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Hand gesture recognition from multi-channel surface electromyography (sEMG) have been widely studied in the past decade. By analyzing muscle activities measured from forearm muscles, multiple hand gestures can be recognized. This technology can benefit upper-limb amputees in motion intention recognition, especially for those with trans-radial amputation, in terms of prosthesis control, rehabilitation and further human–computer interaction. However, due to the scarcity of signals collected from amputees, many related studies used signals from intact subjects as a proxy and result in overoptimistic classification performance. Comparing to sEMG signals from intact subjects, signals from upper-limb amputees suffer from signal quality deterioration which relates to the level of amputation and maybe other amputation information. Therefore, this study aims at improving the motion intention recognition performance in trans-radial amputated subjects. To tackle the challenges of data scarcity and signal quality deterioration, we propose a CNN-based transfer learning solution leveraging the knowledge learned from sEMG signals of intact subjects. The proposed method was developed from and tested with NinaPro database where 20 intact subjects and 11 amputees. We obtained 67.5% accuracy in the mDWT feature after transfer. And the results improved by 9.4% after transfer compared to no transfer in the RMS feature. In the end of the study, we further discussed the correlation between classification accuracy and amputation information including the percentage of remaining forearm and the number of years since amputation.
Jinghua Fan; Mingzhe Jiang; Chuang Lin; Gloria Li; Jinan Fiaidhi; Chenfei Ma; Wanqing Wu. Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning. Neural Computing and Applications 2021, 1 -11.
AMA StyleJinghua Fan, Mingzhe Jiang, Chuang Lin, Gloria Li, Jinan Fiaidhi, Chenfei Ma, Wanqing Wu. Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning. Neural Computing and Applications. 2021; ():1-11.
Chicago/Turabian StyleJinghua Fan; Mingzhe Jiang; Chuang Lin; Gloria Li; Jinan Fiaidhi; Chenfei Ma; Wanqing Wu. 2021. "Improving sEMG-based motion intention recognition for upper-limb amputees using transfer learning." Neural Computing and Applications , no. : 1-11.
The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high risk pregnancies based on IoT sensors, feature extraction based on data analytics and an intelligent diagnostic aid system based on a one-dimensional Convolutional Neural Network (CNN) classifier. The Fetal Heart Rate and a group of maternal clinical indicators such as the uterine tonus activity, blood pressure, heart rate, temperature and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different format and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.
Joao Alexandre Lobo Marques; Tao Han; Wanqing Wu; Joao Paulo Do Vale Madeiro; Aloisio V. Lira Neto; Raffaele Gravina; Giancarlo Fortino; Victor Hugo C. de Albuquerque. IoT-based Smart Health System for Ambulatory Maternal and Fetal Monitoring. IEEE Internet of Things Journal 2020, PP, 1 -1.
AMA StyleJoao Alexandre Lobo Marques, Tao Han, Wanqing Wu, Joao Paulo Do Vale Madeiro, Aloisio V. Lira Neto, Raffaele Gravina, Giancarlo Fortino, Victor Hugo C. de Albuquerque. IoT-based Smart Health System for Ambulatory Maternal and Fetal Monitoring. IEEE Internet of Things Journal. 2020; PP (99):1-1.
Chicago/Turabian StyleJoao Alexandre Lobo Marques; Tao Han; Wanqing Wu; Joao Paulo Do Vale Madeiro; Aloisio V. Lira Neto; Raffaele Gravina; Giancarlo Fortino; Victor Hugo C. de Albuquerque. 2020. "IoT-based Smart Health System for Ambulatory Maternal and Fetal Monitoring." IEEE Internet of Things Journal PP, no. 99: 1-1.
Background and Objective: Acute psychological stress is conducted along a different neural pathway from acute physical stress and causes a conduction delay. This conduction delay reflected on the central nervous system (CNS) as an evaluation process, which allows the human body to make the best response to a stressor based on preceding experiences. However, how this conduction delay reflected on physiological explicit, remains to be studied. Methods: In this work, the variation of nine traditional heart rate variability (HRV) features are studied from twenty-six healthy subjects during acute physical (Cold pressor test (CPT)) and psychological (Stroop color word test (SCWT)) stress on separate occasions. Besides, we discussed ultra-short and short-term HRV analysis and their relationship with acute stress. Results: The experimental results suggest that there was a delayed response of HRV features under acute psychological stress compared with acute physical stress. In addition, during the later stage of stress, four features could differentiate between baseline and acute psychological stress (p < 0.05): SDNN, LFpower, HFpower, LF/HF, but these features do not have significant difference during acute physical stress (p > 0.05). Conclusions: Due to the delayed psychological stress response induced by extra neural regulation, a significant change of HRV features was found at the third minute of psychological stress stage. And the variation of these features have linear correlation with time during the remaining three to five minutes stress stage (r = −0.987, 0.996, −0.952, 0.938 for SDNN, LFpower, HFpower, LF/HF).
