Human behaviour modelling for welfare technology using hidden Markov models

被引：20

作者：

Sanchez, Veralia Gabriela ^{[1
]}

Lysaker, Ola Marius ^{[2
]}

Skeie, Nils-Olav ^{[1
]}

机构：

[1] Univ South Eastern Norway USN, Dept Elect Engn Informat Technol & Cybernet, Porsgrunn, Norway

[2] Univ South Eastern Norway USN, Dept Proc Energy & Environm Technol, Porsgrunn, Norway

来源：

PATTERN RECOGNITION LETTERS | 2020年 / 137卷 / 137期

关键词：

Ambient assisted living; HMM; Behaviour recognition; Assistive technology; Pattern recognition; Norway; Smart house; ACTIVITY RECOGNITION;

D O I：

10.1016/j.patrec.2019.09.022

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Human behaviour modelling for welfare technology is the task of recognizing a person's behaviour patterns in order to construct a safe environment for that person. It is useful in building environments for older adults or to help any person in his or her daily life. The aim of this study is to model the behaviour of a person living in a smart house environment in order to detect abnormal behaviour and assist the person if help is needed. Hidden Markov models, location of the person in the house, posture of the person, and time frame rules are implemented using a real-world, open-source dataset for training and testing. The proposed model presented in this study models the normal behaviour of a person and detects anomalies in the usual pattern. The model shows good results in the identification of abnormal behaviour when tested. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：71 / 79

页数：9

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