作者
Gorka Azkune, Aitor Almeida, Diego López-de-Ipiña, Liming Chen
发表日期
2015/4/15
期刊
Expert Systems with Applications
卷号
42
期号
6
页码范围
3115-3128
出版商
Pergamon
简介
Knowledge-driven activity recognition is an emerging and promising research area which has already shown very interesting features and advantages. However, there are also some drawbacks, such as the usage of generic and static activity models. This paper presents an approach to using data-driven techniques to evolve knowledge-driven activity models with a user’s behavioral data. The approach includes a novel clustering process where initial incomplete models developed through knowledge engineering are used to detect action clusters which represent activities and aggregate new actions. Based on those action clusters, a learning process is then designed to learn and model varying ways of performing activities in order to acquire complete and specialized activity models. The approach has been tested with real users’ inputs, noisy sensors and demanding activity sequences. Initial results have shown …
学术搜索中的文章
G Azkune, A Almeida, D López-de-Ipiña, L Chen - Expert Systems with Applications, 2015