  The health monitoring of chronic diseases is very important for people with
movement disorders because of their limited mobility and long duration of
chronic diseases. Machine learning-based processing of data collected from the
human with movement disorders using wearable sensors is an effective method
currently available for health monitoring. However, wearable sensor systems are
difficult to obtain high-quality and large amounts of data, which cannot meet
the requirement for diagnostic accuracy. Moreover, existing machine learning
methods do not handle this problem well. Feature learning is key to machine
learning. To solve this problem, a health monitoring of movement disorder
subject based on diamond stacked sparse autoencoder ensemble model (DsaeEM) is
proposed in this paper. This algorithm has two major components. First, feature
expansion is designed using feature-embedded stacked sparse autoencoder
(FSSAE). Second, a feature reduction mechanism is designed to remove the
redundancy among the expanded features. This mechanism includes L1 regularized
feature-reduction algorithm and the improved manifold dimensionality reduction
algorithm. This paper refers to the combined feature expansion and feature
reduction mechanism as the diamond-like feature learning mechanism. The method
is experimentally verified with several state of art algorithms and on two
datasets. The results show that the proposed algorithm has higher accuracy
apparently. In conclusion, this study developed an effective and feasible
feature-learning algorithm for the recognition of chronic diseases.
