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

• Published in: IEEE transactions on geoscience and remote sensing Vol. 47; no. 5; pp. 1328 - 1337
• Main Authors: Youngwook Kim, Youngwook Kim, Hao Ling, Hao Ling
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2009.2012849

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.