Name
Abstract | ||
|---|---|---|
Evaluating motion quality has many applications in health promotion and exercise coaching. This study aimed to develop an approach for automatic and cost-efficient evaluation on motion quality using the Nintendo Wii Balance Board (WBB) and machine learning techniques. We conducted a pilot study with twelve participants to collect data of chest rotation and hip joint rotation. We used support vector machine for automatic classification of good and poor motions. The preliminary results suggested that using WBB for motion quality evaluation is feasible. Future studies are needed to improve the accuracy of the classification model and to investigate the health impact of the proposed approach. |
Year | Venue | Keywords |
|---|---|---|
2017 | IEEE Global Conference on Consumer Electronics | Nintendo Wii Balance Board,motion quality,pervasive computing,health |
Field | DocType | ISSN |
Computer science,Support vector machine,Feature extraction,Balance board,Coaching,Artificial intelligence,Machine learning,Health promotion | Conference | 2378-8143 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zilu Liang | 1 | 38 | 10.72 |
| Takuichi Nishimura | 2 | 576 | 65.34 |
| Nami Iino | 3 | 0 | 1.35 |
| Yasuyuki Yoshida | 4 | 0 | 1.01 |
| Satoshi Nishimura | 5 | 1 | 5.14 |