Faculty of Computing and Informatics, Multimedia University, Persiaran Multimedia, Cyberjaya 63100, Malaysia.
School of Mathematical and Computational Sciences, Massey University, Palmerston North 4442, New Zealand.
Sensors (Basel). 2022 Nov 3;22(21):8458. doi: 10.3390/s22218458.
Sensor-based human activity recognition has been extensively studied. Systems learn from a set of training samples to classify actions into a pre-defined set of ground truth activities. However, human behaviours vary over time, and so a recognition system should ideally be able to continuously learn and adapt, while retaining the knowledge of previously learned activities, and without failing to highlight novel, and therefore potentially risky, behaviours. In this paper, we propose a method based on compression that can incrementally learn new behaviours, while retaining prior knowledge. Evaluation was conducted on three publicly available smart home datasets.
基于传感器的人体活动识别已经得到了广泛的研究。系统从一组训练样本中学习，将动作分类到预定义的一组真实活动中。然而，人类的行为随着时间的推移而变化，因此识别系统理想情况下应该能够持续学习和适应，同时保留先前学习的活动的知识，并且不会忽略新的、可能有风险的行为。在本文中，我们提出了一种基于压缩的方法，可以在保留先前知识的同时逐步学习新的行为。在三个公开的智能家居数据集上进行了评估。
