Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments

Joint Authors

Tang, Pei
Fang, Hongqing
Si, Hao

Source

Journal of Healthcare Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-11-29

Country of Publication

Egypt

No. of Pages

13

Main Topic

Public Health
Medicine

Abstract EN

In this paper, maximal relevance measure and minimal redundancy maximal relevance (mRMR) algorithm (under D-R and D/R criteria) have been applied to select features and to compose different features subsets based on observed motion sensor events for human activity recognition in smart home environments.

And then, the selected features subsets have been evaluated and the activity recognition accuracy rates have been compared with two probabilistic algorithms: naïve Bayes (NB) classifier and hidden Markov model (HMM).

The experimental results show that not all features are beneficial to human activity recognition and different features subsets yield different human activity recognition accuracy rates.

Furthermore, even the same features subset has different effect on human activity recognition accuracy rate for different activity classifiers.

It is significant for researchers performing human activity recognition to consider both relevance between features and activities and redundancy among features.

Generally, both maximal relevance measure and mRMR algorithm are feasible for feature selection and positive to activity recognition.

American Psychological Association (APA)

Fang, Hongqing& Tang, Pei& Si, Hao. 2020. Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186596

Modern Language Association (MLA)

Fang, Hongqing…[et al.]. Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments. Journal of Healthcare Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1186596

American Medical Association (AMA)

Fang, Hongqing& Tang, Pei& Si, Hao. Feature Selections Using Minimal Redundancy Maximal Relevance Algorithm for Human Activity Recognition in Smart Home Environments. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1186596

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1186596