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The unpredictable situation from the Coronavirus (COVID-19) globally and the severity of the third wave has resulted in the entire world being quarantined from one another again. Self-quarantine is the only existing solution to stop the spread of the virus when vaccination is under trials. Due to COVID-19, individuals may have difficulties in breathing and may experience cognitive impairment, which results in physical and psychological health issues. Healthcare professionals are doing their best to treat the patients at risk to their health. It is important to develop innovative solutions to provide non-contact and remote assistance to reduce the spread of the virus and to provide better care to patients. In addition, such assistance is important for elderly and those that are already sick in order to provide timely medical assistance and to reduce false alarm/visits to the hospitals. This research aims to provide an innovative solution by remotely monitoring vital signs such as breathing and other connected health during the quarantine. We develop an innovative solution for connected health using software-defined radio (SDR) technology and artificial intelligence (AI). The channel frequency response (CFR) is used to extract the fine-grained wireless channel state information (WCSI) by using the multi-carrier orthogonal frequency division multiplexing (OFDM) technique. The design was validated by simulated channels by analyzing CFR for ideal, additive white gaussian noise (AWGN), fading, and dispersive channels. Finally, various breathing experiments are conducted and the results are illustrated as having classification accuracy of 99.3% for four different breathing patterns using machine learning algorithms. This platform allows medical professionals and caretakers to remotely monitor individuals in a non-contact manner. The developed platform is suitable for both COVID-19 and non-COVID-19 scenarios.
Muhammad Khan; Mubashir Rehman; Ali Mustafa; Raza Shah; Xiaodong Yang. Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology. Electronics 2021, 10, 1558 .
AMA StyleMuhammad Khan, Mubashir Rehman, Ali Mustafa, Raza Shah, Xiaodong Yang. Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology. Electronics. 2021; 10 (13):1558.
Chicago/Turabian StyleMuhammad Khan; Mubashir Rehman; Ali Mustafa; Raza Shah; Xiaodong Yang. 2021. "Intelligent Non-Contact Sensing for Connected Health Using Software Defined Radio Technology." Electronics 10, no. 13: 1558.
Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.
Mubashir Rehman; Raza Shah; Muhammad Khan; Najah AbuAli; Syed Shah; Xiaodong Yang; Akram Alomainy; Muhmmad Imran; Qammer Abbasi. RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. Sensors 2021, 21, 3855 .
AMA StyleMubashir Rehman, Raza Shah, Muhammad Khan, Najah AbuAli, Syed Shah, Xiaodong Yang, Akram Alomainy, Muhmmad Imran, Qammer Abbasi. RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices. Sensors. 2021; 21 (11):3855.
Chicago/Turabian StyleMubashir Rehman; Raza Shah; Muhammad Khan; Najah AbuAli; Syed Shah; Xiaodong Yang; Akram Alomainy; Muhmmad Imran; Qammer Abbasi. 2021. "RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices." Sensors 21, no. 11: 3855.
Iatrogenic contamination causes serious health threats to both patients and healthcare staff. Contact operation is an important transmission route for nosocomial infection. Reducing direct contact during medical treatment can reduce nosocomial infection quickly and effectively. Scientific and technological progress in the 5G era brings new solutions to the problem of iatrogenic contamination. We conducted experiments at 27 GHz and 37 GHz to achieve contactless gesture recognition through the bornprint of body-centric channel. The original channel S-parameters can achieve 82% (27GHz) and 89% (37GHz) basic recognition accuracy through simple statistical analysis. Basic switch recognition and multi-gesture selection recognition can meet the common operation requirements of circulating nurses, greatly reducing contact operations and reducing the probability of cross-contamination. Fully physically isolated body centric channel gesture sensing provides a new entry point for reducing iatrogenic contamination.
Nan Zhao; Xiaodong Yang; Zhiya Zhang; Muhammad Bilal Khan. Circulating Nurse Assistant: Non-Contact Body Centric Gesture Recognition Towards Reducing Latrogenic Contamination. IEEE Journal of Biomedical and Health Informatics 2020, 25, 2305 -2316.
AMA StyleNan Zhao, Xiaodong Yang, Zhiya Zhang, Muhammad Bilal Khan. Circulating Nurse Assistant: Non-Contact Body Centric Gesture Recognition Towards Reducing Latrogenic Contamination. IEEE Journal of Biomedical and Health Informatics. 2020; 25 (6):2305-2316.
