Performance Analysis of Classification Algorithms for Activity Recognition Using Micro-Doppler Feature

Lin, Yier; Kernec, Julien Le

doi:10.1109/cis.2017.00111

Cited by 27 publications

(13 citation statements)

References 7 publications

(8 reference statements)

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“…as [19][20][21][22][23] make use of simulated radar returns via MoCapbased data. Even though the accuracy of the MoCap data cannot be guaranteed as some noise exists in positioning the joints during motion as the markers are considered to be affixed on a rigid structure where in fact they are fitted on skin or clothing that stretch during movements.…”

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

“…The MoCap data in this paper comes from Carnegie Mellon University (CMU) Motion Capture (MoCap) Database [25,26] and the simulation process is illustrated in Fig. 2 that we developed in [19,20]. This method is used to assess the performance for different configurations of our method and other classic techniques and their robustness against noise.…”

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

“…where {f d } denotes the Doppler shift, and f v denotes the motion frequency of the body part, as well as ψ denotes the initial phase. Considering (18) and (19), the radar returns from the whole body can be modeled as the sum of the contributions from the different body parts (legs, arms, torso, etc.) as shown in (20).…”

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

“…The simulation produces range data with the appropriate phase delay for Doppler processing [1]. The details of the algorithm can be found both in [19,20,24].…”

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

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Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

et al. 2018

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The accurate classification of activity patterns based on radar signatures is still an open problem and is key to detect anomalous behavior for security and health applications. This paper presents a novel iterative convolutional neural networks strategy with an autocorrelation pre-processing instead of the traditional micro-Doppler image pre-processing to classify activities or subjects accurately. The proposed strategy uses an iterative deep learning framework for the automatic definition and extraction of features. This is followed by a traditional supervised learning classifier to label the different activities. Using three human subjects and their real motion captured data, twelve thousand radar signatures were simulated by varying additive white Gaussian noise. Additionally, 6720 experimental radar signatures were captured with a frequency-modulated continuous radar at 5.8GHz with 400M Hz of instantaneous bandwidth from seven activities using one subject and 4800 signatures from five subjects while walking. The simulated and experimental data were both used to validate our proposed method. With SNR varying from −20 to 20dB with 88.74% average accuracy at −10dB and 100% peak accuracy at 15dB. The proposed Iterative Convolutional Neural Networks followed with Random Forests (ICNNRF) does not only outperform the feature-based methods using micro-Doppler images but also the classification methods using other types of supervised classifiers after our proposed iterative convolutional neural network.

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Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

“…The simulation produces range data with the appropriate phase delay for Doppler processing [1]. The details of the algorithm can be found both in [19,20,24].…”

Section: Simulation Setup and Numerical Resultsmentioning

confidence: 99%

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Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

et al. 2018

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“…Future work will look at carrying out live experiments for validation with ultra‐wideband software defined radar [7, 9, 13, 31, 32].…”

Section: Discussionmentioning

confidence: 99%

From Kinect skeleton data to hand gesture recognition with radar

Shrestha

Kernec

et al. 2019

J. eng.

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Bespoke Simulator for Human Activity Classification with Bistatic Radar

Yang

Abbasi

Fioranelli

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Radar is now widely used in human activity classification because of its contactless sensing capabilities, robustness to light conditions and privacy preservation compared to plain optical images. It has great value in elderly care, monitoring accidental falls and abnormal behaviours. Monostatic radar suffers from degradation in performance with varying aspect angles with respect to the target. Bistatic radar may offer a solution to this problem but finding the right geometry can be quite resource-intensive. We propose a bespoke simulation framework to test the radar geometry for human activity recognition. First, the analysis focuses on the monostatic radar model based on the Doppler effect in radar. We analyse the spectrogram of different motions by Short-time Fourier analysis (STFT), and then the classification data set was built for feature extraction and classification. The results show that the monostatic radar system has the highest accuracy, up to 98.17%. So, a bistatic radar model with separate transmitter and receiver was established in the experiment, and results show that bistatic radar with specific geometry configuration (CB2.5) not only has higher classification accuracy than monostatic radar in each aspect angle but also can recognise the object in a wider angle range. After training and fusing the data of all angles, it is found that the accuracy, sensitivity, and specificities of CB2.5 have 2.2%, 7.7% and 1.5% improvement compared with monostatic radar.

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Performance Analysis of Classification Algorithms for Activity Recognition Using Micro-Doppler Feature

Cited by 27 publications

References 7 publications

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

Human Activity Classification With Radar: Optimization and Noise Robustness With Iterative Convolutional Neural Networks Followed With Random Forests

From Kinect skeleton data to hand gesture recognition with radar

Bespoke Simulator for Human Activity Classification with Bistatic Radar

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