Forearm Motion Recognition With Noncontact Capacitive Sensing

doi:10.3389/fnbot.2018.00047

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 机器人应用与理论组

	Forearm Motion Recognition With Noncontact Capacitive Sensing
	Zheng, Enhao1 ; Mai, Jingeng 2,3; Liu, Yuxiang 2; Wang, Qining 2,3
刊名	FRONTIERS IN NEUROROBOTICS
	2018-07-27
卷号	12 期号:12 页码:1-13
关键词	Noncontact Capacitive Sensing Upper-limb Motion Recognition Human-machine Interface Automatic Data Labeling Robot Learning From Humans
DOI	10.3389/fnbot.2018.00047
文献子类	Article
英文摘要	This study presents a noncontact capacitive sensing method for forearm motion recognition. A method is proposed to record upper limb motion information from muscle contractions without contact with human skin, compensating for the limitations of existing sEMG-based methods. The sensing front-ends are designed based on human forearm shapes, and the forearm limb shape changes caused by muscle contractions will be represented by capacitance signals. After implementation of the capacitive sensing system, experiments on healthy subjects are conducted to evaluate the effectiveness. Nine motion patterns combined with 16 motion transitions are investigated on seven participants. We also designed an automatic data labeling method based on inertial signals from the measured hand, which greatly accelerated the training procedure. With the capacitive sensing system and the designed recognition algorithm, the method produced an average recognition of over 92%. Correct decisions could be made with approximately a 347-ms delay from the relaxed state to the time point of motion initiation. The confounding factors that affect the performances are also analyzed, including the sliding window length, the motion types and the external disturbances. We found the average accuracy increased to 98.7% when five motion patterns were recognized. The results of the study proved the feasibility and revealed the problems of the noncontact capacitive sensing approach on upper-limb motion sensing and recognition. Future efforts in this direction could be worthwhile for achieving more promising outcomes.
WOS关键词	PATTERN-RECOGNITION ; ARM ; ELECTROMYOGRAPHY ; CONTRACTION ; AMPUTEES ; ROBUST
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
语种	英语
WOS记录号	WOS:000440030700001
资助机构	National Natural Science Foundation of China(61703400 ; Beijing Natural Science Foundation(L172052) ; 91648207)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/21727]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_机器人应用与理论组
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Peking Univ, Coll Engn, Robot Res Grp, Beijing, Peoples R China 3.Peking Univ, BIC ESAT, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Zheng, Enhao,Mai, Jingeng,Liu, Yuxiang,et al. Forearm Motion Recognition With Noncontact Capacitive Sensing[J]. FRONTIERS IN NEUROROBOTICS,2018,12(12):1-13.
APA	Zheng, Enhao,Mai, Jingeng,Liu, Yuxiang,&Wang, Qining.(2018).Forearm Motion Recognition With Noncontact Capacitive Sensing.FRONTIERS IN NEUROROBOTICS,12(12),1-13.
MLA	Zheng, Enhao,et al."Forearm Motion Recognition With Noncontact Capacitive Sensing".FRONTIERS IN NEUROROBOTICS 12.12(2018):1-13.