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Tong Liu
Department of Electronics Engineering, Huizhou University, Huizhou 516007, China

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Journal article
Published: 08 February 2018 in Journal of Sensor and Actuator Networks
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The sparse distribution of targets in monitored areas is an important prior for device-free localization (DFL) with radio tomography networks. In this article, our goal is to develop an enhanced sparse representation-based DFL method that takes the full potential of sparsity for location reconstruction. An expanded sensing matrix spanning the concatenation of a sampling matrix and a unit error-correcting base is proposed for modelling the measurement process. The sampling matrix can either be composed of the ellipse model from calibrated networks or the received signal strength (RSS) fingerprint-based model induced by training samples with one person at predefined locations. Thus, the sparsity of targets is enhanced under the expanded sensing matrix and the ℓ1-minimization-based approximations are derived for the recovery of locations. Experimental studies in an open outdoor scenario, in a line-of-sight (LOS) indoor scenario, and in a non-line-of-sight (NLOS) indoor scenario, are conducted to verify the efficacy of the proposed method.

ACS Style

Tong Liu; Xiaomu Luo; Zhuoqian Liang. Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks. Journal of Sensor and Actuator Networks 2018, 7, 7 .

AMA Style

Tong Liu, Xiaomu Luo, Zhuoqian Liang. Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks. Journal of Sensor and Actuator Networks. 2018; 7 (1):7.

Chicago/Turabian Style

Tong Liu; Xiaomu Luo; Zhuoqian Liang. 2018. "Enhanced Sparse Representation-Based Device-Free Localization with Radio Tomography Networks." Journal of Sensor and Actuator Networks 7, no. 1: 7.

Journal article
Published: 03 June 2016 in Sensors
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Healthy aging is one of the most important social issues. In this paper, we propose a method for abnormal activity detection without any manual labeling of the training samples. By leveraging the Field of View (FOV) modulation, the spatio-temporal characteristic of human activity is encoded into low-dimension data stream generated by the ceiling-mounted Pyroelectric Infrared (PIR) sensors. The similarity between normal training samples are measured based on Kullback-Leibler (KL) divergence of each pair of them. The natural clustering of normal activities is discovered through a self-tuning spectral clustering algorithm with unsupervised model selection on the eigenvectors of a modified similarity matrix. Hidden Markov Models (HMMs) are employed to model each cluster of normal activities and form feature vectors. One-Class Support Vector Machines (OSVMs) are used to profile the normal activities and detect abnormal activities. To validate the efficacy of our method, we conducted experiments in real indoor environments. The encouraging results show that our method is able to detect abnormal activities given only the normal training samples, which aims to avoid the laborious and inconsistent data labeling process.

ACS Style

Xiaomu Luo; Huoyuan Tan; Qiuju Guan; Tong Liu; Hankz Hankui Zhuo; Baihua Shen. Abnormal Activity Detection Using Pyroelectric Infrared Sensors. Sensors 2016, 16, 822 .

AMA Style

Xiaomu Luo, Huoyuan Tan, Qiuju Guan, Tong Liu, Hankz Hankui Zhuo, Baihua Shen. Abnormal Activity Detection Using Pyroelectric Infrared Sensors. Sensors. 2016; 16 (6):822.

Chicago/Turabian Style

Xiaomu Luo; Huoyuan Tan; Qiuju Guan; Tong Liu; Hankz Hankui Zhuo; Baihua Shen. 2016. "Abnormal Activity Detection Using Pyroelectric Infrared Sensors." Sensors 16, no. 6: 822.

Journal article
Published: 03 September 2014 in Journal of Ambient Intelligence and Humanized Computing
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Fall is a common daily activity and a leading cause of death among the older adults. It reveals growing demands to use some non-invasive methods to detect the pose of older people and give a timely and efficient alert, especially in some place with high fall-risk. This article presents a radio tomographic imaging (RTI) based approach for fall detection. A wireless network organized by a group of radio-frequency sensors is used for human pose sensing in the vertical direction. The human body would cause the statistical shadowing losses on the passing links between pairs of nodes in the network. Then an attenuation image of body pose can be obtained by using the received signal strength measurements. The non-negative total variation minimization is used to reconstruct the gray image of body. The fall detection is cast as an image recognition problem. This is a new approach based on the use of RTI to enable the building of a fall detection system. Experimental studies are conducted to validate the proposed method.

