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：为实现脑瘫等神经运动障碍的早期客观检测，我们提出了一种创新性非侵入式方法，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"不安运动期"（即不安运动）与"前不安运动期"（即扭动运动）典型运动模式的可行性。受试者（N=45）来自典型发育婴儿队列。对每名婴儿在出生后4-16周的七次实验室随访中（每两周一次），前瞻性记录了包括32×32网格压力传感垫（含1024个传感器）压力数据在内的多模态传感器数据。为验证概念，从两个目标年龄阶段选取了1776段时长各5秒的压力数据片段进行运动分类。每段数据基于同步录像视频数据由人工评估员预先标注为"存在不安运动（FM+）"或"无不安运动（FM-）"。我们测试了多种神经网络架构以区分FM+与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）。其中，CNN对FM+与FM-分类的平均准确率最高（81.4%）。通过比较其他自动GMA方法与压力传感方法的优劣，我们得出结论：压力传感方法在大规模运动数据采集与共享方面具有巨大潜力。这将反过来推动该方法性能的提升，使其有望扩展应用于日常临床评估婴儿神经运动功能。