|
|
|
|
| LEADER |
01000caa a22002652 4500 |
| 001 |
NLM369922727 |
| 003 |
DE-627 |
| 005 |
20240807232509.0 |
| 007 |
cr uuu---uuuuu |
| 008 |
240320s2024 xx |||||o 00| ||eng c |
| 024 |
7 |
|
|a 10.1109/TPAMI.2024.3378753
|2 doi
|
| 028 |
5 |
2 |
|a pubmed24n1494.xml
|
| 035 |
|
|
|a (DE-627)NLM369922727
|
| 035 |
|
|
|a (NLM)38502632
|
| 040 |
|
|
|a DE-627
|b ger
|c DE-627
|e rakwb
|
| 041 |
|
|
|a eng
|
| 100 |
1 |
|
|a Yu, Bruce X B
|e verfasserin
|4 aut
|
| 245 |
1 |
0 |
|a EGCN++
|b A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment
|
| 264 |
|
1 |
|c 2024
|
| 336 |
|
|
|a Text
|b txt
|2 rdacontent
|
| 337 |
|
|
|a ƒaComputermedien
|b c
|2 rdamedia
|
| 338 |
|
|
|a ƒa Online-Ressource
|b cr
|2 rdacarrier
|
| 500 |
|
|
|a Date Completed 06.08.2024
|
| 500 |
|
|
|a Date Revised 07.08.2024
|
| 500 |
|
|
|a published: Print-Electronic
|
| 500 |
|
|
|a Citation Status MEDLINE
|
| 520 |
|
|
|a Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed. We previously proposed an ensemble-based graph convolutional network (EGCN) that considers both position and orientation features to construct a model-based approach. Integrating these types of features achieved better performance than available methods. However, EGCN lacked a fusion strategy across the data, feature, decision, and model levels. In this paper, we present an advanced framework, EGCN++, for rehabilitation exercise assessment. Based on EGCN, a new fusion strategy called MLE-PO is proposed for EGCN++; this technique considers fusion at the data and model levels. We conduct extensive cross-validation experiments and investigate the consistency between machine and human evaluations on three datasets: UI-PRMD, KIMORE, and EHE. Results demonstrate that MLE-PO outperforms other EGCN ensemble strategies and representative baselines. Furthermore, the MLE-PO's model evaluation scores are more quantitatively consistent with clinical evaluations than other ensemble strategies
|
| 650 |
|
4 |
|a Journal Article
|
| 700 |
1 |
|
|a Liu, Yan
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chan, Keith C C
|e verfasserin
|4 aut
|
| 700 |
1 |
|
|a Chen, Chang Wen
|e verfasserin
|4 aut
|
| 773 |
0 |
8 |
|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 46(2024), 9 vom: 19. Aug., Seite 6471-6485
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
|
| 773 |
1 |
8 |
|g volume:46
|g year:2024
|g number:9
|g day:19
|g month:08
|g pages:6471-6485
|
| 856 |
4 |
0 |
|u http://dx.doi.org/10.1109/TPAMI.2024.3378753
|3 Volltext
|
| 912 |
|
|
|a GBV_USEFLAG_A
|
| 912 |
|
|
|a SYSFLAG_A
|
| 912 |
|
|
|a GBV_NLM
|
| 912 |
|
|
|a GBV_ILN_350
|
| 951 |
|
|
|a AR
|
| 952 |
|
|
|d 46
|j 2024
|e 9
|b 19
|c 08
|h 6471-6485
|