Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud
Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/8/6/168 |
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| author | Hui Niu Takahiro Ito Damien Desclaux Ko Ayusawa Yusuke Yoshiyasu Ryusuke Sagawa Eiichi Yoshida |
| author_facet | Hui Niu Takahiro Ito Damien Desclaux Ko Ayusawa Yusuke Yoshiyasu Ryusuke Sagawa Eiichi Yoshida |
| author_sort | Hui Niu |
| collection | DOAJ |
| description | Estimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain. |
| first_indexed | 2024-03-09T23:24:29Z |
| format | Article |
| id | doaj.art-6e79077083404d89bb963187746feb5e |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-09T23:24:29Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-6e79077083404d89bb963187746feb5e2023-11-23T17:20:38ZengMDPI AGJournal of Imaging2313-433X2022-06-018616810.3390/jimaging8060168Estimating Muscle Activity from the Deformation of a Sequential 3D Point CloudHui Niu0Takahiro Ito1Damien Desclaux2Ko Ayusawa3Yusuke Yoshiyasu4Ryusuke Sagawa5Eiichi Yoshida6National Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, JapanNational Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, JapanCNRS-AIST JRL (Joint Robotics Laboratory), IRL, Tsukuba 305-8560, JapanNational Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, JapanNational Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, JapanNational Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, JapanNational Institute of Advanced Industrial Science and Technology, Tsukuba 305-8560, JapanEstimation of muscle activity is very important as it can be a cue to assess a person’s movements and intentions. If muscle activity states can be obtained through non-contact measurement, through visual measurement systems, for example, muscle activity will provide data support and help for various study fields. In the present paper, we propose a method to predict human muscle activity from skin surface strain. This requires us to obtain a 3D reconstruction model with a high relative accuracy. The problem is that reconstruction errors due to noise on raw data generated in a visual measurement system are inevitable. In particular, the independent noise between each frame on the time series makes it difficult to accurately track the motion. In order to obtain more precise information about the human skin surface, we propose a method that introduces a temporal constraint in the non-rigid registration process. We can achieve more accurate tracking of shape and motion by constraining the point cloud motion over the time series. Using surface strain as input, we build a multilayer perceptron artificial neural network for inferring muscle activity. In the present paper, we investigate simple lower limb movements to train the network. As a result, we successfully achieve the estimation of muscle activity via surface strain.https://www.mdpi.com/2313-433X/8/6/168muscle activitystrainnon-rigid registrationpoint cloudmultilayer perceptron |
| spellingShingle | Hui Niu Takahiro Ito Damien Desclaux Ko Ayusawa Yusuke Yoshiyasu Ryusuke Sagawa Eiichi Yoshida Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud Journal of Imaging muscle activity strain non-rigid registration point cloud multilayer perceptron |
| title | Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud |
| title_full | Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud |
| title_fullStr | Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud |
| title_full_unstemmed | Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud |
| title_short | Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud |
| title_sort | estimating muscle activity from the deformation of a sequential 3d point cloud |
| topic | muscle activity strain non-rigid registration point cloud multilayer perceptron |
| url | https://www.mdpi.com/2313-433X/8/6/168 |
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