Research
Dr. Kim's research on human motion classification by radar using machine learning is making impacts on academia, industry, and military including (i) the non-cooperative classification of humans for security and sensing, such as intruder detection, senior fall detection, people counting, and patient monitoring, and (ii) cooperative classification for enhanced human-machine interface, such as hand gesture recognition, and speech recognition.
His recent research can be categorized to five, 1) Human motion classification using machine learning, 2) Development of deep learning algorithms for radar target detection/tracking/classification, 3) Bio-sensing using radar, 4) RF system design, and 5) Antenna design.
1) Human Detection and Motion Classification Using Deep Learning
Y. Kim and H. Ling, "Human activity classification based on micro-Doppler signatures using a support vector machine ," IEEE Transactions on Geoscience and Remote Sensing , vol. 47, pp. 1328 –1337, May 2009.
This paper describes classifying human micro-Doppler signatures using SVM. Diverse human activities were measured using Doppler radar and significant features were extracted from micro-Doppler spectrogram. Then, SVM was designed to classify them. It became a milestone paper in this area.
Y. Kim and T. Moon, "Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks," IEEE Geoscience and Remote Sensing Letters, vol. 13, pp.2-8, Jan. 2016.
In this paper, he applied deep learning algorithms to human activity classification. He developed DCNN to process human micro-Doppler spectrograms to capture the significant features as well as to construct the decision boundary. This paper was featured as one of the most frequently accessed documents on the GRSS web for more than 3 years.
Y. Kim, S. Ha and J. Kwon, "Human detection using Doppler radar based on physical characteristics of targets," IEEE Geoscience and Remote Sensing Letters, vol.12, pp. 289 – 293, Feb. 2015.
In this paper, the physical characteristic of a human was evaluated. On the basis of micro-Doppler period and range displacement, the stride of humans and animals was estimated. Those parameters were compared and the classification was conducted by SVM.
J. Rios and Y. Kim, "Application of linear predictive coding for human activity classification based on micro-Doppler signatures," IEEE Geoscience and Remote Sensing Letters, vol. 11, pp. 1831-1834, Oct. 2014.
This paper describes the application of a linear predictive code (LCP) to the time-domain radar signal to classify human activities. LCP allows a time-domain processing that does not requires transformation into a frequency domain. This approach could reduce the time cost in realizing real-time processing.
Y. Kim and B. Toomajian, "Hand gesture recognition using micro-Doppler signatures with convolutional neural network," IEEE Access, Oct. 2016.
This paper proposed the classification of human hand gestures measured by radar. Measure data was converted to spectrogram and the deep convolutional neural network was applied to classify the spectrograms. This paper became one of mile stone for the hand gesture recognition area. This paper is very actively cited in a short period.
2) Development of Deep Learning Algorithms for Radar
J. Park, J. Rios, T. Moon and Y. Kim, "Micro-Doppler based classification of human aquatic activities via transfer learning of convolutional neural networks," Sensors, Nov. 2016.
This paper introduced the concept of transfer learning to improve radar data classification accuracy when the available data samples are limited. The pre-trained networks for optical images were successfully applied to radar images, leading to improve the classification accuracy.
I. Alnujaim, D. Oh and Y. Kim, "Generative adversarial networks for classification of micro-Doppler signatures of human activity," IEEE Geoscience and Remote Sensing Letters, June 2019.
While applying ML to radar data, it is found that there is an intrinsic issue with radar data, that is data deficiency issue. This paper addressed this problem through data augmentation via generative adversarial networks (GANs).
D. Lee, H. Park, T. Moon and Y. Kim, "Continual learning of micro-Doppler signature-based human activity classification," IEEE Geoscience and Remote Sensing Letters, Jan. 2021.
This paper investigated methods to update trained networks when new data or a new class is added. Through continual learning, the catastrophic forgetting problem can be overcome in micro-Doppler classification.
3) Bio-Sensing Using Radar
C. Lee, C. Yoon, H. Kong, H. Kim and Y. Kim, "Heart rate tracking using Doppler radar with the reassigned joint time frequency transform," IEEE Antennas and Wireless Propagation Letters, vol. 10, pp. 1096-1099, Sep. 2011.
In this paper, he applied re-assigned joint time frequency analysis to the receive radar signal to identify heartbeat. The displacement of chest was measured by radar and the cardiopulmonary motion was estimated.
