Jump detection using fuzzy logic

Jump detection and measurement is of particular interest in a wide range of sports, including snowboarding, skiing, skateboarding, wakeboarding, motorcycling, biking, gymnastics, and the high jump, among others. However, determining jump duration and height is often difficult and requires expert knowledge or visual analysis either in real-time or using video. Recent advances in low-cost MEMS inertial sensors enable a data-driven approach to jump detection and measurement. Today, inertial and GPS sensors attached to an athlete or to his or her equipment, e.g. snowboard, skateboard, or skis, can collect data during sporting activities. In these real life applications, effects such as vibration, sensor noise and bias, and various athletic maneuvers make jump detection difficult even using multiple sensors. This paper presents a fuzzy logic-based algorithm for jump detection in sport using accelerometer data. Fuzzy logic facilitates conversion of human intuition and vague linguistic descriptions of jumps to algorithmic form. The fuzzy algorithm described here was applied to snowboarding and ski jumping data, and successfully detected 92% of snowboarding jumps identified visually (rejecting 8% of jumps identified visually), with only 8% of detected jumps being false positives. In ski jumping, it successfully detected 100% of jumps identified visually, with no false positives. The fuzzy algorithm presented here has successfully been applied to automate jump detection in ski and snowboarding on a large scale, and as the basis of the AlpineReplay ski and snowboarding smartphone app, has identified 6370971 jumps from August 2011 through June 2014.