KEYWORDS: Video, RGB color model, Skin, Light sources and illumination, Injuries, Algorithm development, Detection and tracking algorithms, Data acquisition, Data processing, Motion analysis
Sports cycling is becoming increasingly popular over last years. Obtaining and maintaining a proper position on the bike has been shown to be crucial for performance, comfort and injury avoidance. Various techniques of bike fitting are available - from rough settings based on body dimensions to professional services making use of sophisticated equipment and expert knowledge. Modern fitting techniques use mainly joint angles as a criterion of proper position. In this work we examine performance of two proposed methods for dynamic cyclist position assessment based on video data recorded during stationary cycling. Proposed methods are intended for home use, to help amateur cyclist improve their position on the bike, and therefore no professional equipment is used. As a result of data processing, ranges of angles in selected joints are provided. Finally strengths and weaknesses of both proposed methods are discussed.
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