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.
Highlights
In recent years, human–machine interaction techniques have been spotlighted in robotics research.For human–machine interaction, accurate representation of the human body state is important
Given that the soft sensor has higher nonlinear characteristics compared to traditional sensors [10], deep learning has been applied to characterize soft sensors and motion recognition in [11,12,13]
We introduce the structure of the network using the Long-Short Term Memory (LSTM) units for predicting outputs and utilized the estimated result in our glove system
Summary
For human–machine interaction, accurate representation of the human body state is important. In studies on human motion recognition, various sensors are used to measure human body states [1,2]. If the sensor has a nonlinear characteristic, it is hard to analyze or utilize. To solve this problem, recent studies have proposed the use of networks using deep learning to predict the nonlinear correlation [6,7,8,9]. Given that the soft sensor has higher nonlinear characteristics compared to traditional sensors [10], deep learning has been applied to characterize soft sensors and motion recognition in [11,12,13]
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