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Xiaomu Luo
School of Information Technology, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China

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Journal article
Published: 29 July 2017 in Sensors
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Indoor human tracking and activity recognition are fundamental yet coherent problems for ambient assistive living. In this paper, we propose a method to address these two critical issues simultaneously. We construct a wireless sensor network (WSN), and the sensor nodes within WSN consist of pyroelectric infrared (PIR) sensor arrays. To capture the tempo-spatial information of the human target, the field of view (FOV) of each PIR sensor is modulated by masks. A modified partial filter algorithm is utilized to decode the location of the human target. To exploit the synergy between the location and activity, we design a two-layer random forest (RF) classifier. The initial activity recognition result of the first layer is refined by the second layer RF by incorporating various effective features. We conducted experiments in a mock apartment. The mean localization error of our system is about 0.85 m. For five kinds of daily activities, the mean accuracy for 10-fold cross-validation is above 92%. The encouraging results indicate the effectiveness of our system.

ACS Style

Xiaomu Luo; Qiuju Guan; Huoyuan Tan; Liwen Gao; Zhengfei Wang; Xiaoyan Luo. Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors. Sensors 2017, 17, 1738 .

AMA Style

Xiaomu Luo, Qiuju Guan, Huoyuan Tan, Liwen Gao, Zhengfei Wang, Xiaoyan Luo. Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors. Sensors. 2017; 17 (8):1738.

Chicago/Turabian Style

Xiaomu Luo; Qiuju Guan; Huoyuan Tan; Liwen Gao; Zhengfei Wang; Xiaoyan Luo. 2017. "Simultaneous Indoor Tracking and Activity Recognition Using Pyroelectric Infrared Sensors." Sensors 17, no. 8: 1738.

Conference paper
Published: 01 May 2017 in 2017 29th Chinese Control And Decision Conference (CCDC)
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Radio tomographic networks based imaging is bringing significant impact in activity sensing. In this article, we proposed an abnormal activity detection method without any computed recovery imaging. By organizing a vertically arranged profile-aware network, the critical state feature of abnormal activity is encoded into data stream of received signal strengths (RSSs). Then, the new coming sensor data is compared with the instantaneous state feature already recorded, and abnormal detection is performed according to similarity. To validate the efficacy of our method, we defined walking as normal activity and fall as abnormal activity in indoor environments. Experiments give the encouraging results.

ACS Style

Tong Liu; Xiao-Hui Wei; Zhi-Ming Chen; Xiao-Mu Luo; Jun Liu. Abnormal activity detection based on received signal strengths of radio tomographic networks. 2017 29th Chinese Control And Decision Conference (CCDC) 2017, 7310 -7314.

AMA Style

Tong Liu, Xiao-Hui Wei, Zhi-Ming Chen, Xiao-Mu Luo, Jun Liu. Abnormal activity detection based on received signal strengths of radio tomographic networks. 2017 29th Chinese Control And Decision Conference (CCDC). 2017; ():7310-7314.

Chicago/Turabian Style

Tong Liu; Xiao-Hui Wei; Zhi-Ming Chen; Xiao-Mu Luo; Jun Liu. 2017. "Abnormal activity detection based on received signal strengths of radio tomographic networks." 2017 29th Chinese Control And Decision Conference (CCDC) , no. : 7310-7314.

Conference paper
Published: 29 August 2016 in 2016 35th Chinese Control Conference (CCC)
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This article presents a preliminary research on radio tomographic imaging (RTI) based approach for three-dimensional static body posture sensing. A wireless network organized by a multi-level of radio frequency (RF) sensor array is introduced for posture sensing covering a three-dimensional space. It is assumed the statistical shadowing losses on the passing links between pairs of nodes will be attenuated by the occlusion body. Then an attenuation tomographic image of body posture can be obtained by using the received signal strength (RSS) measurements. Considering the property of spatial piecewise constant of body, total variation (TV) minimization is used to reconstruct the three-dimensional gray image of posture. Experimental studies show the proposed method is able to reconstruct three-dimensional body with several kinds of posture, which will bring significant benefits for future behavior analysis as well as many other applications.

ACS Style

Tong Liu; Zhuo-Qian Liang; Jun Liu; Xiao-Mu Luo. Multi-level radio tomographic imaging based three-dimensional static body posture sensing. 2016 35th Chinese Control Conference (CCC) 2016, 8418 -8422.

AMA Style

Tong Liu, Zhuo-Qian Liang, Jun Liu, Xiao-Mu Luo. Multi-level radio tomographic imaging based three-dimensional static body posture sensing. 2016 35th Chinese Control Conference (CCC). 2016; ():8418-8422.

Chicago/Turabian Style

Tong Liu; Zhuo-Qian Liang; Jun Liu; Xiao-Mu Luo. 2016. "Multi-level radio tomographic imaging based three-dimensional static body posture sensing." 2016 35th Chinese Control Conference (CCC) , no. : 8418-8422.

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.

