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Open AccessArticle

A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data

1
Department of Computer Science and Engineering, Uttara University, Dhaka 1230, Bangladesh
2
Centre for Higher Studies and Research, Bangladesh University of Professionals, Mirpur Cantonment, Dhaka 1216, Bangladesh
3
College of Aeronautics and Engineering, Kent State University, Kent, OH 44240, USA
4
Department of Computer Science and Engineering, University of Asia Pacific, Dhaka 1205, Bangladesh
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(23), 6990; https://doi.org/10.3390/s20236990
Received: 31 October 2020 / Revised: 27 November 2020 / Accepted: 3 December 2020 / Published: 7 December 2020
(This article belongs to the Special Issue Selected Papers from IEEE ICKII 2020)
Human Activity Recognition (HAR) using embedded sensors in smartphones and smartwatch has gained popularity in extensive applications in health care monitoring of elderly people, security purpose, robotics, monitoring employees in the industry, and others. However, human behavior analysis using the accelerometer and gyroscope data are typically grounded on supervised classification techniques, where models are showing sub-optimal performance for qualitative and quantitative features. Considering this factor, this paper proposes an efficient and reduce dimension feature extraction model for human activity recognition. In this feature extraction technique, the Enveloped Power Spectrum (EPS) is used for extracting impulse components of the signal using frequency domain analysis which is more robust and noise insensitive. The Linear Discriminant Analysis (LDA) is used as dimensionality reduction procedure to extract the minimum number of discriminant features from envelop spectrum for human activity recognition (HAR). The extracted features are used for human activity recognition using Multi-class Support Vector Machine (MCSVM). The proposed model was evaluated by using two benchmark datasets, i.e., the UCI-HAR and DU-MD datasets. This model is compared with other state-of-the-art methods and the model is outperformed. View Full-Text
Keywords: human activity recognition (HAR); feature extraction; feature reduction; enveloped power spectrum (EPS); linear discriminant analysis (LDA); multi-class support vector machine (MCSVM) human activity recognition (HAR); feature extraction; feature reduction; enveloped power spectrum (EPS); linear discriminant analysis (LDA); multi-class support vector machine (MCSVM)
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MDPI and ACS Style

Ahmed Bhuiyan, R.; Ahmed, N.; Amiruzzaman, M.; Islam, M.R. A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data. Sensors 2020, 20, 6990.

AMA Style

Ahmed Bhuiyan R, Ahmed N, Amiruzzaman M, Islam MR. A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data. Sensors. 2020; 20(23):6990.

Chicago/Turabian Style

Ahmed Bhuiyan, Rasel; Ahmed, Nadeem; Amiruzzaman, Md; Islam, Md R. 2020. "A Robust Feature Extraction Model for Human Activity Characterization Using 3-Axis Accelerometer and Gyroscope Data" Sensors 20, no. 23: 6990.

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