ACS Style

Tong Liu; Jun Liu; Xiao-Mu Luo. Radio tomographic imaging based body pose sensing for fall detection. Journal of Ambient Intelligence and Humanized Computing 2014, 5, 897 -907.

AMA Style

Tong Liu, Jun Liu, Xiao-Mu Luo. Radio tomographic imaging based body pose sensing for fall detection. Journal of Ambient Intelligence and Humanized Computing. 2014; 5 (6):897-907.

Chicago/Turabian Style

Tong Liu; Jun Liu; Xiao-Mu Luo. 2014. "Radio tomographic imaging based body pose sensing for fall detection." Journal of Ambient Intelligence and Humanized Computing 5, no. 6: 897-907.

Journal article
Published: 19 February 2014 in EURASIP Journal on Advances in Signal Processing
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ACS Style

Tong Liu; Jun Liu. Design and implementation of a compressive infrared sampling for motion acquisition. EURASIP Journal on Advances in Signal Processing 2014, 2014, 20 .

AMA Style

Tong Liu, Jun Liu. Design and implementation of a compressive infrared sampling for motion acquisition. EURASIP Journal on Advances in Signal Processing. 2014; 2014 (1):20.

Chicago/Turabian Style

Tong Liu; Jun Liu. 2014. "Design and implementation of a compressive infrared sampling for motion acquisition." EURASIP Journal on Advances in Signal Processing 2014, no. 1: 20.

Research article
Published: 01 January 2014 in International Journal of Advanced Robotic Systems
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This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR) sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisition. Then we express the fall detection problem in silhouette image-based pose recognition. For the pose recognition, we use a robust sparse representation-based method for fall detection. The normal and fall poses are sparsely represented in the basis space spanned by the combinations of a pose training template and an error template. The ℓ1 norm minimizations with linear programming (LP) and orthogonal matching pursuit (OMP) are used for finding the sparsest solution, and the entity with the largest amplitude encodes the class of the testing sample. The application of the proposed sensing paradigm to fall detection is addressed in the context of three scenarios, including: ideal non-obstruction, simulated random pixel obstruction and simulated random block obstruction. Experimental studies are conducted to validate the effectiveness of the proposed method for nursing and homeland healthcare.

ACS Style

Tong Liu; Jun Liu. Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection. International Journal of Advanced Robotic Systems 2014, 11, 42 .

AMA Style

Tong Liu, Jun Liu. Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection. International Journal of Advanced Robotic Systems. 2014; 11 (3):42.

Chicago/Turabian Style

Tong Liu; Jun Liu. 2014. "Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection." International Journal of Advanced Robotic Systems 11, no. 3: 42.

Journal article
Published: 25 September 2012 in EURASIP Journal on Advances in Signal Processing
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This article presents a feature-specific infrared sensing paradigm and its application in building a lightweight human biometric detection system for wireless sensor network (WSN). The proposed paradigm exploits the novel use of pyroelectric infrared (PIR) sensor arrays in pursuit of unifying motion detection and biometric sensing with a ceiling view based fashion. Three PIR sensors with compound-eye structured field of view (FOV) are used to organize the biometric feature acquisition. The application of the proposed sensing paradigm to lightweight biometric sensing is addressed in the context of the path-constrained walker recognition with the vector quantization (VQ) method. Experimental studies are conducted to validate the proposed method.

ACS Style

Tong Liu; Jun Liu. Feature-specific biometric sensing using ceiling view based pyroelectric infrared sensors. EURASIP Journal on Advances in Signal Processing 2012, 2012, 206 .

AMA Style

Tong Liu, Jun Liu. Feature-specific biometric sensing using ceiling view based pyroelectric infrared sensors. EURASIP Journal on Advances in Signal Processing. 2012; 2012 (1):206.

