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Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine

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2 Author(s)
Youngwook Kim ; Dept. of Electr. & Comput. Eng., California State Univ. at Fresno, Fresno, CA ; Hao Ling

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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Geoscience and Remote Sensing, IEEE Transactions on  (Volume:47 ,  Issue: 5 )