LaHAR: Latent Human Activity Recognition using LDA
This work addresses the problem of discovering underlying, human-understandable activity patterns in multi-sensor data for researchers and practitioners in Human Activity Recognition, offering an incremental approach.
This paper proposes a novel approach for Human Activity Recognition (HAR) that discovers latent activity patterns in sequential multi-sensor data using Latent Dirichlet Allocation (LDA). The method extracts "sensory words" from sequential data to make it suitable for LDA, and experiments show it accurately clusters HAR data sequences compared to labeled activities.
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.