Ming Li; Shixiong Chen; Zhen Gao; Wanqing Wu; Lingzheng Xu. Physiological explicit of delayed psychological stress response induced by extra neural regulation. Computer Methods and Programs in Biomedicine 2020, 196, 105610 .
AMA StyleMing Li, Shixiong Chen, Zhen Gao, Wanqing Wu, Lingzheng Xu. Physiological explicit of delayed psychological stress response induced by extra neural regulation. Computer Methods and Programs in Biomedicine. 2020; 196 ():105610.
Chicago/Turabian StyleMing Li; Shixiong Chen; Zhen Gao; Wanqing Wu; Lingzheng Xu. 2020. "Physiological explicit of delayed psychological stress response induced by extra neural regulation." Computer Methods and Programs in Biomedicine 196, no. : 105610.
Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.
Yun Lu; Mingjiang Wang; Wanqing Wu; Qiquan Zhang; Yufei Han; Tasleem Kausar; Shixiong Chen; Ming Liu; Bo Wang. Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals. Complexity 2020, 2020, 1 -17.
AMA StyleYun Lu, Mingjiang Wang, Wanqing Wu, Qiquan Zhang, Yufei Han, Tasleem Kausar, Shixiong Chen, Ming Liu, Bo Wang. Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals. Complexity. 2020; 2020 ():1-17.
Chicago/Turabian StyleYun Lu; Mingjiang Wang; Wanqing Wu; Qiquan Zhang; Yufei Han; Tasleem Kausar; Shixiong Chen; Ming Liu; Bo Wang. 2020. "Entropy-Based Pattern Learning Based on Singular Spectrum Analysis Components for Assessment of Physiological Signals." Complexity 2020, no. : 1-17.
Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new approach to the segmentation of cardiac fat from Computed Tomography (CT) images. The study employs a clustering algorithm called Floor of Log (FoL). The advantage of this method is the significant drop in segmentation time. Support Vector Machine was used to learn the best FoL algorithm parameter as well as mathematical morphology techniques for noise removal. The time to segment cardiac fat on a CT is only 2.01 s on average. In contrast, literature works require more than one hour to perform segmentation. Therefore, this job is one of the fastest to segment an exam completely. The value of the Accuracy metric was 93.45% and Specificity of 95.52%. The proposed approach is automatic and requires less computational effort. With these results, the use of this approach for the segmentation of cardiac fat proves to be efficient, besides having good application times. Therefore, it has the potential to be a medical diagnostic aid tool. Consequently, it is possible to help experts achieve faster and more accurate results.
Victor Hugo C. de Albuquerque; Douglas De A. Rodrigues; Roberto F. Ivo; Solon A. Peixoto; Tao Han; Wanqing Wu; Pedro P. Rebouças Filho. Fast fully automatic heart fat segmentation in computed tomography datasets. Computerized Medical Imaging and Graphics 2019, 80, 101674 .
AMA StyleVictor Hugo C. de Albuquerque, Douglas De A. Rodrigues, Roberto F. Ivo, Solon A. Peixoto, Tao Han, Wanqing Wu, Pedro P. Rebouças Filho. Fast fully automatic heart fat segmentation in computed tomography datasets. Computerized Medical Imaging and Graphics. 2019; 80 ():101674.
Chicago/Turabian StyleVictor Hugo C. de Albuquerque; Douglas De A. Rodrigues; Roberto F. Ivo; Solon A. Peixoto; Tao Han; Wanqing Wu; Pedro P. Rebouças Filho. 2019. "Fast fully automatic heart fat segmentation in computed tomography datasets." Computerized Medical Imaging and Graphics 80, no. : 101674.
