Abstract
The present study aims to identify the accuracy of the NBN23® system, an indoor tracking system based on radio-frequency and standard Bluetooth Low Energy channels. Twelve capture tags were attached to a custom cart with fixed distances of 0.5, 1.0, 1.5, and 1.8 m. The cart was pushed along a predetermined course following the lines of a standard dimensions Basketball court. The course was performed at low speed (<10.0 km/h), medium speed (>10.0 km/h and <20.0 km/h) and high speed (>20.0 km/h). Root mean square error (RMSE) and percentage of variance accounted for (%VAF) were used as accuracy measures. The obtained data showed acceptable accuracy results for both RMSE and %VAF, despite the expected degree of error in position measurement at higher speeds. The RMSE for all the distances and velocities presented an average absolute error of 0.30 ± 0.13 cm with 90.61 ± 8.34 of %VAF, in line with most available systems, and considered acceptable for indoor sports. The processing of data with filter correction seemed to reduce the noise and promote a lower relative error, increasing the %VAF for each measured distance. Research using positional-derived variables in Basketball is still very scarce; thus, this independent test of the NBN23® tracking system provides accuracy details and opens up opportunities to develop new performance indicators that help to optimize training adaptations and performance.
Highlights
Team sports are very complex to describe, because players perform under a very wide range of environmental information, requiring constant decision making in stressful scenarios [1]
This study aims to identify the accuracy of the NBN23® system, an indoor tracking system based on standard
This study aimed to identify the accuracy of the NBN23® indoor tracking system, using a cart with tags positioned at known distances
Summary
Team sports are very complex to describe, because players perform under a very wide range of environmental information, requiring constant decision making in stressful scenarios [1]. Dynamic behavior under these circumstances is modelled by the ability to identify, interpret, and even predict the actions of teammates and opponents [2]. In this sense, data collection using video and computer tracking systems can be used to generate critical information about movement patterns, allowing in-depth analyses of locomotion [3,4]
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