  Wrist-bands such as smartwatches have become an unobtrusive interface for
collecting physiological and contextual data from users. Smartwatches are being
used for smart healthcare, telecare, and wellness monitoring. In this paper, we
used data collected from the AnEAR framework leveraging smartwatches to gather
and store physiological data from patients in naturalistic settings. This data
included temperature, galvanic skin response (GSR), acceleration, and heart
rate (HR). In particular, we focused on HR and acceleration, as these two
modalities are often correlated. Since the data was unlabeled we relied on
unsupervised learning for multi-modal signal analysis. We propose using k-means
clustering, GMM clustering, and Self-Organizing maps based on Neural Networks
for group the multi-modal data into homogeneous clusters. This strategy helped
in discovering latent structures in our data.