With the rapid development of science and technology, the Industrial Internet of Things (IIoT) has been improving and developing continuously since it was put forward, and the traditional industry has gradually moved towards networking and intellectualization. In the past, it was necessary to attach a radio frequency identification (RFID) label to each goods to complete the quantity monitoring, or to complete the counting by manpower. But the Radio Frequency Identification tags cannot be recycled, which will generate a lot of e-waste or increasing the cost of manpower. Therefore, in order to reduce the use of Radio Frequency Identification tags in practical applications, it is necessary to explore an innovative quantity monitoring system. We use the relationship between the quantity of goods and the digital signal to collect and analyze the data and information, and then to collect statistical data and real-time feedback information. The ultimate goal is to realize the intelligent management of goods in factory warehouse. In this paper, we propose a goods quantity monitoring system in a small warehouse. Firstly, we extract Radio Frequency (RF) signals in static and dynamic scene and preprocess them. Then, we extract the corresponding features according to different situations. Finally, we identify the quantity of goods according to K-Nearest Neighbors (KNN) classification algorithm. We have done a lot of experiments with Radio Frequency Identification equipment. The experimental results show that our system is robust and the average recognition accuracy reaches 95.53%.
Song Xu; Fu Xiao; Nana Si; Lijuan Sun; Wanqing Wu; Victor Hugo C. de Albuquerque. GQM: Autonomous goods quantity monitoring in IIoT based on battery-free RFID. Mechanical Systems and Signal Processing 2019, 136, 106411 .
AMA StyleSong Xu, Fu Xiao, Nana Si, Lijuan Sun, Wanqing Wu, Victor Hugo C. de Albuquerque. GQM: Autonomous goods quantity monitoring in IIoT based on battery-free RFID. Mechanical Systems and Signal Processing. 2019; 136 ():106411.
Chicago/Turabian StyleSong Xu; Fu Xiao; Nana Si; Lijuan Sun; Wanqing Wu; Victor Hugo C. de Albuquerque. 2019. "GQM: Autonomous goods quantity monitoring in IIoT based on battery-free RFID." Mechanical Systems and Signal Processing 136, no. : 106411.
Considering autonomous mobile robots with a variety of specific functions as a kind of service, when there are many types and quantities of services and the density of regional services is large, proposing an algorithm of Circular Area Search (CAS) because of the problem of multi-robot service scheduling in various areas. Firstly, Django is used as the web framework to build the Service-Oriented Architecture (SOA) multi-robot service cloud platform, which is the basic platform for multi-service combination. Then, the service type, the latitude and longitude and the scoring parameters of the service are selected as the service search metrics to design the CAS algorithm that based on the existing service information registered in MySQL and the Gaode Map for screening optimal service, and then providing the service applicant with the best service. Finally, the service applicant applies for the self-driving tour service as an example to perform performance simulation test on the proposed CAS algorithm. The results show that the CAS algorithm of the multi-robot service cloud platform proposed in this paper is practical compared to the global search. And compared with the Greedy Algorithm experiment, the service search time is reduced about 58% compared with the Greedy Algorithm, which verifies the efficiency of CAS algorithm.
Haibo Zhou; Jianjun Zhang; Zhenzhong Liu; Dong Nie; Wanqing Wu; Victor Hugo C. De Albuquerque. Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform. Applied Soft Computing 2019, 88, 105816 .
AMA StyleHaibo Zhou, Jianjun Zhang, Zhenzhong Liu, Dong Nie, Wanqing Wu, Victor Hugo C. De Albuquerque. Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform. Applied Soft Computing. 2019; 88 ():105816.
Chicago/Turabian StyleHaibo Zhou; Jianjun Zhang; Zhenzhong Liu; Dong Nie; Wanqing Wu; Victor Hugo C. De Albuquerque. 2019. "Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform." Applied Soft Computing 88, no. : 105816.
Emotion plays an important role in mental and physical health, decision-making, and social communication. An accurate detection of human emotions is critical to ensure effective interaction and activate proper emotional feedback. In the existing emotion recognition methods, poor generalization capability caused by individual differences in emotion experiences is still a problem. This article proposes a new framework of dynamic entropy-based pattern learning to enable subject-independent emotion recognition from electroencephalogram (EEG) signals with good generalization. Firstly, we exploit dynamic entropy measures in quantitative EEG measurement to extract consecutive entropy values from EEG signals over time. Then, based on the concatenation of consecutive entropy values to form feature vectors, the dynamic entropy-based patterning learning can be able to achieve subject-independent emotion recognition across individuals to obtain excellent identification accuracy. Experiment results show that the best average accuracy of 85.11% is reached to identify the negative and positive emotions. Besides, by comparison with the recent researches, the results have fully demonstrated that our method can achieve excellent performance for emotion recognition across individuals. In summary, an universal and subject-independent emotion recognition method with excellent generalization capability is developed by the proposed dynamic entropy-based pattern learning, which may have the great application potential to address the emotion detection in healthcare decision-making and human-computer interaction systems.