Y. Kim, "Detection of eye blinking using Doppler sensor with principal component analysis," IEEE Antennas and Wireless Propagation Letters, pp.1-4, Issue 99, Feb. 2015.
This paper proposed the measurement of eye blinking using radar. Using principal component analysis, the conscious and non-conscious blinking could be classified.
R. Khanna, D. Oh and Y. Kim, "Through-wall remote human voice recognition using Doppler radar with transfer learning," IEEE Sensors Journal, vol. 19, pp. 4571 – 4576, Feb. 2019.
This paper presents an application of radar to detection the vibration of vocal cord and movement of mouth to recognize the voice. Transfer learning has been used to improve the classification accuracy. Through-wall measurement has been also conducted. This paper was featured in the IEEE Innovation spotlight on Nov 2019.
Y. Kim and Y. Li, "Human activity classification with transmission and reflection coefficients of on-body antennas through deep convolutional neural networks," IEEE Transactions on Antennas and Propagation, Mar. 2017.
The channel between on-body antennas was used to detect the body movements. When the creeping wave is perturbed by body motion, the channel pattern has been analyzed by DCNN.
D. Bresnahan, Y. Li and Y. Kim, "Monitoring human head and neck-based motions from transmission coefficient of on-body antennas," IEEE Antennas and Wireless Propagation Letters, July 2018.
This paper describes the use creeping wave to sense the head and neck movement. The variation characteristics of creeping wave was captured and analyzed by DCNN.
B. Xu, Y. Li and Y. Kim, "Classification of finger movements based on reflection coefficient variations of a body-worn electrically small antenna," IEEE Antennas and Wireless Component Letters, pp. 1812-1815, Mar. 2017.
This paper discusses the classification of finger movements based on the reflection coefficient variations of a wrist-worn electrically small antenna. Dynamic time-warping (DTW) algorithm is employed to classify the measured reflection coefficient variations of the FCH for different finger activities.
4) RF Systems
Y. Kim, Y. Kim and S. Lee, “Linearized mixer using predistortion technique,” IEEE Microwave and Wireless Components Letters, vol. 12, pp. 204 -205, June 2002.
This paper proposes a predistortion technique to reduce Intermodulation Distortion (IMD) that occurs during the transformation process of the mixer. The unique feature of the present work is that down converted IF signals are linearized with the predistorted RF signals in the mixer.
Y. Kim and H. Ling, “Direction of arrival estimation of humans with a small sensor array using an artificial neural network,” Progress In Electromagnetics Research, vol. 27, pp.127-149, 2011.
This paper proposes an array processing algorithm based on artificial neural networks (ANNs) to estimate the directions of arrival (DOAs) of moving humans using a small sensor array. Due to the small number of array elements, the proposed technique was found to be effective for tracking targets when the number is limited.
M. Nazaroff, G. Byun, H. Coo, J. Shin and Y. Kim, "2-D Direction of arrival estimation system using circular array with mutually coupled reference signal", IEEE Sensors Journal, vol. 18, pp. 9763-9769, Dec. 2018.
This paper discusses experimental results from an actual implementation of synchronizing a DOA circular array receiver system to mitigate DOA estimation error. To reduce phase errors at each channel, the reference signal is mutually coupled to antenna array elements.
5) Antenna Design
Y. Kim and H. Ling, “On the optimal sampling strategy for model-based parameter estimation using rational functions,” IEEE Transactions on Antennas and Propagation, vol. 54, pp. 762-765, Feb. 2006.
This paper investigates several sampling strategies to achieve the least number of samples required for a given RMS error when using the MBPE for a fast frequency sweep. They are: i) uniform-like sampling; ii) random sampling; and iii) sampling based on the Fisher information theory. These three methods are evaluated against a reference optimal sampling solution.
Y. Kim, S. Keely, J. Ghosh and H. Ling, “Application of artificial neural networks to broadband antenna design based on a parametric frequency model,” IEEE Transactions on Antennas and Propagation, vol. 55, pp. 669-674, Mar. 2007.
This paper proposed an artificial neural network (ANN) to predict the input impedance of a broadband antenna as a function of its geometric parameters. Compared with performance of direct approach, ANN approach led to a tenfold reduction in the number of required EM simulations and was still able to maintain an acceptable level of accuracy.