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学者姓名：陈良琴
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Abstract ：
In the evolution of non-contact human-computer interactions, the integration of low-resolution infrared thermal pile sensors has gained prominence, particularly in applications like smart homes and human-computer collaboration. However, challenges arise from uncontrollable environmental temperatures, causing issues such as blurry or missing details in finger contours during infrared thermal pile imaging. This paper addresses these challenges by employing the DiffBIR diffusion model for gesture reconstruction and integrating the CBAM module into a lightweight gesture recognition network named Infrared Image Reconstruction Gesture Network (IR-GNet). The proposed approach enhances attention weights for hand regions and effectively eliminates interference from other heat sources, ensuring optimal performance in low-resolution scenarios. Our model achieved state-of-the-art accuracy on both static and dynamic gesture datasets, and on the Worker-Robot Collaboration (WRC) gesture dataset. Experiments involving thermal source interference also demonstrated that the model outperforms other lightweight models in terms of recognition accuracy while meeting real-time requirements. Moreover, this study introduces a joint training approach to mitigate the relatively high time cost associated with image reconstruction. By combining the training set of original images with their corresponding reconstructed images, and using only original, unreconstructed images for testing, significant enhancements in recognition accuracy were observed under identical testing conditions. These results affirm the feasibility of deploying the proposed network in real-time scenarios to achieve a robust gesture recognition system.
Keyword ：
Diffusion model Diffusion model Gesture recognition Gesture recognition Infrared thermopile sensor Infrared thermopile sensor Lightweight network Lightweight network Low resolution Low resolution
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| GB/T 7714 | Chen, Liangqin , Sun, Qinghao , Xu, Zhimeng et al. A low-resolution infrared gesture recognition method combining weak information reconstruction and joint training strategy [J]. | DIGITAL SIGNAL PROCESSING , 2025 , 158 . |
| MLA | Chen, Liangqin et al. "A low-resolution infrared gesture recognition method combining weak information reconstruction and joint training strategy" . | DIGITAL SIGNAL PROCESSING 158 (2025) . |
| APA | Chen, Liangqin , Sun, Qinghao , Xu, Zhimeng , Liao, Yipeng , Chen, Zhizhang (David) . A low-resolution infrared gesture recognition method combining weak information reconstruction and joint training strategy . | DIGITAL SIGNAL PROCESSING , 2025 , 158 . |
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Heart rate variability (HRV), indicating the variation in intervals between consecutive heartbeats, is a crucial physiological indicator of human health. However, detecting HRV using frequency-modulated continuous-wave (FMCW) radar is highly susceptible to interference from respiration, minor body movements, and environmental noise, especially in multitarget scenarios. To address these challenges, we propose the Health-Radar system, which comprises three functional modules. In the target detection module, the system accurately identifies the number and locations of targets. In the phase extraction module, the signal undergoes dc offset calibration to extract the chest displacement signals. In the heartbeat signal extraction module, we introduce Health-VMD, an adaptive parameter variational mode decomposition (VMD) method. This method optimizes the VMD parameters using an improved grasshopper optimization algorithm (GOA) and accurately extracts vital sign signals from chest displacement signals to estimate HRV. In addition, we propose a novel objective function, composed of permutation entropy, mutual information, and energy loss rate (PME), specifically designed for vital sign extraction. Experiments with multiple participants in various scenarios demonstrated that the designed system can accurately identify different targets and detect HRV with high precision. The root-mean-square error (RMSE) of the detected interbeat intervals (IBIs) is 29.72 ms, the RMSE of the standard deviation of NN intervals (SDNN) is 4.1 ms, and the RMSE of the root mean square of successive differences (RMSSD) is 18.61 ms, outperforming existing methods.
