Hierarchical Co-occurrence Network from Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation Contributions PyTorch reimplementation of Hierarchical Co-occurrence Network (HCN) Application of Prototype loss during training of HCN Experiments showing that training with prototype loss can achieve similar accuracy
Input variables : NTU RGB+D Dataset
Output Variables : Identification of 60 Action Classes in NTU RGB+D
A1. drink water.
A2. eat meal/snack.
A3. brushing teeth.
A4. brushing hair.
A5. drop.
A6. pickup.
A7. throw.
A8. sitting down.
A9. standing up (from sitting position).
A10. clapping.
A11. reading.
A12. writing.
A13. tear up paper.
A14. wear jacket.
A15. take off jacket.
A16. wear a shoe.
A17. take off a shoe.
A18. wear on glasses.
A19. take off glasses.
A20. put on a hat/cap.
A21. take off a hat/cap.
A22. cheer up.
A23. ha
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : github.com | github.com | github.com
| Model Category | : | Public |
| Date Published | : | March, 2019 |
| Healthcare Domain | : | Provider |
| Code | : | github.com |
| Medical Imaging |
| Health Risk Management |
| Health Risk Prediction |