Name
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.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3372342 | ACM Transactions on Internet of Things |
Keywords | DocType | Volume |
Soccer,activity recognition,dynamic time warping,event detection,goalkeeping,machine learning,signal processing,wearable sensor | Journal | 1 |
Issue | ISSN | Citations |
2 | 2691-1914 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Haladjian | 1 | 17 | 6.75 |
| Daniel Schlabbers | 2 | 0 | 0.34 |
| Sajjad Taheri | 3 | 8 | 2.86 |
| Max Tharr | 4 | 0 | 0.34 |
| Bernd Bruegge | 5 | 0 | 0.34 |