NDLI: Human activity classification based on micro-Doppler signatures using an artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Youngwook Kim Hao Ling
Copyright Year	2008
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Univ. of Texas at Austin, Austin, TX (Youngwook Kim; Hao Ling)
Abstract	An ANN has been proposed to classify human activities from their micro-Doppler signatures. Data were collected using a Doppler radar for 12 human subjects performing seven activities to construct the training data set. Six features from Doppler signatures were captured in the spectrogram. Validation tests based on the features resulted in an 82.7% and 87.8% classification accuracy for two different validation scenarios. This result shows that it is quite feasible to recognize the different human activities using micro-Doppler information. Several issues still need to be further addressed. In this study, we used measurement data for the training process. The features can be affected by the characteristics of the particular radar used, such as I-Q imbalance, polarization and Rx-Tx locations. Therefore, the trained ANN could lead to high error when it is used to classify data measured from another sensor. Our study is only applicable when the human approaches the radar head-on. Data from other aspects should be included in the testing. Also, we used a 3-second time-window for the features extraction. If the human activity changes during the window duration, classification error may increase. A method to extract features within a shorter time duration needs further research.
Starting Page	1
Ending Page	4
File Size	457037
Page Count	4
File Format	PDF
ISBN	9781424420414
DOI	10.1109/APS.2008.4619933
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-07-05
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Doppler effect Feature extraction Radar Artificial neural networks Doppler radar Time frequency analysis Training
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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