Chicago/Turabian StyleNan Zhao; Xiaodong Yang; Zhiya Zhang; Muhammad Bilal Khan. 2020. "Circulating Nurse Assistant: Non-Contact Body Centric Gesture Recognition Towards Reducing Latrogenic Contamination." IEEE Journal of Biomedical and Health Informatics 25, no. 6: 2305-2316.
In this paper, we present ground moving target indication (GMTI) signal processing algorithm encompassing clutter suppression, target detection and parameter estimation. One of the most significant yet least publicized is the need of the GMTI mode for a forward-looking airborne radar. The integration of GMTI mode in a forward-looking airborne radar allows reconnaissance and surveillance operations in all weather conditions. In this context, space time adaptive processing (STAP) offers a unique prospect of enabling the GMTI mode in forward looking airborne radar. STAP is a two-dimensional filter designed to suppress platform motion-induced clutter Doppler spread. Interference is characterized by a covariance matrix. In the case of a forward-looking airborne radar, the clutter Doppler is dependent on range. Clutter Doppler dependency on the range renders the training cells heterogeneous. The heterogeneity effects are particularly prominent in the near range bins. Non-homogeneous training cells have a deleterious effect on STAP performance. In this study, we propose an adaptive Doppler compensation to mitigate the degraded STAP performance in the near range bins. The adaptivity feature circumvents the need for the availability of radar parameters in real-time. The real time implementation of STAP is impeded by requirements of a large number of training samples and covariance matrix inversion. Therefore, there is a dire need to devise a framework to detect and estimate target parameters within the STAP. In this regard, we propose an efficient STAP algorithm to detect and estimate target parameters. STAP weights are applied to the input data to obtain a 3D array. The range projection of the 3D array is utilized to detect and estimate the range of the target, while the angle–Doppler projection is used to estimate spatial and temporal parameters of the target. Most of the literature on STAP is geared towards a known covariance matrix. The assumption of a known covariance matrix may degrade STAP performance because of the inherent mismatches between the actual and assumed target steering vectors. In this study, we estimate the covariance matrix based on the synthetic data generated from a model of an airborne phased array radar. The developed STAP algorithms closely mimic a real-time implementation scheme in an airborne radar platform. The results of the proposed algorithm are validated through target parameter estimation and STAP metrics on synthetic data.
Muhammad Bilal Khan; Ahmed Hussain; Umar Anjum; Channa Babar Ali; Xiaodong Yang. Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar. Electronics 2020, 9, 1896 .
AMA StyleMuhammad Bilal Khan, Ahmed Hussain, Umar Anjum, Channa Babar Ali, Xiaodong Yang. Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar. Electronics. 2020; 9 (11):1896.
Chicago/Turabian StyleMuhammad Bilal Khan; Ahmed Hussain; Umar Anjum; Channa Babar Ali; Xiaodong Yang. 2020. "Adaptive Doppler Compensation for Mitigating Range Dependence in Forward-Looking Airborne Radar." Electronics 9, no. 11: 1896.
For an actual visible light communication system, it is necessary to consider the uniformity of indoor illumination. Most of the existing optimization schemes, however, do not consider the effect of the first reflected light, and do not conform to the practical application conventions, which increases the actual cost and the complexity of system construction. In this paper, considering the first reflected light and based on the conventional layout model and the classic indoor visible light communication model, a scheme using the parameter Q to determine the optimal layout of channel quality is proposed. We determined the layout, and then carried out a simulation. For comparison, the normal layout and the optimal layout of illumination were also simulated. The simulation results show that the illuminance distributions of the three layouts meet the standards of the International Organization for Standardization. The optimal layout of channel quality in the signal-to-noise ratio distribution, maximum delay spread distribution, and impulse response is obviously better than the optimal layout of illumination. In particular, the effective area percentage of the optimal layout of channel quality is increased by 0.32% and 6.08% to 88.80% as compared with the normal layout’s 88.48% and the optimal layout of illumination’s 82.72%. However, compared with the normal layout, the advantages are not very prominent.
