  Autism Spectrum Disorders are associated with atypical movements, of which
stereotypical motor movements (SMMs) interfere with learning and social
interaction. The automatic SMM detection using inertial measurement units (IMU)
remains complex due to the strong intra and inter-subject variability,
especially when handcrafted features are extracted from the signal. We propose
a new application of the deep learning to facilitate automatic SMM detection
using multi-axis IMUs. We use a convolutional neural network (CNN) to learn a
discriminative feature space from raw data. We show how the CNN can be used for
parameter transfer learning to enhance the detection rate on longitudinal data.
We also combine the long short-term memory (LSTM) with CNN to model the
temporal patterns in a sequence of multi-axis signals. Further, we employ
ensemble learning to combine multiple LSTM learners into a more robust SMM
detector. Our results show that: 1) feature learning outperforms handcrafted
features; 2) parameter transfer learning is beneficial in longitudinal
settings; 3) using LSTM to learn the temporal dynamic of signals enhances the
detection rate especially for skewed training data; 4) an ensemble of LSTMs
provides more accurate and stable detectors. These findings provide a
significant step toward accurate SMM detection in real-time scenarios.
