Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network

Title
Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network
Authors
차영수김강건김성희권용찬
Keywords
deep learning; soft sensors and actuators; virtual reality and interfaces; wearable robots
Issue Date
2020-03
Publisher
Applied Sciences-basel
Citation
VOL 10, NO 6, 2194
Abstract
Soft sensors are attracting significant attention in human– machine interaction due to their high flexibility and adaptability. However, estimating motion state from these sensors is difficult due to their nonlinearity and noise. In this paper, we propose a deep learning network for a smart glove system to predict the moving state of a piezoelectric soft sensor. We implemented the network using Long-Short Term Memory (LSTM) units and demonstrated its performance in a real-time system based on two experiments. The sensor’s moving state was estimated and the joint angles were calculated. Since we use moving state in the sensor offset calculation and the offset value is used to estimate the angle value, the accurate moving state estimation results in good performance for angle value estimation. The proposed network performed better than the conventional heuristic method in estimating the moving state. It was also confirmed that the calculated values successfully mimic the joint angles measured using a leap motion controller.
URI
http://pubs.kist.re.kr/handle/201004/71121
ISSN
2076-3417
Appears in Collections:
KIST Publication > Article
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE