Name
Abstract | ||
|---|---|---|
Based on an EMG-adjusted method in neuromusculoskeletal model, this study aims to predict the individual muscle force in shoulder and elbow during table tennis strokes. Muscle force estimation makes muscle activation analysis more physiological in sports. Twenty subjects, divided into professional group and amateur group, were adopted in this study. They were asked to do a basic stoke motion: backhand block. Surface electromyography (sEMG) of nine muscles was recorded, as well as the motion data collected by three inertial sensors. A Hill-type musculotendon model was then adopted to estimate individual muscle force by combining adjusted sEMG and motion data. The result shows that the method can estimate individual muscle force during table tennis strokes accurately, and the two groups show significant difference in muscle force of shoulders and elbows. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BSN.2019.8771082 | 2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN) |
Keywords | Field | DocType |
table tennis,Hill-type musculotendon model,surface electromyography (sEMG),muscle force,upper limb | Muscle force,Computer vision,Upper limb muscle,Elbow,Computer science,Shoulders,Electromyography,Muscle activation,Artificial intelligence,Inertial measurement unit,Physical medicine and rehabilitation,Backhand | Conference |
ISSN | ISBN | Citations |
2376-8886 | 978-1-7281-0804-9 | 0 |
PageRank | References | Authors |
0.34 | 2 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yiming Guo | 1 | 0 | 0.68 |
| yingfei sun | 2 | 2 | 7.17 |
| Yi Ren | 3 | 18 | 4.65 |
| Zhipei Huang | 4 | 44 | 8.71 |
| Jiankang Wu | 5 | 576 | 79.80 |
| Zhiqiang Zhang | 6 | 22 | 6.43 |