  Pervasive computing allows the provision of services in many important areas,
including the relevant and dynamic field of health and well-being. In this
domain, Human Activity Recognition (HAR) has gained a lot of attention in
recent years. Current solutions rely on Machine Learning (ML) models and
achieve impressive results. However, the evolution of these models remains
difficult, as long as a complete retraining is not performed. To overcome this
problem, the concept of Continual Learning is very promising today and, more
particularly, the techniques based on regularization. These techniques are
particularly interesting for their simplicity and their low cost. Initial
studies have been conducted and have shown promising outcomes. However, they
remain very specific and difficult to compare. In this paper, we provide a
comprehensive comparison of three regularization-based methods that we adapted
to the HAR domain, highlighting their strengths and limitations. Our
experiments were conducted on the UCI HAR dataset and the results showed that
no single technique outperformed all others in all scenarios considered.