Keyword ：
Accuracy Accuracy Biomedical monitoring Biomedical monitoring Estimation Estimation Frequency-modulated continuous-wave (FMCW) radar Frequency-modulated continuous-wave (FMCW) radar Harmonic analysis Harmonic analysis Heart beat Heart beat Heart rate variability Heart rate variability heart rate variability (HRV) heart rate variability (HRV) multitarget vital signs detection multitarget vital signs detection noncontact detection noncontact detection Optimization Optimization Radar Radar Radar detection Radar detection Sensors Sensors variational mode decomposition (VMD) variational mode decomposition (VMD)
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| GB/T 7714 | Xu, Zhimeng , Ye, Tao , Chen, Liangqin et al. Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar [J]. | IEEE SENSORS JOURNAL , 2025 , 25 (1) : 405-418 . |
| MLA | Xu, Zhimeng et al. "Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar" . | IEEE SENSORS JOURNAL 25 . 1 (2025) : 405-418 . |
| APA | Xu, Zhimeng , Ye, Tao , Chen, Liangqin , Gao, Yueming , Chen, Zhizhang . Health-Radar: Noncontact Multitarget Heart Rate Variability Detection Using FMCW Radar . | IEEE SENSORS JOURNAL , 2025 , 25 (1) , 405-418 . |
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This article presents a novel millimeter-wave (mm-wave) radar-based continuous motion detection approach called mmCMD for the long-term care of the elderly. The proposed mmCMD visualizes the micro-Doppler signatures of continuous motions generated from millimeter radar as images, which are then analyzed by an object detection network for continuous motion detection. To improve the imaging quality of micro-Doppler signatures, a dynamic feature visualization (DFV) method is proposed by selectively mapping the micro-Doppler matrix (MDM) elements with significant values, highlighting human motion to enhance subsequent detection network's accuracy in capturing the details of the motion. Furthermore, a novel detection network is designed for the visualized micro-Doppler images by combining the specially designed fusion squeeze-and-excitation (FSE) module with the coordinate attention (CA) into the YOLOv5 architecture, which is distinct from prior works that overlook global contextual information. Experimental results demonstrate that the proposed mmCMD achieves a mean average precision (mAP) of 93% at the intersection over union (IoU) thresholds from 0.5 to 0.95 and an F1 score of 99% for 12 actions, which makes it a promising solution for remotely monitoring and detecting elderly individuals' activities to enhance safety and risk prevention capabilities.
Keyword ：
Continuous human motion detection Continuous human motion detection frequency-modulated continuous-wave (FMCW) radar frequency-modulated continuous-wave (FMCW) radar micro-Doppler effect micro-Doppler effect object detection object detection YOLOv5 YOLOv5
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| GB/T 7714 | Xu, Zhimeng , Ding, Junyin , Zhang, Shanshan et al. mmCMD: Continuous Motion Detection From Visualized Radar Micro-Doppler Signatures Using Visual Object Detection Techniques [J]. | IEEE SENSORS JOURNAL , 2024 , 24 (3) : 3394-3405 . |
| MLA | Xu, Zhimeng et al. "mmCMD: Continuous Motion Detection From Visualized Radar Micro-Doppler Signatures Using Visual Object Detection Techniques" . | IEEE SENSORS JOURNAL 24 . 3 (2024) : 3394-3405 . |
| APA | Xu, Zhimeng , Ding, Junyin , Zhang, Shanshan , Gao, Yueming , Chen, Liangqin , Vasic, Zeljka Lucev et al. mmCMD: Continuous Motion Detection From Visualized Radar Micro-Doppler Signatures Using Visual Object Detection Techniques . | IEEE SENSORS JOURNAL , 2024 , 24 (3) , 3394-3405 . |
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In this paper, a context-based human target detection and position estimation algorithm, as well as a position calibration algorithm based on radar irradiation angle are proposed to improve the positioning accuracy, which is limited by the sparse and easily submerged characteristics of the point cloud generated by millimeter-wave radar, which leads to difficulty in achieving high-precision positioning. Furthermore, an indoor target positioning and tracking system is built using 77 GHz millimeter-wave radar to verify the proposed algorithms. The experimental results indicate that the proposed algorithms can improve the positioning accuracy both in single-person and multi-person positioning scenarios, with median positioning errors 8.7 cm (36.7% decrease) and 12.95 cm (average) respectively. Therefore, the proposed sensing method is considered as a very promising technique for designing a high precision human trajectory tracking and positioning radar system.
Keyword ：
millimeter-wave radar millimeter-wave radar point cloud point cloud target positioning target positioning
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| GB/T 7714 | Xu, Zhimeng , Wu, Zhenbin , Li, Dan et al. Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2023 , 34 (2) . |
| MLA | Xu, Zhimeng et al. "Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar" . | MEASUREMENT SCIENCE AND TECHNOLOGY 34 . 2 (2023) . |
| APA | Xu, Zhimeng , Wu, Zhenbin , Li, Dan , Chen, Liangqin , Zhang, Shanshan , Chen, Zhizhang (David) . Position estimation and calibration for high precision human positioning and tracking using millimeter-wave radar . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2023 , 34 (2) . |
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在驾驶和在线课堂这类持续时间较长的行为中,人们容易出现分心而导致事故发生或上课效率差.为检测这类分心行为,提出了一种基于低分辨率红外阵列传感器的头部运动检测方法,它在实现行为监测的同时也保护了个人隐私.首先,基于图像处理方法提取了人体的显著区域;然后设计了一种三维图像融合算法来提取时空域的变化信息;最后,设计了一个改进的残差网络来实现头部运动分类.面向驾驶和在线课堂应用场景设计了10种头部运动.实验结果表明,在50 cm到100 cm的检测范围内,平均识别率为96.76％,处理速度为9帧/s,优于现有算法.将该系统应用于车内实测,也达到了93.7％的准确率.
