Sensor-based Detection and Classification of Soccer Goalkeeper Training Exercises
Juan Haladjian, Daniel Schlabbers, Sajjad Taheri, Max Tharr, Bernd Bruegge
Abstract
Many goalkeeper trainees cannot afford a personal human coach. Hence, they could benefit from a virtual coach that provides personalized feedback about the execution of their training exercises. As a first step towards this goal, we developed an algorithm to detect and classify goalkeeper training exercises using a wearable inertial sensor attached to a goalkeeper glove. We collected data from 14 goalkeeper trainees while performing a series of training exercises (e.g., dives, catches, throws). Our approach first detects the exercises using an event detection algorithm based on a high-pass filter, a peak detector, and Dynamic Time Warping to detect and eliminate irrelevant motion instances. Then, it extracts a set of statistical and heuristic features to describe the different exercises and train a machine learning classifier. Our exercise detection approach retrieves 93.8% of the relevant exercises with 90.6% precision and classifies the detected exercises with an accuracy of 96.5%. The exercises recognized by our algorithm can be used to compute further qualitative metrics about individual exercise executions to provide goalkeepers with relevant feedback about their training.