  Application and use of deep learning algorithms for different healthcare
applications is gaining interest at a steady pace. However, use of such
algorithms can prove to be challenging as they require large amounts of
training data that capture different possible variations. This makes it
difficult to use them in a clinical setting since in most health applications
researchers often have to work with limited data. Less data can cause the deep
learning model to over-fit. In this paper, we ask how can we use data from a
different environment, different use-case, with widely differing data
distributions. We exemplify this use case by using single-sensor accelerometer
data from healthy subjects performing activities of daily living - ADLs (source
dataset), to extract features relevant to multi-sensor accelerometer gait data
(target dataset) for Parkinson's disease classification. We train the
pre-trained model using the source dataset and use it as a feature extractor.
We show that the features extracted for the target dataset can be used to train
an effective classification model. Our pre-trained source model consists of a
convolutional autoencoder, and the target classification model is a simple
multi-layer perceptron model. We explore two different pre-trained source
models, trained using different activity groups, and analyze the influence the
choice of pre-trained model has over the task of Parkinson's disease
classification.
