Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit

This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOF...

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Main Author: Christos Mousas
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Sensors
Subjects:
HMM
IMU
Online Access:https://www.mdpi.com/1424-8220/17/11/2589
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spelling doaj-47c86924441e4843863cc59e093351fd2020-11-24T21:48:27ZengMDPI AGSensors1424-82202017-11-011711258910.3390/s17112589s17112589Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement UnitChristos Mousas0Department of Computer Science, Southern Illinois University, 1230 Lincoln Drive, Mail Code 4511, Carbondale, IL 62901, USAThis paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user’s full-body locomotion and the IMU’s data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently. By developing a hierarchical multivariate hidden Markov model with reactive interpolation functionality the system learns the structure of the motion sequences. Specifically, the phases of the locomotion sequence are assigned in the higher hierarchical level, and the frame structure of the motion sequences are assigned at the lower hierarchical level. During the runtime of the method, the forward algorithm is used for reconstructing the full-body motion of a virtual character. Firstly, the method predicts the phase where the input motion belongs (higher hierarchical level). Secondly, the method predicts the closest trajectories and their progression and interpolates the most probable of them to reconstruct the virtual character’s full-body motion (lower hierarchical level). Evaluating the proposed method shows that it works on reasonable framerates and minimizes the reconstruction errors compared with previous approaches.https://www.mdpi.com/1424-8220/17/11/2589character animationmotion datalocomotion reconstructionHMMIMU
collection DOAJ
language English
format Article
sources DOAJ
author Christos Mousas
spellingShingle Christos Mousas
Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
Sensors
character animation
motion data
locomotion reconstruction
HMM
IMU
author_facet Christos Mousas
author_sort Christos Mousas
title Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
title_short Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
title_full Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
title_fullStr Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
title_full_unstemmed Full-Body Locomotion Reconstruction of Virtual Characters Using a Single Inertial Measurement Unit
title_sort full-body locomotion reconstruction of virtual characters using a single inertial measurement unit
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-11-01
description This paper presents a method of reconstructing full-body locomotion sequences for virtual characters in real-time, using data from a single inertial measurement unit (IMU). This process can be characterized by its difficulty because of the need to reconstruct a high number of degrees of freedom (DOFs) from a very low number of DOFs. To solve such a complex problem, the presented method is divided into several steps. The user’s full-body locomotion and the IMU’s data are recorded simultaneously. Then, the data is preprocessed in such a way that would be handled more efficiently. By developing a hierarchical multivariate hidden Markov model with reactive interpolation functionality the system learns the structure of the motion sequences. Specifically, the phases of the locomotion sequence are assigned in the higher hierarchical level, and the frame structure of the motion sequences are assigned at the lower hierarchical level. During the runtime of the method, the forward algorithm is used for reconstructing the full-body motion of a virtual character. Firstly, the method predicts the phase where the input motion belongs (higher hierarchical level). Secondly, the method predicts the closest trajectories and their progression and interpolates the most probable of them to reconstruct the virtual character’s full-body motion (lower hierarchical level). Evaluating the proposed method shows that it works on reasonable framerates and minimizes the reconstruction errors compared with previous approaches.
topic character animation
motion data
locomotion reconstruction
HMM
IMU
url https://www.mdpi.com/1424-8220/17/11/2589
work_keys_str_mv AT christosmousas fullbodylocomotionreconstructionofvirtualcharactersusingasingleinertialmeasurementunit
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