Work

Incremental Learning of Human Activities in Smart Homes

Year: 2022

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s22218458

Authors: Sook-Ling Chua, Lee Kien Foo, Hans W. Guesgen, Stephen Marsland

Institutions: Multimedia University, Massey University, Victoria University of Wellington

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,513

Funder: Ministry of Education, India