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 va...

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Bibliographic Details
Published in:Journal of imaging 2022-06, Vol.8 (6), p.168
Main Authors: Niu, Hui, Ito, Takahiro, Desclaux, Damien, Ayusawa, Ko, Yoshiyasu, Yusuke, Sagawa, Ryusuke, Yoshida, Eiichi
Format: Article
Language:English
Subjects:
Artificial neural networks
Computer Science
Correspondence
Electromyography
Estimation
Human body
Human mechanics
Human motion
Methods
multilayer perceptron
Multilayer perceptrons
muscle activity
Muscle function
Muscles
non-rigid registration
point cloud
Reconstruction
Registration
Robotics
Sensors
Simulation
Skin
strain
Three dimensional models
Time series
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Summary: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.
ISSN:2313-433X
2313-433X
DOI:10.3390/jimaging8060168