Aiming at objective early detection of neuromotor disorders such as cerebral palsy, we proposed an innovative non-intrusive approach using a pressure sensing device to classify infant general movements (GMs). Here, we tested the feasibility of using pressure data to differentiate typical GM patterns of the ''fidgety period'' (i.e., fidgety movements) vs. the ''pre-fidgety period'' (i.e., writhing movements). Participants (N = 45) were sampled from a typically-developing infant cohort. Multi-modal sensor data, including pressure data from a 32x32-grid pressure sensing mat with 1024 sensors, were prospectively recorded for each infant in seven succeeding laboratory sessions in biweekly intervals from 4-16 weeks of post-term age. For proof-of-concept, 1776 pressure data snippets, each 5s long, from the two targeted age periods were taken for movement classification. Each snippet was pre-annotated based on corresponding synchronised video data by human assessors as either fidgety present (FM+) or absent (FM-). Multiple neural network architectures were tested to distinguish the FM+ vs. FM- classes, including support vector machines (SVM), feed-forward networks (FFNs), convolutional neural networks (CNNs), and long short-term memory (LSTM) networks. The CNN achieved the highest average classification accuracy (81.4%) for classes FM+ vs. FM-. Comparing the pros and cons of other methods aiming at automated GMA to the pressure sensing approach, we concluded that the pressure sensing approach has great potential for efficient large-scale motion data acquisition and sharing. This will in return enable improvement of the approach that may prove scalable for daily clinical application for evaluating infant neuromotor functions.

翻译：旨在客观早期发现脑瘫等神经运动障碍，我们提出了一种创新的非侵入性方法，利用压力传感设备对婴儿全身运动（GMs）进行分类。本研究测试了使用压力数据区分典型运动模式"不安时期"（即不安运动）与"前不安时期"（即扭动运动）的可行性。参与者（N=45）来自典型发育婴儿队列。对每名婴儿在产后4-16周以双周间隔进行的七个连续实验室环节中，前瞻性记录了多模态传感器数据，包括来自32x32网格（含1024个传感器）压力感应垫的压力数据。为验证概念，选取了来自两个目标年龄期的1776个5秒长的压力数据片段进行运动分类。每个片段由人工评估员基于同步视频数据预先标注为存在不安运动（FM+）或不存在不安运动（FM-）。测试了多种神经网络架构以区分FM+和FM-类别，包括支持向量机（SVM）、前馈网络（FFN）、卷积神经网络（CNN）和长短期记忆网络（LSTM）。CNN在区分FM+与FM-类别时获得了最高平均分类准确率（81.4%）。通过比较其他自动化GMA方法与压力传感方法的优缺点，我们得出结论：压力传感方法在高效大规模运动数据采集与共享方面具有巨大潜力。这反过来将有助于改进该方法，使其可能扩展至日常临床应用中评估婴儿神经运动功能。