Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine
The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The seven activities include running, walking, walking while holding a stick, cra...
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| Published in | IEEE transactions on geoscience and remote sensing Vol. 47; no. 5; pp. 1328 - 1337 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
IEEE
01.05.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects | |
| Online Access | Get full text |
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| Abstract | The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The seven activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. Six features are extracted from the Doppler spectrogram. A support vector machine (SVM) is then trained using the measurement features to classify the activities. A multiclass classification is implemented using a decision-tree structure. Optimal parameters for the SVM are found through a fourfold cross-validation. The resulting classification accuracy is found to be more than 90%. The potentials of classifying human activities over extended time duration, through wall, and at oblique angles with respect to the radar are also investigated and discussed. |
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| AbstractList | The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The seven activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. Six features are extracted from the Doppler spectrogram. A support vector machine (SVM) is then trained using the measurement features to classify the activities. A multiclass classification is implemented using a decision-tree structure. Optimal parameters for the SVM are found through a fourfold cross-validation. The resulting classification accuracy is found to be more than 90%. The potentials of classifying human activities over extended time duration, through wall, and at oblique angles with respect to the radar are also investigated and discussed. |
| Author | Hao Ling Youngwook Kim |
| Author_xml | – sequence: 1 givenname: Youngwook Kim surname: Youngwook Kim fullname: Youngwook Kim, Youngwook Kim – sequence: 2 givenname: Hao Ling surname: Hao Ling fullname: Hao Ling, Hao Ling |
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| CODEN | IGRSD2 |
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| References | ref13 ref15 ref14 ref11 ref32 ref10 ref1 chang (ref30) 2001 ref17 ref16 ref19 ref18 kim (ref34) 2008 geisheimer (ref9) 2002; 4744 vapnik (ref23) 1998 chen (ref12) 2002 ref24 ref26 ref20 ref22 ref28 ref27 ref29 fumitake (ref31) 2002; 3 ref8 kim (ref33) 2008 ref7 nag (ref2) 2002; 4744 ref4 ref3 ref6 ref5 anderson (ref21) 2007; 6547 hastie (ref25) 2001 |
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| SubjectTerms | Classification Data mining Doppler effect Doppler radar Human Human activity classification Humans Information security Legged locomotion micro-doppler Radar detection Radar signal processing Radar tracking Signatures Spectrograms support vector machine Support vector machine classification Support vector machines through-wall Walking Walls |
| Title | Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine |
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