| Popis: |
BackgroundSwimmers commonly access performance metrics such as lap splits, distance, and pacing information between work bouts while they rest. Recently, a new category of tracking devices for swimming was introduced with the FORM Smart Swim Goggles (FORM Goggles). The goggles have a built-in see-through display and are capable of tracking and displaying distance, time splits, stroke, and pace metrics in real time using machine learning and augmented reality through a heads-up display. The purpose of this study was to assess the validity and reliability of the FORM Goggles compared with video analysis for stroke type, pool length count, pool length time, stroke rate, and stroke count in recreational swimmers and triathletes.MethodA total of 36 participants performed mixed swimming intervals in a 25-m pool across two identical 900-m swim sessions performed at comparable intensities with 1 week interval. The participants wore FORM Goggles during their swims, which detected the following five swim metrics: stroke type, pool length time, pool length count, stroke count, and stroke rate. Four video cameras were positioned on the pool edges to capture ground truth video footage, which was then manually labeled by three trained individuals. Mean (SD) differences between FORM Goggles and ground truth were calculated for the selected metrics for both sessions. The absolute mean difference and mean absolute percentage error were used to assess the differences of the FORM Goggles relative to ground truth. The test–retest reliability of the goggles was assessed using both relative and absolute reliability metrics.ResultsCompared with video analysis, the FORM Goggles identified the correct stroke type at a rate of 99.7% (N = 2,354 pool lengths, p |
