Patellar tendinopathy in junior basketball players : a controlled clinical and ultrasonographic study of 268 patellar tendons in players aged 14-18 years

Anterior knee pain is a common presenting complaint amongst adolescent athletes. We hypothesised that patellar tendinopathy may occur at a younger age than is generally recognised. Thus, we studied the patellar tendons in 134 elite 14- to 18-year-old female (n=64) and male (n=70) basketball players and 29 control swimmers (17 female, 12 male) clinically and with ultrasonography. We found that of 268 tendons, 19 (7%) had current patellar tendinopathy on clinical grounds (11% in males, 2% in females). Twenty-six percent of the basketball players' patellar tendons contained an ultrasonographic hypoechoic region. Ultrasonographic abnormality was more prevalent in the oldest tertile of players (17-18 years) than the youngest tertile (14-15.9 years). Of tendons categorised clinically as 'Never patellar tendinopathy', 22% had an ultrasonographic hypoechoic region nevertheless. This study indicates that patellar tendinopathy can occur in 14- to 18-year-old basketball players. Ultrasonographic tendon abnormality is 3 times as common as clinical symptoms

Swimmer localization from a moving camera

At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.