Athletics — The world’s most measurable sport, but at what price? A comparison of fully automatic timing with times measured with standard mobile phones
Peer reviewed, Journal article
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Original versionJohansen, B. & van den Tillaar, R. (2022). Athletics—The world’s most measurable sport, but at what price? A comparison of fully automatic timing with times measured with standard mobile phones. Sensors, 22(7), Article 2774. doi: 10.3390/s22072774
The aim of this study is to quantify potential systemic timing bias between fully automatic timing (FAT) and timing with iPhone camera (Apple Inc., Cupertino, CA, USA) and then consider whether an iPhone can be used as an inexpensive timing system for sprint events at athletics competitions. A flashlight was aimed at FAT camera (Lynx System Developers, Haverhill, MA, USA) and two iPhones, at 120 and 240 frames per second (fps), respectively, so that they could capture the light from it. By turning the flashlight on and off at varying intervals (1–33 s, average 9.5 s), the cameras captured a series of light beams. The time intervals between the start of two light beams were measured 31 times on the recordings from all the cameras. On each recording with the iPhones, two analyses were performed: one where the video image before the light beam (start before light) from the flashlight was set to 0 s and one where the first image with the light beam (start on light) was set to 0 s. Start on light showed no significant time differences compared to FAT. With 240 fps the standard deviation was ± 0.001 s, 29% of the times were the same as FAT, while 81% of the times are within ± 0.001 s. The largest deviation was a time of −0.003 s from FAT. With 120 fps there was a standard deviation of ± 0.003 and a maximum deviation of −0.006 s, where 39% of the times were within ± 0.001 s. At start before light, a significant but expected difference was found with an average deviation of +0.008 s with 120 fps and +0.004 s with 240 fps, with maximum deviations of +0.014 and +0.006 s. It can be concluded that the camera on an iPhone is accurate as we did not find any systematic bias from FAT with start on light.