Name
Abstract | ||
|---|---|---|
In this paper, an activity testing model was proposed to detect and assess automatic correction of hand pointing. The average recognition rate for automatic corrections of hand pointings was 98.2% using the acceleration data. Moreover, a score was calculated using the activity data of successful recognition and it provided sufficient estimation for the performance level of automatic correction. Experimental results showed that our model was effective and it could be applied to neurorehabilitation. We propose an activity testing model to assess automatic correction of hand pointing.The mechanism of automatic correction of hand pointing provided the theory basis.Two types of hand pointings were calculated and tested.The score from the proposed scoring system can provide sufficient estimation.Our testing model was effective and it could be applied to neurorehabilitation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ipl.2016.06.008 | Inf. Process. Lett. |
Keywords | Field | DocType |
Activity testing,Automatic correction,Hand pointing,Rehabilitation,Design of algorithms | Computer vision,Computer science,Speech recognition,Acceleration,Artificial intelligence,Neurorehabilitation | Journal |
Volume | Issue | ISSN |
116 | 11 | 0020-0190 |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yalin Song | 1 | 0 | 0.34 |
| Yaoru Sun | 2 | 212 | 16.11 |
| Zhang Hong | 3 | 18 | 3.74 |
| Fang Wang | 4 | 14 | 2.29 |