Xiangyang Zhang; Nan Zhao; Fadi Al-Turjman; Muhammad Khan; Xiaodong Yang. An Optimization of the Signal-to-Noise Ratio Distribution of an Indoor Visible Light Communication System Based on the Conventional Layout Model. Sustainability 2020, 12, 9006 .
AMA StyleXiangyang Zhang, Nan Zhao, Fadi Al-Turjman, Muhammad Khan, Xiaodong Yang. An Optimization of the Signal-to-Noise Ratio Distribution of an Indoor Visible Light Communication System Based on the Conventional Layout Model. Sustainability. 2020; 12 (21):9006.
Chicago/Turabian StyleXiangyang Zhang; Nan Zhao; Fadi Al-Turjman; Muhammad Khan; Xiaodong Yang. 2020. "An Optimization of the Signal-to-Noise Ratio Distribution of an Indoor Visible Light Communication System Based on the Conventional Layout Model." Sustainability 12, no. 21: 9006.
The rapid finger tap test is widely used in clinical assessment of dyskinesias in Parkinson’s disease. In clinical practice, doctors rely on their clinical experience and use the Parkinson’s Disease Uniform Rating Scale to make a brief judgment of symptoms. We propose a novel C-band microwave sensing method to evaluate finger tapping quantitatively and qualitatively in a non-contact way based on wireless channel information (WCI). The phase difference between adjacent antennas is used to calibrate the original random phase. Outlier filtering and smoothing filtering are used to process WCI waveforms. Based on the resulting signal, we define and extract a set of features related to the features described in UPDRS. Finally, the features are input into a support vector machine (SVM) to obtain results for patients with different severity. The results show that the proposed system can achieve an average accuracy of 99%. Compared with the amplitude, the average quantization accuracy of the phase difference on finger tapping is improved by 3%. In the future, the proposed system could assist doctors to quantify the movement disorders of patients, and it is very promising to be a candidate for clinical practice.
Xiaodong Yang; Lei Guan; Yajun Li; Weigang Wang; Qing Zhang; Masood Ur Rehman; Qammer Hussain Abbasi. Contactless Finger Tapping Detection at C-Band. IEEE Sensors Journal 2020, 21, 5249 -5258.
AMA StyleXiaodong Yang, Lei Guan, Yajun Li, Weigang Wang, Qing Zhang, Masood Ur Rehman, Qammer Hussain Abbasi. Contactless Finger Tapping Detection at C-Band. IEEE Sensors Journal. 2020; 21 (4):5249-5258.
Chicago/Turabian StyleXiaodong Yang; Lei Guan; Yajun Li; Weigang Wang; Qing Zhang; Masood Ur Rehman; Qammer Hussain Abbasi. 2020. "Contactless Finger Tapping Detection at C-Band." IEEE Sensors Journal 21, no. 4: 5249-5258.
Nan Zhao; Zhiya Zhang; Xiaodong Yang; Aifeng Ren; Jianxun Zhao; Masood Ur Rehman. Securing Health Monitoring via Body-Centric Time-Frequency Signature Authorization. IEEE Internet of Things Journal 2020, 8, 4711 -4722.
AMA StyleNan Zhao, Zhiya Zhang, Xiaodong Yang, Aifeng Ren, Jianxun Zhao, Masood Ur Rehman. Securing Health Monitoring via Body-Centric Time-Frequency Signature Authorization. IEEE Internet of Things Journal. 2020; 8 (6):4711-4722.
Chicago/Turabian StyleNan Zhao; Zhiya Zhang; Xiaodong Yang; Aifeng Ren; Jianxun Zhao; Masood Ur Rehman. 2020. "Securing Health Monitoring via Body-Centric Time-Frequency Signature Authorization." IEEE Internet of Things Journal 8, no. 6: 4711-4722.
