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

By Youngwook Kim, Hao Ling. IEEE Transactions on Geoscience and Remote Sensing, 2009

852 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Biomedical Engineering

View Paper: https://www.semanticscholar.org/paper/e700d9222697201207564a0e31f446dab8c3a98d

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Citations in the corpus

Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities (2018, methodology): “Such techniques have been shown in a variety of works [15], [16] to yield high classification performance.”
Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks (2016, methodology): “We investigated the performance of the DCNN in the classification of human activities employing the data set used in [5].”
Gait recognition using wifi signals (2016, methodology): “The micro-Doppler signature, which describes the velocity of different body parts, can be used for human activity recognition [16,21,22].”
WiGest: A ubiquitous WiFi-based gesture recognition system (2014, background): “Finally, WiSee [10] is a fine-grained gesture recognition system that builds on DopLink [37] and [38] by exploiting the doppler shift in narrow bands extracted form wide-band OFDM transmissions to recognize nine different human gesturers.”
Whole-home gesture recognition using wireless signals (2013, background): “Specifically, since wireless signals are electromagnetic waves that propagate at the speed of light (c m/sec), a human moving at a speed of v m/sec, results in a maximum Doppler shift of 2f c v , where f is the frequency of the wireless transmission [3].”
A Review on Recent Progress of Portable Short-Range Noncontact Microwave Radar Systems (2017, background): “Analysis of the radardetected signal in both time and frequency domains can reveal features of a movement for classification purposes [20], [21], [87], [88].”
See through walls with WiFi! (2013, background): “Hence, most of these systems are demonstrated either in simulation [29], or in free space with no obstruction [22, 24].”
Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network (2016, methodology): “There are five main approaches to feature extraction: i) handcraft features [10], ii) empirical mode decomposition [11], iii) linear predictive code [15], iv) principal component analysis [16], and v) DCNNs [17], [18].”
Human Action Recognition From Various Data Modalities: A Review (2020, methodology): “Some hand-crafted feature-based HAR methods using radar data have been designed in the works of [329], [330], [331].”