Next Article in Journal
A Closed-form Expression to Estimate the Uncertainty of THD Starting from the LPIT Accuracy Class
Previous Article in Journal
Bio-Inspired Strategies for Improving the Selectivity and Sensitivity of Artificial Noses: A Review
Previous Article in Special Issue
Application-Based Production and Testing of a Core–Sheath Fiber Strain Sensor for Wearable Electronics: Feasibility Study of Using the Sensors in Measuring Tri-Axial Trunk Motion Angles
Open AccessArticle

Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture

Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 04620, Korea
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(6), 1801; https://doi.org/10.3390/s20061801
Received: 27 January 2020 / Revised: 6 March 2020 / Accepted: 9 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Soft Sensors for Motion Capture and Analysis)
The importance of estimating human movement has increased in the field of human motion capture. HTC VIVE is a popular device that provides a convenient way of capturing human motions using several sensors. Recently, the motion of only users’ hands has been captured, thereby greatly reducing the range of motion captured. This paper proposes a framework to estimate single-arm orientations using soft sensors mainly by combining a Bi-long short-term memory (Bi-LSTM) and two-layer LSTM. Positions of the two hands are measured using an HTC VIVE set, and the orientations of a single arm, including its corresponding upper arm and forearm, are estimated using the proposed framework based on the estimated positions of the two hands. Given that the proposed framework is meant for a single arm, if orientations of two arms are required to be estimated, the estimations are performed twice. To obtain the ground truth of the orientations of single-arm movements, two Myo gesture-control sensory armbands are employed on the single arm: one for the upper arm and the other for the forearm. The proposed framework analyzed the contextual features of consecutive sensory arm movements, which provides an efficient way to improve the accuracy of arm movement estimation. In comparison with the ground truth, the proposed method estimated the arm movements using a dynamic time warping distance, which was the average of 73.90% less than that of a conventional Bayesian framework. The distinct feature of our proposed framework is that the number of sensors attached to end-users is reduced. Additionally, with the use of our framework, the arm orientations can be estimated with any soft sensor, and good accuracy of the estimations can be ensured. Another contribution is the suggestion of the combination of the Bi-LSTM and two-layer LSTM. View Full-Text
Keywords: human motion capture; movement estimation; HTC VIVE; Myo armband; soft sensor human motion capture; movement estimation; HTC VIVE; Myo armband; soft sensor
Show Figures

Figure 1

MDPI and ACS Style

Guo, H.; Sung, Y. Movement Estimation Using Soft Sensors Based on Bi-LSTM and Two-Layer LSTM for Human Motion Capture. Sensors 2020, 20, 1801.

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