The rapid spread of the novel coronavirus disease, COVID-19, and its resulting situation has garnered much effort to contain the virus through scientific research. The tragedy has not yet fully run its course, but it is already clear that the crisis is thoroughly global, and science is at the forefront in the fight against the virus. This includes medical professionals trying to cure the sick at risk to their own health; public health management tracking the virus and guardedly calling on such measures as social distancing to curb its spread; and researchers now engaged in the development of diagnostics, monitoring methods, treatments and vaccines. Recent advances in non-contact sensing to improve health care is the motivation of this study in order to contribute to the containment of the COVID-19 outbreak. The objective of this study is to articulate an innovative solution for early diagnosis of COVID-19 symptoms such as abnormal breathing rate, coughing and other vital health problems. To obtain an effective and feasible solution from existing platforms, this study identifies the existing methods used for human activity and health monitoring in a non-contact manner. This systematic review presents the data collection technology, data preprocessing, data preparation, features extraction, classification algorithms and performance achieved by the various non-contact sensing platforms. This study proposes a non-contact sensing platform for the early diagnosis of COVID-19 symptoms and monitoring of the human activities and health during the isolation or quarantine period. Finally, we highlight challenges in developing non-contact sensing platforms to effectively control the COVID-19 situation.
Muhammad Bilal Khan; Zhiya Zhang; Lin Li; Wei Zhao; Mohammed Ali Mohammed Al Hababi; Xiaodong Yang; Qammer H. Abbasi. A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19. Micromachines 2020, 11, 912 .
AMA StyleMuhammad Bilal Khan, Zhiya Zhang, Lin Li, Wei Zhao, Mohammed Ali Mohammed Al Hababi, Xiaodong Yang, Qammer H. Abbasi. A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19. Micromachines. 2020; 11 (10):912.
Chicago/Turabian StyleMuhammad Bilal Khan; Zhiya Zhang; Lin Li; Wei Zhao; Mohammed Ali Mohammed Al Hababi; Xiaodong Yang; Qammer H. Abbasi. 2020. "A Systematic Review of Non-Contact Sensing for Developing a Platform to Contain COVID-19." Micromachines 11, no. 10: 912.
The future of dependable wireless communication will encompass a much eclectic range of applications. Not only are traditional telecommunication facilities such as text messaging, audio and video calling, video download and upload, web browsing, and social networking being improved but also a wide range of sensors and devices in the “Internet of things,” such as “smart cities” and smart hospital applications are being adopted. Researchers are trying hard to ensure timely detection of various diseases anytime and anywhere. In this research, a portable and multifunctional software-defined radio (SDR) platform is designed to detect different activities of human life, in particular for the monitoring of health. The wireless channel state information (WCSI) in the presence of the human body is investigated to capture movements using different frequency bands and is the key idea of this work. Orthogonal frequency division multiplexing (OFDM) with 64 subcarriers and the magnitude and phase responses in the frequency domain are used to capture the WCSI of the activity. The design is validated through simulation and real-time experiments. However, it is widely accepted that simulation results fail to capture real-life situations. Extensive and repeated real-time experiments are carried out on the hardware platform to ensure that the activity is detected accurately. The results achieved by detecting hand motion activity ensure that the system is capable of detecting human body motions and vital signs.
Muhammad Bilal Khan; Chunxi Dong; Mohammed Ali Mohammed Al-Hababi; Xiaodong Yang. Design of a portable and multifunctional dependable wireless communication platform for smart health care. Annals of Telecommunications 2020, 76, 287 -296.
AMA StyleMuhammad Bilal Khan, Chunxi Dong, Mohammed Ali Mohammed Al-Hababi, Xiaodong Yang. Design of a portable and multifunctional dependable wireless communication platform for smart health care. Annals of Telecommunications. 2020; 76 (5-6):287-296.
Chicago/Turabian StyleMuhammad Bilal Khan; Chunxi Dong; Mohammed Ali Mohammed Al-Hababi; Xiaodong Yang. 2020. "Design of a portable and multifunctional dependable wireless communication platform for smart health care." Annals of Telecommunications 76, no. 5-6: 287-296.
Non-contact health care monitoring is a unique feature in the emerging 5G networks that is achieved by exploiting artificial intelligence (AI). The ratio of the number of health care problems and patients is increasing exponentially and creating burgeoning data. The integration of AI and Internet of things (IoT) systems enables us to increase the huge volume of data to be generated. The approach by which AI is applied to the IoT systems enhances the intelligence of the health care system. In post-surgery monitoring of the patient, timely consultation is essential before further loss. Unfortunately, even after the advice of the doctor to the patient, he/she may forget to perform the activity in the correct way, which may lead to complications in recovery. In this research, the idea is to design a non-contact sensing testbed using AI for the classification of post-surgery activities. Universal software-defined radio peripheral (USRP) is utilized to collect the data of spinal cord operated patients during weight lifting activity. The wireless channel state information (WCSI) is extracted by using orthogonal frequency division multiplexing (OFDM) technique. AI applies machine learning to classify the correct and wrong way of weight lifting activity that was considered for experimental analysis. The accuracy achieved by the proposed testbed by using a fine K-nearest neighbor (FKNN) algorithm is 99.6%.