Yun Lu; Mingjiang Wang; Wanqing Wu; Yufei Han; Qiquan Zhang; Shixiong Chen. Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals. Measurement 2019, 150, 107003 .
AMA StyleYun Lu, Mingjiang Wang, Wanqing Wu, Yufei Han, Qiquan Zhang, Shixiong Chen. Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals. Measurement. 2019; 150 ():107003.
Chicago/Turabian StyleYun Lu; Mingjiang Wang; Wanqing Wu; Yufei Han; Qiquan Zhang; Shixiong Chen. 2019. "Dynamic entropy-based pattern learning to identify emotions from EEG signals across individuals." Measurement 150, no. : 107003.
This paper presents a systematic review of the current computational technologies applied to medical images for the detection, segmentation and classification of strokes. Besides, analyzing and evaluating the technological advances, the challenges to be overcome and the future trends are discussed. The principal approaches make use of artificial intelligence, digital image processing and analysis and various other technologies to develop computer aided diagnosis (CAD) systems to improve the accuracy in the diagnostic process, as well as the interpretation consistency of medical images. However, there are some points that require greater attention such as low sensitivity, optimization of the algorithm, a reduction of false positives, improvement in the identification and segmentation processes of different sizes and shapes. Also there is a need, to improve the classification steps of different stroke types and subtypes. Furthermore there is an additional need for further research to improve the current techniques and develop new algorithms to overcome disadvantages identified here. The main focus of this research is to analyze the applied technologies for the development of CAD systems and verify how effective they are for stroke detection, segmentation and classification. The main contributions of this review are that it analyzes only up-to-date studies, mainly from 2015 to 2018, as well as organizing the various studies in the area according to the research proposal, i.e., detection, segmentation and classification of the types of stroke and the respective techniques used. Thus, the review has great relevance for future research, since it presents an ample comparison of the most recent works in the area, clearly showing the existing difficulties and the models that have been proposed to overcome such difficulties.
Roger M. Sarmento; Francisco F. Ximenes Vasconcelos; Pedro P. Reboucas Filho; Wanqing Wu; Victor Hugo C. De Albuquerque. Automatic Neuroimage Processing and Analysis in Stroke—A Systematic Review. IEEE Reviews in Biomedical Engineering 2019, 13, 130 -155.
AMA StyleRoger M. Sarmento, Francisco F. Ximenes Vasconcelos, Pedro P. Reboucas Filho, Wanqing Wu, Victor Hugo C. De Albuquerque. Automatic Neuroimage Processing and Analysis in Stroke—A Systematic Review. IEEE Reviews in Biomedical Engineering. 2019; 13 (99):130-155.
Chicago/Turabian StyleRoger M. Sarmento; Francisco F. Ximenes Vasconcelos; Pedro P. Reboucas Filho; Wanqing Wu; Victor Hugo C. De Albuquerque. 2019. "Automatic Neuroimage Processing and Analysis in Stroke—A Systematic Review." IEEE Reviews in Biomedical Engineering 13, no. 99: 130-155.
In this study, active and arousal elements of emotion associated with acute stress were systematically investigated with respect to the relations between the brain activity and autonomic nervous system. In this regard, we examined the differences in short-term heart rate variability (HRV) with respect to time-frequency domain characteristics, nonlinear features, and heart rhythm patterns, when breathing volitionally in a resonant frequency (RF) respiratory with International Affective Picture Systems (IAPS) triggered negative stimulus. In this regard, a sample-based event-related functional magnetic resonance imaging (efMRI) experiments were performed to verify the dynamic changes in brain lateralisation, and 105 healthy right-handed subjects participated in the HRV study while eight of them were randomly chosen to perform small sample based efMRI test. The experimental results suggest that when experiencing negative emotions, RF-based volitional breathing is sufficient to facilitate coherence of autonomic nervous system (ANS) performance, and shifted the brain activation toward left lateralized neural activity. In combination with the previous research on cerebral correlates of emotion, this study validated the feasibility of applying HRV biofeedback in the regulation of negative emotion in healthcare settings.
Xiaofan Wang; Shengjie Li; Wanqing Wu. Effects of medical biofeedback trainings on acute stress by hybridizing heart rate variability and brain imaging. Multimedia Tools and Applications 2019, 79, 10141 -10155.
AMA StyleXiaofan Wang, Shengjie Li, Wanqing Wu. Effects of medical biofeedback trainings on acute stress by hybridizing heart rate variability and brain imaging. Multimedia Tools and Applications. 2019; 79 (15-16):10141-10155.
