Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud
Estimating Muscle Activity from the Deformation of a Sequential 3D Point Cloud
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DOI：
10.3390/jimaging8060168
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发表时间：
2022-06-13
影响因子：
3.2
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中科院分区：
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肌肉活动的估计非常重要，因为它可以作为评估一个人的动作和意图的线索。如果能够通过非接触式测量获得肌肉活动状态，例如通过视觉测量系统，肌肉活动将为各个研究领域提供数据支持和帮助。在本文中，我们提出了一种根据皮肤表面应变预测人体肌肉活动的方法。这就要求我们获得相对精度较高的3D重建模型。问题在于，由于视觉测量系统中生成的原始数据的噪声而导致的重建误差是不可避免的。特别是，时间序列上每一帧之间的独立噪声使得精确跟踪运动变得困难。为了获得有关人体皮肤表面的更精确的信息，我们提出了一种在非刚性配准过程中引入时间约束的方法。我们可以通过限制时间序列上的点云运动来实现更准确的形状和运动跟踪。使用表面应变作为输入，我们构建了一个多层感知器人工神经网络来推断肌肉活动。在本文中，我们研究简单的下肢运动来训练网络。结果，我们成功地实现了通过表面应变来估计肌肉活动。
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.