Name
Abstract | ||
|---|---|---|
Time-varying synergies from kinematic data can be used to discern fundamental patterns of movement. We show through simultaneous extraction of synergies from both novice and experienced pianists that movement common to both groups can be identified. The extracted synergies successfully allow for the majority of the variability of the data to be accounted for by a limited number of components. Furthermore, classification of the weightings representing the recruitment of each of the synergies accurately distinguishes between the piano playing of the two groups of subjects. However, the major differences between the two groups lie not in the synergies representing the majority of the variance of the data but in the recruitment of smaller synergies. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/EMBC.2015.7318345 | 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Keywords | Field | DocType |
Biomechanical Phenomena,Female,Humans,Male,Movement,Music,Nontherapeutic Human Experimentation,Young Adult | Computer vision,Engineering drawing,Kinematics,Computer science,Human–computer interaction,Artificial intelligence,Piano playing,Biomechanical Phenomena | Conference |
Volume | ISSN | Citations |
2015 | 1094-687X | 1 |
PageRank | References | Authors |
0.39 | 2 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Beth Jelfs | 1 | 62 | 9.40 |
| Shengli Zhou | 2 | 3909 | 262.81 |
| Bernard K Y Wong | 3 | 1 | 0.39 |
| Chung Tin | 4 | 14 | 2.78 |
| Rosa H M Chan | 5 | 182 | 22.79 |