Abstract
Bending sensors enable compact, wearable designs when used for measuring hand configurations in data gloves. While existing data gloves can accurately measure angular displacement of the finger and distal thumb joints, accurate measurement of thumb carpometacarpal (CMC) joint movements remains challenging due to crosstalk between the multi-sensor outputs required to measure the degrees of freedom (DOF). To properly measure CMC-joint configurations, sensor locations that minimize sensor crosstalk must be identified. This paper presents a novel approach to identifying optimal sensor locations. Three-dimensional hand surface data from ten subjects was collected in multiple thumb postures with varied CMC-joint flexion and abduction angles. For each posture, scanned CMC-joint contours were used to estimate CMC-joint flexion and abduction angles by varying the positions and orientations of two bending sensors. Optimal sensor locations were estimated by the least squares method, which minimized the difference between the true CMC-joint angles and the joint angle estimates. Finally, the resultant optimal sensor locations were experimentally validated. Placing sensors at the optimal locations, CMC-joint angle measurement accuracies improved (flexion, 2.8° ± 1.9°; abduction, 1.9° ± 1.2°). The proposed method for improving the accuracy of the sensing system can be extended to other types of soft wearable measurement devices.
Recommended Citation
D. Kim et al., "Improving Kinematic Accuracy of Soft Wearable Data Gloves by Optimizing Sensor Locations," Sensors (Switzerland), vol. 16, no. 6, MDPI AG, May 2016.
The definitive version is available at https://doi.org/10.3390/s16060766
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
3D measurement; Kinematic accuracy; Sensor locations; Thumb-joint angle estimation; Wearable hand device
International Standard Serial Number (ISSN)
1424-8220; 1424-8220
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2016 The Authors AG, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
01 May 2016
PubMed ID
27240364
Comments
This research was supported by the convergence technology development program for bionic arms through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (2015M3C1B2052817), and this research was supported in part by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2013R1A1A2063097).