Forearm Motion Recognition With Noncontact Capacitive Sensing
Zheng, Enhao1; Mai, Jingeng2,3; Liu, Yuxiang2; Wang, Qining2,3
Source PublicationFRONTIERS IN NEUROROBOTICS
2018-07-27
Volume12Issue:12Pages:1-13
SubtypeArticle
AbstractThis 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.
KeywordNoncontact Capacitive Sensing Upper-limb Motion Recognition Human-machine Interface Automatic Data Labeling Robot Learning From Humans
WOS HeadingsScience & Technology ; Technology ; Life Sciences & Biomedicine
DOI10.3389/fnbot.2018.00047
WOS KeywordPATTERN-RECOGNITION ; ARM ; ELECTROMYOGRAPHY ; CONTRACTION ; AMPUTEES ; ROBUST
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61703400 ; Beijing Natural Science Foundation(L172052) ; 91648207)
WOS Research AreaComputer Science ; Robotics ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS IDWOS:000440030700001
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21727
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Affiliation1.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
Recommended Citation
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.
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