Feature-based human motion parameter estimation with radar

Dorp, P. van; Groen, Frans C. A.

doi:10.1049/iet-rsn:20070086

Cited by 166 publications

(71 citation statements)

References 5 publications

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“…The envelopes of the spectrograms are extracted using a percentile technique proposed by Van Dorp and Groen [34] in 2008. First, the element of the envelope in each column of the spectrogram data is computed.…”

Section: A Feature Extractionmentioning

confidence: 99%

Knowledge Exploitation for Human Micro-Doppler Classification

Karabacak

Gürbüz

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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Abstract-Micro-Doppler radar signatures have a great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data needs to be complemented with accurate simulated radar data to successfully be able to design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment any desired scenario may be generated. Motion capture has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of motion capture data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, especially in the case when the total amount of data is small.

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Section: A Feature Extractionmentioning

confidence: 99%

Knowledge Exploitation for Human Micro-Doppler Classification

Karabacak

Gürbüz

et al. 2015

IEEE Geosci. Remote Sensing Lett.

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“…As reference for Doppler estimation, we use a method for characterizing the frequency response to quantify velocity from the Doppler shifts by considering the 5th and 95th Percentiles as described in [15]. The percentiles are determined by first evaluating…”

Section: Reference Methodsmentioning

confidence: 99%

“…The generated profiles will be used to compare the proposed Doppler estimation method and the reference method [15]. Arm movements are modeled by a feedback control loop consisting of a plant and a Proportional-IntegralDerivative (PID) controller.…”

Section: A Movement Modelmentioning

confidence: 99%

“…However, privacy issues relating to the use of vision sensors remain a concern [14]. A radar sensor was used to estimate human motion features using a Boulic model in [15]. Ultrasound as a sensor modality for activity monitoring and classification has been shown to be effective, while being unobtrusive and addressing privacy concerns [16]- [18].…”

Section: Introductionmentioning

confidence: 99%

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Ultrasonic Array Doppler Sensing for Human Movement Classification

et al. 2014

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Abstract-Classification of human movements is an important problem in healthcare and well-being applications. An ultrasonic array Doppler sensing method is proposed for classifying movements from a given set. The proposed method uses velocity and angular information derived from Doppler frequencies and direction-of-arrival (DoA) by processing the signals at the receiver sensor array. Doppler frequency estimation is done by obtaining an initial estimate based on the Fourier transform in conjunction with a predictive tracker. A Root-MUSIC algorithm is used at the estimated Doppler frequencies to obtain DoA corresponding to the dominating moving object. Using speed, direction, and angle as features, a Bayesian classifier is employed to distinguish between a set of movements. The performance of the proposed method is evaluated using an analytical model of arm movements and also using experimental data sets. The proposed ultrasonic Doppler array sensor and processing methods provide a new, compact solution to human arm movement classification.

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“…Model-based approaches with the Boulic model for estimating human motion parameters are described in [4], [5] and [6].…”

Section: Introductionmentioning

confidence: 99%