statistische Analyse; Fragebogen; Frühe Kindheit; Eltern; Urban area; Stadtregion; Beobachtung; Messtechnik
Abstract	The purpose of this study was to estimate distances from accelerometer-derived Bluetooth signals as a measure of interpersonal spatial proximity. Accelerometer-derived proximity data were collected indoors and outdoors over a 10m range to calibrate simulation models. Proximity data were simulated over 20m (indoor) and 50m (outdoor) ranges. Competing statistical and machine learning models were used to predict simulated distances; the Root-Mean-Square-Error (RMSE) was calculated. Simulation estimates were validated under conditions wherein a single beacon-receiver (SBR) and multiple beacons-receivers (MBR) collected proximity data indoors and outdoors within a =10m range. Simulation data showed that a Random Forest (RF) model performed optimally. The validated RF RMSE was =2.7 for SBR, and =90% of predicted distances were accurately classified as =10m. For MBR, =67% of predicted distances were accurately classified as =10m. Simulation and validation data suggest that distances can be estimated from accelerometer-derived proximity data within a 20m range using a SBR. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01