Realization of wearable sensors-based human activity recognition with an augmented feature group

Yan Wang*, Shuang Cang, Hongnian Yu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Feature extraction is a critical stage in human activity recognition. The information carried in features directly affects the classification performance. This paper explores a new group of features for activity recognition, which have not been broadly applied in previous works in this field. The newly introduced features are related to the attitude of the on-body devices, being extracted from both time-domain and frequency-domain. Based on the collected data, we implemented certain standard data mining techniques, e.g., the Minimum-Redundancy-Maximum-Relevance (mRMR) algorithm for feature selection, and Support Vector Machine (SVM) for classification, to evaluate the performance of the hypothesis. The comparison studies suggest the augmented features perform better than the commonly used features, giving a higher recognition accuracy of 93.46%. Exploring new features without adding more sensors, while improving the accuracy significantly, enables an efficient extraction of features from limited availability of sensors.

Original languageEnglish
Title of host publication2016 22nd International Conference on Automation and Computing, ICAC 2016
Subtitle of host publicationTackling the New Challenges in Automation and Computing
PublisherIEEE
Pages473-478
Number of pages6
ISBN (Electronic)9781862181311
ISBN (Print)978-1-5090-2877-1
DOIs
Publication statusPublished - 24 Oct 2016
Event22nd International Conference on Automation and Computing, ICAC 2016 - Colchester, United Kingdom
Duration: 7 Sep 20168 Sep 2016

Conference

Conference22nd International Conference on Automation and Computing, ICAC 2016
CountryUnited Kingdom
CityColchester
Period7/09/168/09/16

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