Name
Abstract | ||
|---|---|---|
Martial arts can promote healthy lifestyles, improve self-confidence and provide self-defence skills. Previous work has demonstrated that inertial sensors can be used to recognise movements such as punches in boxing and support self-directed training. However, many martial arts do not use gloves which means that punches can be performed with different parts of the hand, and therefore produce a different sound on impact. We investigate if it is possible to recognise different punches executed with a bare hand, and if the recognition rate improves by combining audio input with the traditional inertial sensors. We conducted a pilot study collecting a total of 600 punches, using a wearable wristband to capture inertial data and a stand-alone microphone for audio input. The results showed that it was possible to distinguish five types of punches with 94.4% accuracy when using only inertial data, and that adding audio input did not improve the accuracy. These findings can guide the design of future wearables for punch recognition.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3335595.3335641 | Proceedings of the XX International Conference on Human Computer Interaction |
Keywords | Field | DocType |
gesture recognition, inertial sensors, machine learning, martial arts, punch recognition, wearables | Inertial frame of reference,Computer science,Wearable computer,Gesture recognition,Martial arts,Human–computer interaction,Inertial measurement unit,Microphone | Conference |
ISBN | Citations | PageRank |
978-1-4503-7176-6 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Quintero Ovalle | 1 | 0 | 0.34 |
| Katarzyna Stawarz | 2 | 67 | 7.29 |
| Asier Marzo | 3 | 53 | 12.59 |