Keyword ：
三维图像融合 三维图像融合 分心行为 分心行为 头部运动检测 头部运动检测 红外阵列传感器 红外阵列传感器
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| GB/T 7714 | 陈良琴 , 曾铭萱 , 许志猛 et al. 基于低分辨率红外阵列传感器的头部运动检测 [J]. | 红外与毫米波学报 , 2023 , 42 (2) : 276-284 . |
| MLA | 陈良琴 et al. "基于低分辨率红外阵列传感器的头部运动检测" . | 红外与毫米波学报 42 . 2 (2023) : 276-284 . |
| APA | 陈良琴 , 曾铭萱 , 许志猛 , 陈志璋 . 基于低分辨率红外阵列传感器的头部运动检测 . | 红外与毫米波学报 , 2023 , 42 (2) , 276-284 . |
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With the development of wireless communication technology, indoor tracking technology has been rapidly developed. Wits presents a new indoor positioning and tracking algorithm with channel state information of Wi-Fi signals. Wits tracks using motion speed. Firstly, it eliminates static path interference and calibrates the phase information. Then, the maximum likelihood of the phase is used to estimate the radial Doppler velocity of the target. Experiments were conducted, and two sets of receiving antennas were used to determine the velocity of a human. Finally, speed and time intervals were used to track the target. Experimental results show that Wits can achieve the mean error of 0.235 m in two different environments with a known starting point. If the starting point is unknown, the mean error is 0.410 m. Wits has good accuracy and efficiency for practical applications.
Keyword ：
channel state information channel state information Doppler velocity Doppler velocity maximum likelihood estimation maximum likelihood estimation trajectory trajectory Wi-Fi Wi-Fi
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| GB/T 7714 | Tian, Li-Ping , Chen, Liang-Qin , Xu, Zhi-Meng et al. Wits: An Efficient Wi-Fi Based Indoor Positioning and Tracking System [J]. | REMOTE SENSING , 2022 , 14 (1) . |
| MLA | Tian, Li-Ping et al. "Wits: An Efficient Wi-Fi Based Indoor Positioning and Tracking System" . | REMOTE SENSING 14 . 1 (2022) . |
| APA | Tian, Li-Ping , Chen, Liang-Qin , Xu, Zhi-Meng , Chen, Zhizhang (David) . Wits: An Efficient Wi-Fi Based Indoor Positioning and Tracking System . | REMOTE SENSING , 2022 , 14 (1) . |
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An angle estimation algorithm for tracking indoor moving targets with WiFi is proposed. First, phase calibration and static path elimination are proposed and performed on the collected channel state information signals from different antennas. Then, the angle of arrival information is obtained with the joint estimation algorithm of the angle of arrival (AOA) and time of flight (TOF). To deal with the multipath effects, we adopt the DBscan spatiotemporal clustering algorithm with adaptive parameters. In addition, the time-continuous angle of arrival information is obtained by interpolating and supplementing points to extract the dynamic signal paths better. Finally, the least-squares method is used for linear fitting to obtain the final angle information of a moving target. Experiments are conducted with the tracking data set presented with Tsinghua's Widar 2.0. The results show that the average angle estimation error with the proposed algorithm is smaller than Widar2.0. The average angle error is about 7.18 degrees in the classroom environment, 3.62 degrees in the corridor environment, and 12.16 degrees in the office environment; they are smaller than the errors of the existing system.
