In recent years, numerous automated approaches complementing the human
Prechtl's general movements assessment (GMA) were developed. Most approaches
utilised RGB or RGB-D cameras to obtain motion data, while a few employed
accelerometers or inertial measurement units. In this paper, within a
prospective longitudinal infant cohort study applying a multimodal approach for
movement tracking and analyses, we examined for the first time the performance
of pressure sensors for classifying an infant general movements pattern, the
fidgety movements. We developed an algorithm to encode movements with pressure
data from a 32x32 grid mat with 1024 sensors. Multiple neural network
architectures were investigated to distinguish presence vs. absence of the
fidgety movements, including the feed-forward networks (FFNs) with manually
defined statistical features and the convolutional neural networks (CNNs) with
learned features. The CNN with multiple convolutional layers and learned
features outperformed the FFN with manually defined statistical features, with
classification accuracy of $81.4\%$ and $75.6\%$, respectively. We compared the
pros and cons of the pressure sensing approach to the video-based and inertial
motion senor-based approaches for analysing infant movements. The
non-intrusive, extremely easy-to-use pressure sensing approach has great
potential for efficient large-scaled movement data acquisition across cites and
for application in busy daily clinical routines for evaluating infant
neuromotor functions. The pressure sensors can be combined with other sensor
modalities to enhance infant movement analyses in research and practice, as
proposed in our multimodal sensor fusion model.

本文研究了使用压力传感器将数据编码为算法以用于感知婴儿的细微运动模式。结果证明了压力传感器方案的潜力和准确性，可以在儿科领域大幅提高运动数据的质量和效率。