Joint Learning of Temporal Models to Handle Imbalanced Data for Human Activity Recognition

Applied Sciences

RebeenAli Hamad¹  Bo Wei¹  Longzhi Yang¹  WaiLok Woo¹ 

[1] Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK;

关键词: Activity recognition;    Smart home;    Imbalanced class;    Joint learning;    Temporal models;   

DOI  :  10.3390/app10155293

来源: DOAJ

【 摘 要 】

Human activity recognition has become essential to a wide range of applications, such as smart home monitoring, health-care, surveillance. However, it is challenging to deliver a sufficiently robust human activity recognition system from raw sensor data with noise in a smart environment setting. Moreover, imbalanced human activity datasets with less frequent activities create extra challenges for accurate activity recognition. Deep learning algorithms have achieved promising results on balanced datasets, but their performance on imbalanced datasets without explicit algorithm design cannot be promised. Therefore, we aim to realise an activity recognition system using multi-modal sensors to address the issue of class imbalance in deep learning and improve recognition accuracy. This paper proposes a joint diverse temporal learning framework using Long Short Term Memory and one-dimensional Convolutional Neural Network models to improve human activity recognition, especially for less represented activities. We extensively evaluate the proposed method for Activities of Daily Living recognition using binary sensors dataset. A comparative study on five smart home datasets demonstrate that our proposed approach outperforms the existing individual temporal models and their hybridization. Furthermore, this is particularly the case for minority classes in addition to reasonable improvement on the majority classes of human activities.

【 授权许可】

Unknown