Mohammed Ali Mohammed Al-Hababi; Muhammad Bilal Khan; Fadi Al-Turjman; Nan Zhao; Xiaodong Yang. Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence. Applied Sciences 2020, 10, 4886 .
AMA StyleMohammed Ali Mohammed Al-Hababi, Muhammad Bilal Khan, Fadi Al-Turjman, Nan Zhao, Xiaodong Yang. Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence. Applied Sciences. 2020; 10 (14):4886.
Chicago/Turabian StyleMohammed Ali Mohammed Al-Hababi; Muhammad Bilal Khan; Fadi Al-Turjman; Nan Zhao; Xiaodong Yang. 2020. "Non-Contact Sensing Testbed for Post-Surgery Monitoring by Exploiting Artificial-Intelligence." Applied Sciences 10, no. 14: 4886.
In the past decades, cognitive computing and communication densely used in lots of networking areas. Current improvement in deep learning (DL) and big data analysis create great potential to analyze cognitive intelligence (CI) for many applications such as human activity monitoring and recognition through wireless communication. Cognitive intelligence and wireless communication are using to establish smart healthcare systems. Healthcare monitoring systems turn into interesting research subjects where monitoring post-operative surgical patients are the current focal point to the researcher. In this paper, we argue that deep learning along with the wireless communication technique introduces cognitive intelligence for the healthcare monitoring system. We present a deep learning based convolutional neural network (CNN) model to classify image data and a convenient and multi-functional software-defined radio (SDR) platform to detect movement of the ankle of patients who underwent ankle fracture surgery. Capturing wireless channel state information (WCSI) in the presence of the human body and classifying using CNN to observe distinct movements is the key idea of this study. A universal software radio peripheral (USRP) platform used to capture WCSI data and used for classification. AlexNet and ZFNet both are the famous architecture of CNN and used in a parallel way to classify captured WCSI-based images that converted from numeric data. The classification established on the ankles movements after surgery and classification results show that CNN provides satisfying results where test accuracy is 98.98%.
Arnab Barua; Zhi-Ya Zhang; Fadi Al-Turjman; Xiaodong Yang. Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information. IEEE Access 2020, 8, 112113 -112129.
AMA StyleArnab Barua, Zhi-Ya Zhang, Fadi Al-Turjman, Xiaodong Yang. Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information. IEEE Access. 2020; 8 ():112113-112129.
Chicago/Turabian StyleArnab Barua; Zhi-Ya Zhang; Fadi Al-Turjman; Xiaodong Yang. 2020. "Cognitive Intelligence for Monitoring Fractured Post-Surgery Ankle Activity Using Channel Information." IEEE Access 8, no. : 112113-112129.
Wireless signal technology performs a key role in the research area of medical science to detect diseases that are associated with the human gesture. Recently, wireless channel information (WCI) has received vast consideration because of its potential practice of detecting the human behavior. In this article, we present the convolutional neural network (CNN) model to classify WCI‐based image data and determine the involuntary movement (tic disorder) diseases. Motor and vocal are two aspects of tic disorder and depend on the amount of complication, both aspects classified into the simple and complex group, and each group has several symptoms. Using WCI data of symptoms from the simple and complex group of motor aspects, we form a dataset to train the CNN model. Experimental results show that CNN provides satisfying result in classification, and accuracy is more than 97%.
Arnab Barua; Chunxi Dong; Xiaodong Yang. A deep learning approach for detecting tic disorder using wireless channel information. Transactions on Emerging Telecommunications Technologies 2020, 1 .
AMA StyleArnab Barua, Chunxi Dong, Xiaodong Yang. A deep learning approach for detecting tic disorder using wireless channel information. Transactions on Emerging Telecommunications Technologies. 2020; ():1.
Chicago/Turabian StyleArnab Barua; Chunxi Dong; Xiaodong Yang. 2020. "A deep learning approach for detecting tic disorder using wireless channel information." Transactions on Emerging Telecommunications Technologies , no. : 1.
