Development of Pinching Motion Classification Method Using EIT-Based Tactile Sensor
- Resource Type
- Periodical
- Authors
- Asahi, R.; Yoshimoto, S.; Sato, H.
- Source
- IEEE Access Access, IEEE. 12:62089-62098 2024
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Electrodes
Voltage measurement
Thumb
Tactile sensors
Image reconstruction
Vectors
Motors
Impedance
Haptic interfaces
Electrical impedance tomography
fine motor skills
motion analysis
tactile sensor
- Language
- ISSN
- 2169-3536
Fine motor skills have been suggested to be related to human cognitive abilities. To develop an objective method for evaluating fine motor skills, we applied a flexible tactile sensor based on electrical impedance tomography (EIT) and the contact resistance principle to a cylinder designed to mimic the peg used in the Functional Dexterity Test. Six pinching motions were classified to confirm the feasibility of the prototype system. Two types of classification were performed: classification using reconstructed images and classification using measured voltage vectors. The feasibility of the classification method was evaluated using adult participants, and it was demonstrated that the system can accurately classify various types of pinching motions. The results revealed that utilizing reconstructed images for classification achieved a classification accuracy of 79.4%, while employing measured voltage vectors for classification resulted in a classification accuracy of 91.4%. These findings underscore the potential for developing an automated finger motion analysis system using EIT-based tactile sensor.