  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.