Wireless localization systems have significant impact in the field of human-driven edge computing (HEC). It became very attractive among the researchers and used in applications of numerous areas such as medical, industrial, public safety, logistics, and so on. Ultra-wideband (UWB) technology used in localization systems owing to achieving high accuracy in real-time. In this paper, we exhibit a UWB based localization system based on the edge computing (EC) paradigm to analyze the wandering behavior of the patients who are suffering from dementia disease in the large-scale form. Physical changes in the brain are responsible for dementia disease. The appearance of wandering behavior is a common manner of the patients, which also a threat and interference for caregivers. We used the UWB standard appliance to symbolize various sorts of wandering patterns, including pacing, lapping, and two random movements in the large 2D map. The flow of all the movements illustrated in the X and Y-axis. Support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms used to classify all the patterns and accuracy result is above 99%. The result shows that the proposed system can achieve high accuracy in classification and satisfactory for applications in the medical area.
Arnab Barua; Chunxi Dong; Fadi Al-Turjman; Xiaodong Yang. Edge Computing-Based Localization Technique to Detecting Behavior of Dementia. IEEE Access 2020, 8, 82108 -82119.
AMA StyleArnab Barua, Chunxi Dong, Fadi Al-Turjman, Xiaodong Yang. Edge Computing-Based Localization Technique to Detecting Behavior of Dementia. IEEE Access. 2020; 8 (99):82108-82119.
Chicago/Turabian StyleArnab Barua; Chunxi Dong; Fadi Al-Turjman; Xiaodong Yang. 2020. "Edge Computing-Based Localization Technique to Detecting Behavior of Dementia." IEEE Access 8, no. 99: 82108-82119.
Cerebellar Ataxia (CA) is a neurological disease with the symptom of poor coordination of movement and balance disorders. In clinical medicine, the heel-knee-shin test and the rapid alternating movements test are important basis for assessing CA. Based on the above tests, this paper presents a non-contact method and investigates the feasibility of this method for detecting CA. This body sensor networks uses wireless devices operating in the C-band frequency range to capture data of both types of tests without intrusiveness. The obtained data of tests contains massive useful information about human health which is really significant to subjects. But the information can be so subtle that people ignore the value hidden in it. So utility pattern mining (UPM) is used for the purpose of mining subjects’ activity pattern. We find that the subjects’ activity pattern differs greatly in amplitude information. We extracted the amplitude information that is helpful for analyzing the test’ results to determine whether the test is positive or negative. Then we use different kind of algorithms to classify the data samples. Among them, support vector machine (SVM) has the best classification effect on both tests. In the heel-knee-shin test, the coincidence rate (π) is 98.7%, the sensitivity (Se) is 98.9% and the specificity (Sp) is 98.5%. In the rapid alternating movements test, the π is 99.4%, Se is 99.8% and Sp is 99%. The experimental results show that this technique has the potential to open up new clinical opportunities for contactless and accurate CA monitoring in a patient-friendly and flexible environment.
Jiaxin Jin; Wanrong Sun; Fadi Al-Turjman; Muhammad Bilal Khan; Xiaodong Yang. Activity Pattern Mining for Healthcare. IEEE Access 2020, 8, 56730 -56738.
AMA StyleJiaxin Jin, Wanrong Sun, Fadi Al-Turjman, Muhammad Bilal Khan, Xiaodong Yang. Activity Pattern Mining for Healthcare. IEEE Access. 2020; 8 (99):56730-56738.
Chicago/Turabian StyleJiaxin Jin; Wanrong Sun; Fadi Al-Turjman; Muhammad Bilal Khan; Xiaodong Yang. 2020. "Activity Pattern Mining for Healthcare." IEEE Access 8, no. 99: 56730-56738.
Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.
Tanjila Akter Zilani; Fadi Al-Turjman; Muhammad Bilal Khan; Nan Zhao; Xiaodong Yang. Monitoring Movements of Ataxia Patient by Using UWB Technology. Sensors 2020, 20, 931 .
AMA StyleTanjila Akter Zilani, Fadi Al-Turjman, Muhammad Bilal Khan, Nan Zhao, Xiaodong Yang. Monitoring Movements of Ataxia Patient by Using UWB Technology. Sensors. 2020; 20 (3):931.
