Incremental Learning of Human Activities in Smart Homes

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.

Keywords

activity recognition, incremental learning, novelty detection, prediction by partial matching, smart homes, Humans, Human Activities, Machine Learning

Citation

Chua S-L, Foo LK, Guesgen HW, Marsland S. (2022). Incremental Learning of Human Activities in Smart Homes.. Sensors (Basel). 22. 21. (pp. 8458-).

URI

https://mro.massey.ac.nz/handle/10179/69130

Collections

Journal Articles

Full item page