CASIA OpenIR  > 复杂系统管理与控制国家重点实验室  > 先进机器人
Hand Gesture Recognition using MYO Armband
Shunzhan He; Chenguan Yang; Min Wang; Long Cheng
2017
Conference NameChinese Automation Congress (CAC)
Conference DateOCT 20-22, 2017
Conference PlaceJinan
CountryChina
AbstractSurface 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%.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23122
Collection复杂系统管理与控制国家重点实验室_先进机器人
Recommended Citation
GB/T 7714
Shunzhan He,Chenguan Yang,Min Wang,et al. Hand Gesture Recognition using MYO Armband[C],2017.
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