Chicago/Turabian StyleTanjila Akter Zilani; Fadi Al-Turjman; Muhammad Bilal Khan; Nan Zhao; Xiaodong Yang. 2020. "Monitoring Movements of Ataxia Patient by Using UWB Technology." Sensors 20, no. 3: 931.
Huntington’s disease (HD) is a rare genetic disorder that cannot be cured by current medical techniques. With the development of the disease, the life of patients will become more and more difficult. It is necessary to timely and effectively evaluate the development of the patient’s condition based on the patient’s clinical symptoms to help doctors to formulate a reasonable and effective treatment plan, alleviate the condition, and improve the quality of life, which reflects humane care. Currently, wearable devices or video surveillance are generally used to monitor the patients, and these schemes have some disadvantages. This paper presents a new method to monitor patients with HD using wireless sensing technology. Firstly, experimental data were collected by the self-developed microwave sensing platform (MSP), and then the data were preprocessed. Finally, support vector machine (SVM) and random forest (RF) algorithms were used to train the model. The MSP system continuously monitors patients in a non-contact way, which will not bring inconvenience to patients’ lives, and will not involve privacy issues. The experimental results show that the prediction accuracy of SVM can be as high as 98.0% and that of RF can be as high as 96.7%, which proves the feasibility of the technical scheme described in this paper.
Qiyu Zhu; Lei Guan; Muhammad Bilal Khan; Xiaodong Yang. Monitoring of Huntington’s Disease Based on Wireless Sensing Technology. Applied Sciences 2020, 10, 870 .
AMA StyleQiyu Zhu, Lei Guan, Muhammad Bilal Khan, Xiaodong Yang. Monitoring of Huntington’s Disease Based on Wireless Sensing Technology. Applied Sciences. 2020; 10 (3):870.
Chicago/Turabian StyleQiyu Zhu; Lei Guan; Muhammad Bilal Khan; Xiaodong Yang. 2020. "Monitoring of Huntington’s Disease Based on Wireless Sensing Technology." Applied Sciences 10, no. 3: 870.
This article presents a design for high-gain MIMO antennas with compact geometry. The proposed design is composed of four antennas in MIMO configuration, wherein, each antenna is made up of small units of microstrip patches. The overall geometry is printed on the top layer of the substrate, i.e., Rogers RT-5880 with permittivity of 2.2, permeability of 1.0, dielectric loss of 0.0009, and depth of 0.508 mm. The proposed design covers an area of 29.5 × 61.4 mm2, wherein each antenna covers an area of 11.82 × 25.28 mm2. The dimensions of the microstrip lines in each MIMO element were optimized to achieve a good impedance matching. The design is resonating at 61 GHz, with a wide practical bandwidth of more than 7 GHz, thereby covering IEEE 802.11ad WiGig (58–65 GHz). The average value of gain ranges from 9.45 to 13.6 dBi over the entire frequency bandwidth whereas, the average value of efficiency ranges from 55.5% to 84.3%. The proposed design attains a compact volume, wide bandwidth, and good gain and efficiency performances, which makes it suitable for WiGig terminals.
Sultan Shoaib; Nosherwan Shoaib; Riqza Y. Y. Khattak; Imran Shoaib; Masood Ur Rehman; Xiaodong Yang. Design and Development of MIMO Antennas for WiGig Terminals. Electronics 2019, 8, 1548 .
AMA StyleSultan Shoaib, Nosherwan Shoaib, Riqza Y. Y. Khattak, Imran Shoaib, Masood Ur Rehman, Xiaodong Yang. Design and Development of MIMO Antennas for WiGig Terminals. Electronics. 2019; 8 (12):1548.
Chicago/Turabian StyleSultan Shoaib; Nosherwan Shoaib; Riqza Y. Y. Khattak; Imran Shoaib; Masood Ur Rehman; Xiaodong Yang. 2019. "Design and Development of MIMO Antennas for WiGig Terminals." Electronics 8, no. 12: 1548.
