| Hand Gesture Recognition using MYO Armband | |
| Shunzhan He; Chenguan Yang; Min Wang; Long Cheng | |
| 2017 | |
| 会议日期 | OCT 20-22, 2017 |
| 会议地点 | Jinan |
| 国家 | China |
| 英文摘要 | Surface electromyography (sEMG) is widely used in clinical diagnosis, rehabilitation engineering and human-computer interaction and other fields. In this paper, we use Myo armband to collect sEMG signals. Myo armband can be worn above any elbow of any arm and it can capture the bioelectric signal generated when the arm muscles move. MYO can pass of signals through its low-power Blue-tooth, and its interference is small, which makes the signal quality really good. By collecting the sEMG signals of the upper limb forearm, we extract five eigenvalues in the time domain, and use the BP neural network classification algorithm to realize the recognition of six gestures in this paper. Experimental results show that the use of MYO for gesture recognition can get a very good recognition results, it can accurately identify the six hand movements with the average recognition rate of 93%. |
| 内容类型 | 会议论文 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/23122]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 推荐引用方式 GB/T 7714 | Shunzhan He,Chenguan Yang,Min Wang,et al. Hand Gesture Recognition using MYO Armband[C]. 见:. Jinan. OCT 20-22, 2017. |
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