Conference paper
Published: 01 January 2016 in Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)
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ACS Style

Xiaomu Luo; Tong Liu; Baihua Shen; Jiaming Hong; Qinqun Chen. Human Daily Activity Recognition Using Ceiling Mounted PIR Sensors. Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) 2016, 1 .

AMA Style

Xiaomu Luo, Tong Liu, Baihua Shen, Jiaming Hong, Qinqun Chen. Human Daily Activity Recognition Using Ceiling Mounted PIR Sensors. Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016). 2016; ():1.

Chicago/Turabian Style

Xiaomu Luo; Tong Liu; Baihua Shen; Jiaming Hong; Qinqun Chen. 2016. "Human Daily Activity Recognition Using Ceiling Mounted PIR Sensors." Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016) , no. : 1.

Conference paper
Published: 01 August 2015 in 2015 IEEE International Conference on Information and Automation
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This article reports the findings of a compressive infrared sensing approach for arm gesture acquisition and recognition. The spatial-temporal changing motion information are intrinsical clues for determining the semantics of gesture, which can be considered as a sparse spatial distribution compared with the sensing region. We first built a database of dynamic arm gestures with writing Arabic numerals 0 - 9 in a constrained interaction region, and study its sparse property of spatial distribution. Then we design a pyroelectric infrared (PIR) sensor array with random visibility modulation for compressive gesture acquisition. The semantic recognition of gesture is executed directly on the sensors' low-dimensional sequence using vector quantization (VQ) technology. The experimental results demonstrate the effectiveness of the proposed sensing method.

ACS Style

Tong Liu; Xiao-Mu Luo; Jun Liu; Han Cui. Compressive infrared sensing for arm gesture acquisition and recognition. 2015 IEEE International Conference on Information and Automation 2015, 1882 -1886.

AMA Style

Tong Liu, Xiao-Mu Luo, Jun Liu, Han Cui. Compressive infrared sensing for arm gesture acquisition and recognition. 2015 IEEE International Conference on Information and Automation. 2015; ():1882-1886.

Chicago/Turabian Style

Tong Liu; Xiao-Mu Luo; Jun Liu; Han Cui. 2015. "Compressive infrared sensing for arm gesture acquisition and recognition." 2015 IEEE International Conference on Information and Automation , no. : 1882-1886.

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.

Conference paper
Published: 01 July 2011 in 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage
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Device-free motion tracking with radio tomographic networks using received signal strength (RSS) measurements has attracted considerable research efforts. Since the motion scene to be reconstructed can often be assumed sparse, i.e., it consists only of several targets, the Compressed Sensing (CS) framework can be applied. We cast the motion tracking as a CS problem and employ an efficient algorithm, Orthogonal Matching Pursuit (OMP), for sparse recovery. Furthermore, we exploit a feedback structure which leads to a substantial reduction of the amount of measurements. The feedback structure utilizes the prior knowledge (locations of targets) in time sequence to predict next frame support. Compared with the least-square type methods, the proposed motion tracking based on feedback sparse recovery can directly determine where the targets are located in the network area and reduce the amount of measurements required for reliable tracking. Experimental results show its favorable performance.

ACS Style

Heping Song; Tong Liu; Xiaomu Luo; Guoli Wang. Feedback Based Sparse Recovery for Motion Tracking in RF Sensor Networks. 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage 2011, 1 .

AMA Style

Heping Song, Tong Liu, Xiaomu Luo, Guoli Wang. Feedback Based Sparse Recovery for Motion Tracking in RF Sensor Networks. 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage. 2011; ():1.

Chicago/Turabian Style

Heping Song; Tong Liu; Xiaomu Luo; Guoli Wang. 2011. "Feedback Based Sparse Recovery for Motion Tracking in RF Sensor Networks." 2011 IEEE Sixth International Conference on Networking, Architecture, and Storage , no. : 1.

Conference paper
Published: 01 December 2009 in 2009 IEEE International Conference on Control and Automation
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This paper presents a novel human tracking scheme by using pyroelectric infrared sensors. The scheme includes the visibility modulation of each sensor detector, the layout of the system, the localization and tracking algorithms. The results from the 3D simulations using Webots and Matlab together validate our scheme, and comparisons with other related schemes are made as well. Simulations show that the proposed approach achieves higher tracking accuracy than those of other schemes.

ACS Style

Xiaomu Luo; Baihua Shen; Xuemei Guo; Guocai Luo; Guoli Wang. Human tracking using ceiling pyroelectric infrared sensors. 2009 IEEE International Conference on Control and Automation 2009, 1716 -1721.

AMA Style

Xiaomu Luo, Baihua Shen, Xuemei Guo, Guocai Luo, Guoli Wang. Human tracking using ceiling pyroelectric infrared sensors. 2009 IEEE International Conference on Control and Automation. 2009; ():1716-1721.

Chicago/Turabian Style

Xiaomu Luo; Baihua Shen; Xuemei Guo; Guocai Luo; Guoli Wang. 2009. "Human tracking using ceiling pyroelectric infrared sensors." 2009 IEEE International Conference on Control and Automation , no. : 1716-1721.