In clinical practice, doctors are using bedside tests to assist in the diagnosis of paraparesis. The disadvantage is that it depends on the doctor’s clinical experience and the supervisor’s judgment. Therefore, there is an urgent need for an objective and efficient diagnostic equipment. With the rapid development of wireless technology, ubiquitous RF signals become a promising sensing technology. In this study, we propose a non-contact wireless sensing method based on RF signals to detect paraparesis. Our system can reduce the burden on doctors and improve work efficiency. Outlier filters and wavelet hard threshold decomposition are used to filter the wireless signal. A 1D-CNN model is designed to automatically extract valid features and classifications. The results analyze in two bedside tests, our system perform efficiently and accurately patient screening with suspected paraparesis. This provide more effective guidance and assistance for further treatment. The proposed method has an average accuracy of 99.4% and 98.5% in the Barre test and Mingazzini test respectively.
Lei Guan; Fangming Hu; Fadi Al-Turjman; Muhammad Bilal Khan; Xiaodong Yang. A Non-Contact Paraparesis Detection Technique Based on 1D-CNN. IEEE Access 2019, 7, 182280 -182288.
AMA StyleLei Guan, Fangming Hu, Fadi Al-Turjman, Muhammad Bilal Khan, Xiaodong Yang. A Non-Contact Paraparesis Detection Technique Based on 1D-CNN. IEEE Access. 2019; 7 (99):182280-182288.
Chicago/Turabian StyleLei Guan; Fangming Hu; Fadi Al-Turjman; Muhammad Bilal Khan; Xiaodong Yang. 2019. "A Non-Contact Paraparesis Detection Technique Based on 1D-CNN." IEEE Access 7, no. 99: 182280-182288.
Nan Zhao; Zhi-Ya Zhang; Aifeng Ren; Jianxun Zhao; Xiaodong Yang; Masood Ur-Rehman; Qammer H. Abbasi. Hand Palm Local Channel Characterization for Millimeter-Wave Body-Centric Applications. IEEE Access 2019, 7, 150976 -150982.
AMA StyleNan Zhao, Zhi-Ya Zhang, Aifeng Ren, Jianxun Zhao, Xiaodong Yang, Masood Ur-Rehman, Qammer H. Abbasi. Hand Palm Local Channel Characterization for Millimeter-Wave Body-Centric Applications. IEEE Access. 2019; 7 ():150976-150982.
Chicago/Turabian StyleNan Zhao; Zhi-Ya Zhang; Aifeng Ren; Jianxun Zhao; Xiaodong Yang; Masood Ur-Rehman; Qammer H. Abbasi. 2019. "Hand Palm Local Channel Characterization for Millimeter-Wave Body-Centric Applications." IEEE Access 7, no. : 150976-150982.
Conventional liquid detection instruments are very expensive and not conducive to large-scale deployment. In this work, we propose a method for detecting and identifying suspicious liquids based on the dielectric constant by utilizing the radio signals at a 5G frequency band. There are three major experiments: first, we use wireless channel information (WCI) to distinguish between suspicious and nonsuspicious liquids; then we identify the type of suspicious liquids; and finally, we distinguish the different concentrations of alcohol. The K-Nearest Neighbor (KNN) algorithm is used to classify the amplitude information extracted from the WCI matrix to detect and identify liquids, which is suitable for multimodal problems and easy to implement without training. The experimental result analysis showed that our method could detect more than 98% of the suspicious liquids, identify more than 97% of the suspicious liquid types, and distinguish up to 94% of the different concentrations of alcohol.
Jiewen Deng; Wanrong Sun; Lei Guan; Nan Zhao; Muhammad Bilal Khan; Aifeng Ren; Jianxun Zhao; Xiaodong Yang; Qammer H. Abbasi. Noninvasive Suspicious Liquid Detection Using Wireless Signals. Sensors 2019, 19, 4086 .
AMA StyleJiewen Deng, Wanrong Sun, Lei Guan, Nan Zhao, Muhammad Bilal Khan, Aifeng Ren, Jianxun Zhao, Xiaodong Yang, Qammer H. Abbasi. Noninvasive Suspicious Liquid Detection Using Wireless Signals. Sensors. 2019; 19 (19):4086.
Chicago/Turabian StyleJiewen Deng; Wanrong Sun; Lei Guan; Nan Zhao; Muhammad Bilal Khan; Aifeng Ren; Jianxun Zhao; Xiaodong Yang; Qammer H. Abbasi. 2019. "Noninvasive Suspicious Liquid Detection Using Wireless Signals." Sensors 19, no. 19: 4086.