Wearable-based Human Activity Recognition (HAR) is a key task in human-centric machine learning due to its fundamental understanding of human behaviours. Due to the dynamic nature of human behaviours, continual learning promises HAR systems that are tailored to users' needs. However, because of the difficulty in collecting labelled data with wearable sensors, existing approaches that focus on supervised continual learning have limited applicability, while unsupervised continual learning methods only handle representation learning while delaying classifier training to a later stage. This work explores the adoption and adaptation of CaSSLe, a continual self-supervised learning model, and Kaizen, a semi-supervised continual learning model that balances representation learning and down-stream classification, for the task of wearable-based HAR. These schemes re-purpose contrastive learning for knowledge retention and, Kaizen combines that with self-training in a unified scheme that can leverage unlabelled and labelled data for continual learning. In addition to comparing state-of-the-art self-supervised continual learning schemes, we further investigated the importance of different loss terms and explored the trade-off between knowledge retention and learning from new tasks. In particular, our extensive evaluation demonstrated that the use of a weighting factor that reflects the ratio between learned and new classes achieves the best overall trade-off in continual learning.

翻译：基于可穿戴设备的人类活动识别（HAR）是人机交互机器学习中的关键任务，因其对人类行为的基础性理解而至关重要。受人类行为动态特性的影响，持续学习为定制化满足用户需求的HAR系统提供了可能。然而，由于可穿戴传感器标注数据收集困难，现有监督式持续学习方法的应用受限，无监督式持续学习方法仅处理表示学习而将分类器训练推迟至后续阶段。本研究探索了持续自监督学习模型CaSSLe与半监督持续学习模型Kaizen在可穿戴HAR任务中的采纳与适配方案。前者通过对比学习实现知识保留，后者则结合自训练形成统一框架，能够同时利用无标签与有标签数据进行持续学习。除比较前沿自监督持续学习方案外，我们还深入研究了不同损失项的重要性，并探讨知识保留与新任务学习间的权衡。特别地，通过广泛评估表明，采用反映新旧类别比重的加权因子能实现持续学习中最优的整体权衡。