Next Article in Journal
Optics Based Label-Free Techniques and Applications in Brain Monitoring
Previous Article in Journal
Surface Radiation Balance of Urban Materials and Their Impact on Air Temperature of an Urban Canyon in Lisbon, Portugal
Previous Article in Special Issue
Flexible Shear and Normal Force Sensor Using only One Layer of Polyvinylidene Fluoride Film
Open AccessFeature PaperArticle

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

1
Center for Intelligent and Interactive Robotics, Korea Institute of Science and Technology, Seoul 02792, Korea
2
Department of Mechanical Engineering, Soongsil University, Seoul 07040, Korea
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(6), 2194; https://doi.org/10.3390/app10062194
Received: 21 February 2020 / Revised: 20 March 2020 / Accepted: 20 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Flexible Piezoelectric Materials)
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. View Full-Text
Keywords: deep learning; soft sensors and actuators; virtual reality and interfaces; wearable robots deep learning; soft sensors and actuators; virtual reality and interfaces; wearable robots
Show Figures

Figure 1

MDPI and ACS Style

Kim, S.H.; Kwon, Y.; Kim, K.; Cha, Y. Estimation of Hand Motion from Piezoelectric Soft Sensor Using Deep Recurrent Network. Appl. Sci. 2020, 10, 2194.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop