Knowledge Commons of Institute of Automation,CAS
Gesture Recognition and Conductivity Reconstruction Parameters Analysis with an Electrical-Impedance-Tomography (EIT) Based Interface: Preliminary Results | |
Liu XiaoDong; Enhao Zheng | |
2021 | |
会议名称 | International Conference on Intelligent Robotics and Applications |
会议日期 | 2021.10.18 |
会议地点 | 山东烟台 |
摘要 | With the development of Human-machine interface (HMI), the requirements of perceiving the human intention are much higher. Electrical Impedance Tomography (EIT) is a promising alternative to existing HMIs because of its portability, non-invasiveness and inexpensiveness. In this study, we designed an EIT-based gesture recognition method achieving the recognition of 9 forearm motion patterns. We analysed the parameters, including current level and contact impedance, which are relevant for practical applications in robotic control. The gesture recognition method produced an average accuracy of 99.845% over nine gestures with PCA and QDA model on one subject. The preliminary results of parameter analysis suggested that the resolution increased with the current amplitude less than a threshold (5.5 mA) but decreased when the current amplitude was over 5.5 mA. The mean value of Region of Interest (ROI) nodes didn’t change obviously when the contact impedance increased. In future works, extensive studies will be conducted on the priori information of forearm and biological-model-based methods to further improve recognition performances in more complicated tasks. |
七大方向——子方向分类 | 智能机器人 |
国重实验室规划方向分类 | 其他 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/56633 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
通讯作者 | Enhao Zheng |
作者单位 | The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China |
推荐引用方式 GB/T 7714 | Liu XiaoDong,Enhao Zheng. Gesture Recognition and Conductivity Reconstruction Parameters Analysis with an Electrical-Impedance-Tomography (EIT) Based Interface: Preliminary Results[C],2021. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Gesture Recognition (2004KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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