Infant movement classification through pressure distribution analysis -- added value for research and clinical implementation

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.