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.

翻译：普适计算能够在许多重要领域提供服务，包括健康与福祉这一重要且动态的领域。在此领域中，人类活动识别近年来受到了广泛关注。当前的解决方案依赖于机器学习模型，并取得了令人瞩目的成果。然而，只要不进行完整的重新训练，这些模型的演进仍然困难重重。为克服这一问题，持续学习的概念如今极具前景，尤其是基于正则化的技术。这些技术因其简单性和低成本而特别引人关注。初步研究已经开展并显示出令人鼓舞的结果，但它们仍非常具体且难以比较。在本文中，我们对三种基于正则化的方法进行了全面比较，这些方法已适配至人类活动识别领域，突出了各自的优势与局限性。我们的实验在UCI HAR数据集上进行，结果显示，在所有考虑的场景中，没有任何单一技术能全面超越其他技术。