Keyword ：
angle estimation angle estimation AOA AOA channel state information (CSI) channel state information (CSI) DBscan DBscan least-squares method least-squares method WiFi WiFi
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| GB/T 7714 | Tian, Liping , Chen, Liangqin , Xu, Zhimeng et al. An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering [J]. | SENSORS , 2022 , 22 (1) . |
| MLA | Tian, Liping et al. "An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering" . | SENSORS 22 . 1 (2022) . |
| APA | Tian, Liping , Chen, Liangqin , Xu, Zhimeng , Chen, Zhizhang . An Angle Recognition Algorithm for Tracking Moving Targets Using WiFi Signals with Adaptive Spatiotemporal Clustering . | SENSORS , 2022 , 22 (1) . |
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This paper proposes a novel time-frequency feature fusion method to recognise patients' behaviours based on the Frequency Modulated Continuous Wave (FMCW) radar system, which can locate patients as well as recognise their current actions and thus is expected to solve the shortage of medical staff caused by the novel coronavirus pneumonia (COVID-19). To recognise the patient's behaviour, the FMCW radar is utilised to acquire point clouds reflected by the human body, and the micro-Doppler spectrogram is generated by human motion. Then features are extracted and fused from the time-domain information of point clouds and the frequency-domain information of the micro-Doppler spectrogram respectively. According to the fused features, the patient's behaviour is recognised by a Bayesian optimisation random forest algorithm, where the role of Bayesian optimisation is to select the best hyper-parameters for the random forest, i.e. the number of random forest decision trees, the depth of leaves, and the number of features. The experimental results show that an average accuracy of 99.3% can be achieved by using the time-frequency fusion with the Bayesian optimisation random forest model to recognise six actions.
Keyword ：
Bayesian optimisation random forest Bayesian optimisation random forest FMCW radar FMCW radar intelligent ward monitoring intelligent ward monitoring time-frequency fusion time-frequency fusion
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| GB/T 7714 | Xu, Zhimeng , Zhang, Shanshan , Chen, Liangqin et al. Patient behavior detection based on time-frequency fusion of FMCW radar [J]. | MEASUREMENT SCIENCE AND TECHNOLOGY , 2022 , 33 (11) . |
| MLA | Xu, Zhimeng et al. "Patient behavior detection based on time-frequency fusion of FMCW radar" . | MEASUREMENT SCIENCE AND TECHNOLOGY 33 . 11 (2022) . |
| APA | Xu, Zhimeng , Zhang, Shanshan , Chen, Liangqin , Wu, Zhenbin . Patient behavior detection based on time-frequency fusion of FMCW radar . | MEASUREMENT SCIENCE AND TECHNOLOGY , 2022 , 33 (11) . |
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Counting the number of people and estimating their walking speeds are essential in crowd control and flow. In this work, we propose a system that uses prevalent Wi-Fi signals to identify the number of people entering and leaving a room through a door. It selects the best subcarrier of Wi-Fi signals and applies the Hampel filter to remove outlier information first. Then, it employs a double threshold method to determine the start and end times of entering or leaving. Afterward, it compares the detected signals with the precollected database using the dynamic time-warping algorithm and determines the number of people. It uses a variance threshold method to identify the states of entering or leaving. It also employs a nonlinear fitting approach to calculate the walking speeds. The experiments show that, in a large empty laboratory, the accuracy rates in determining the number of people are 100% for one person, 81% for two persons, and 95% for three persons. In a small office, the accuracy rates for detecting the number of people are 98% for one or two persons, 82% for three persons, 93% for four, and 75% for five persons. For the walking speed estimation, the accuracy rate for a speed error of less than 0.2410 m/s is 75% for a single person.
Keyword ：
channel state information channel state information dynamic time warping dynamic time warping entering and leaving entering and leaving number of persons number of persons variance threshold variance threshold walking speed walking speed Wi-Fi Wi-Fi
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| GB/T 7714 | Tian, Liping , Chen, Liangqin , Xu, Zhimeng et al. A People-Counting and Speed-Estimation System Using Wi-Fi Signals [J]. | SENSORS , 2021 , 21 (10) . |
| MLA | Tian, Liping et al. "A People-Counting and Speed-Estimation System Using Wi-Fi Signals" . | SENSORS 21 . 10 (2021) . |
| APA | Tian, Liping , Chen, Liangqin , Xu, Zhimeng , Chen, Zhizhang (David) . A People-Counting and Speed-Estimation System Using Wi-Fi Signals . | SENSORS , 2021 , 21 (10) . |
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Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi-Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi-dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two-level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one-person and two-person is 99.6% and 94.6% respectively.
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| GB/T 7714 | Xu, Zhimeng , Xi, Jianwei , Chen, Liangqin . Area human sensing via ambient Wi-Fi signals [J]. | IET COMMUNICATIONS , 2021 , 15 (18) : 2275-2284 . |
| MLA | Xu, Zhimeng et al. "Area human sensing via ambient Wi-Fi signals" . | IET COMMUNICATIONS 15 . 18 (2021) : 2275-2284 . |
| APA | Xu, Zhimeng , Xi, Jianwei , Chen, Liangqin . Area human sensing via ambient Wi-Fi signals . | IET COMMUNICATIONS , 2021 , 15 (18) , 2275-2284 . |
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