Chicago/Turabian Style

Tong Liu; Jun Liu. 2012. "Feature-specific biometric sensing using ceiling view based pyroelectric infrared sensors." EURASIP Journal on Advances in Signal Processing 2012, no. 1: 206.

Journal article
Published: 27 March 2012 in EURASIP Journal on Wireless Communications and Networking
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Pervasive healthcare is one of the most important applications of the Internet of Things (IoT). As part of the IoT, the wireless sensor networks (WSNs) are responsible for sensing the abnormal behavior of the elderly or patients. In this article, we design and implement a fall detection system called SensFall. With the resource restricted sensor nodes, it is vital to find an efficient feature to describe the scene. Based on the optical flow analysis, it can be observed that the thermal energy variation of each sub-region of the monitored region is a salient spatio-temporal feature that characterizes the fall. The main contribution of this study is to develop a feature-specific sensing system to capture this feature so as to detect the occurrence of a fall. In our system, the three-dimensional (3D) object space is segmented into some distinct discrete sampling cells, and pyroelectric infrared (PIR) sensors are employed to detect the variance of the thermal flux within these cells. The hierarchical classifier (two-layer HMMs) is proposed to model the time-varying PIR signal and classify different human activities. We use self-developed PIR sensor nodes mounted on the ceiling and construct a WSN based on ZigBee (802.15.4) protocol. We conduct experiments in a real office environment. The volunteers simulate several kinds of activities including falling, sitting down, standing up from a chair, walking, and jogging. Encouraging experimental results confirm the efficacy of our system.

ACS Style

Xiaomu Luo; Tong Liu; Jun Liu; Xuemei Guo; Guoli Wang. Design and implementation of a distributed fall detection system based on wireless sensor networks. EURASIP Journal on Wireless Communications and Networking 2012, 2012, 118 .

AMA Style

Xiaomu Luo, Tong Liu, Jun Liu, Xuemei Guo, Guoli Wang. Design and implementation of a distributed fall detection system based on wireless sensor networks. EURASIP Journal on Wireless Communications and Networking. 2012; 2012 (1):118.

Chicago/Turabian Style

Xiaomu Luo; Tong Liu; Jun Liu; Xuemei Guo; Guoli Wang. 2012. "Design and implementation of a distributed fall detection system based on wireless sensor networks." EURASIP Journal on Wireless Communications and Networking 2012, no. 1: 118.

Journal article
Published: 15 October 2011 in Multidimensional Systems and Signal Processing
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This paper presents a new distributed direction-sensitive infrared sensing approach for fall detection in elderly healthcare applications. Pyroelectric infrared (PIR) sensors are employed in sensing human activities. For capturing the characteristics of human normal and abnormal activities, three modules of a direction-sensitive PIR sensor are organized using a distributed sensing structure. The advantage of using the distributed sensing paradigm is that the synergistic motion patterns of head, upper-limb and lower-limb can be efficiently encoded and thus the more discriminative features can be captured. This is the new consideration of using PIR sensors in building a full detection system. In addition, a two-layer hidden Markov model is developed for recognizing a fall event based on the multidimensional signals of the distributed infrared sensing system. Experimental studies are conducted to validate the proposed method.

ACS Style

Tong Liu; Xuemei Guo; Guoli Wang. Elderly-falling detection using distributed direction-sensitive pyroelectric infrared sensor arrays. Multidimensional Systems and Signal Processing 2011, 23, 451 -467.

AMA Style

Tong Liu, Xuemei Guo, Guoli Wang. Elderly-falling detection using distributed direction-sensitive pyroelectric infrared sensor arrays. Multidimensional Systems and Signal Processing. 2011; 23 (4):451-467.

Chicago/Turabian Style

Tong Liu; Xuemei Guo; Guoli Wang. 2011. "Elderly-falling detection using distributed direction-sensitive pyroelectric infrared sensor arrays." Multidimensional Systems and Signal Processing 23, no. 4: 451-467.