  Processing sequential multi-sensor data becomes important in many tasks due
to the dramatic increase in the availability of sensors that can acquire
sequential data over time. Human Activity Recognition (HAR) is one of the
fields which are actively benefiting from this availability. Unlike most of the
approaches addressing HAR by considering predefined activity classes, this
paper proposes a novel approach to discover the latent HAR patterns in
sequential data. To this end, we employed Latent Dirichlet Allocation (LDA),
which is initially a topic modelling approach used in text analysis. To make
the data suitable for LDA, we extract the so-called "sensory words" from the
sequential data. We carried out experiments on a challenging HAR dataset,
demonstrating that LDA is capable of uncovering underlying structures in
sequential data, which provide a human-understandable representation of the
data. The extrinsic evaluations reveal that LDA is capable of accurately
clustering HAR data sequences compared to the labelled activities.
