Papers › Activity recognition using ST-GCN with 3D motion data
Activity recognition using ST-GCN with 3D motion data
Xin Cao, Wataru Kudo, Chihiro Ito, Masaki Shuzo, Eisaku Maeda
For the Nurse Care Activity Recognition Challenge, an activity recognition algorithm was developed by Team TDU-DSML. A spatial-temporal graph convolutional network (ST-GCN) was applied to process 3D motion capture data included in the challenge dataset. Time-series data was divided into 20-second segments with a 10-second overlap. The recognition model with a tree-structure graph was then created. The prediction result was set to one-minute segments on the basis of a majority decision from each segment output. Our model was evaluated by using leave-one-subject-out cross-validation methods. An average accuracy of 57% for all six subjects was achieved.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multimodal Activity Recognition | Nurse Care Activity Recognition Challenge | ST-GCN | Accuracy | 64.6% | #2 of 2 | Archive leaderboard | report |
| Multimodal Activity Recognition | Nurse Care Activity Recognition Challenge | ST-GCN | Train F-measure | 52.9% | #2 of 2 | Archive leaderboard | report |
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