Chicago/Turabian StyleXiaofan Wang; Shengjie Li; Wanqing Wu. 2019. "Effects of medical biofeedback trainings on acute stress by hybridizing heart rate variability and brain imaging." Multimedia Tools and Applications 79, no. 15-16: 10141-10155.
Graspirng objects is an important capability for humanoid robots. Due to complexity of environmental and diversity of objects, it is difficult for the robot to accurately recognize and grasp multiple objects. In response to this problem, we propose a robotic grasping method that uses the deep learning method You Only Look Once v3 for multi‐target detection and the auxiliary signs to obtain target location. The method can control the movement of the robot and plan the grasping trajectory based on visual feedback information. It is verified by experiments that this method can make the humanoid robot NAO grasp the object effectively, and the success rate of grasping can reach 80% in the experimental environment.
Lei Zhang; Huayan Zhang; Hanting Yang; Gui-Bin Bian; Wanqing Wu. Multi‐target detection and grasping control for humanoid robot NAO. International Journal of Adaptive Control and Signal Processing 2019, 33, 1225 -1237.
AMA StyleLei Zhang, Huayan Zhang, Hanting Yang, Gui-Bin Bian, Wanqing Wu. Multi‐target detection and grasping control for humanoid robot NAO. International Journal of Adaptive Control and Signal Processing. 2019; 33 (7):1225-1237.
Chicago/Turabian StyleLei Zhang; Huayan Zhang; Hanting Yang; Gui-Bin Bian; Wanqing Wu. 2019. "Multi‐target detection and grasping control for humanoid robot NAO." International Journal of Adaptive Control and Signal Processing 33, no. 7: 1225-1237.
Ovarian Cancer (OC) is a type of cancer that affects ovaries in women, and is difficult to detect at initial stage resulting to increased mortality rate. The OC data generated from the Internet of Medical Things (IoMT) can be used to identify distinguish the OC. To achieve this, we utilize Self Organizing Maps (SOM) and Optimal Recurrent Neural Networks (ORNN) to classify OC. SOM algorithm was utilized for better feature subset selection and was also utilized for separating profitable, understood and intriguing data from huge measures of medical data. In addition, an optimal classifier named optimal recurrent neural network (ORNN) is also employed. The classification rate of OC detection process can be improved by optimizing the weights of RNN structure using Adaptive Harmony Search Optimization (AHSO) algorithm. A set of experimentation is carried out using the data collected from women who have a high danger of OC because of familial or individual history of cancer. The proposed method attains a maximum accuracy of 96.27 with the sensitivity and specificity rate of 85.2 respectively when compared to recurrent neural networks (RNN), feedforward neural networks (FFNN) and so on. The experimental results verified that the proposed model can be used to detect cancer at early stages with high accuracy, sensitivity, specificity and low root mean square error (RMSE).
Mohamed Elhoseny; Gui-Bin Bian; S.K. Lakshmanaprabu; K. Shankar; Amit Kumar Singh; Wanqing Wu. Effective features to classify ovarian cancer data in internet of medical things. Computer Networks 2019, 159, 147 -156.
AMA StyleMohamed Elhoseny, Gui-Bin Bian, S.K. Lakshmanaprabu, K. Shankar, Amit Kumar Singh, Wanqing Wu. Effective features to classify ovarian cancer data in internet of medical things. Computer Networks. 2019; 159 ():147-156.
Chicago/Turabian StyleMohamed Elhoseny; Gui-Bin Bian; S.K. Lakshmanaprabu; K. Shankar; Amit Kumar Singh; Wanqing Wu. 2019. "Effective features to classify ovarian cancer data in internet of medical things." Computer Networks 159, no. : 147-156.
Edge-computing plays a significant role for remote healthcare systems in recent times since hospitals adopt Internet of Medical Things (IoMT) for medical applications. One of the primary concern of edge-computing based IoMT systems includes preserving the power of medical devices, also raise the lifetime of the healthcare system. Therefore, energy efficient communication protocol is mandatory for IoMT systems. In recent times, several approaches have been developed to enhance the lifespan of IoMT, but clustering is more preferred for offering energy efficiency in medical applications. The main disadvantage of current clustering technique is that likelihood of packet failure is not considered in their communication model which causes not a reliable communication issue, also cut downs the energy of medical nodes. In this research, we are focused on developing a clustering model for medical applications (CMMA) for cluster head selection to provide effective communication for IoMT based applications. From the experimental analysis, it is revealed that the proposed CMMA has better performance than compared approaches regarding sustainability and energy utilization. Thus, it can be concluded that he proposed CMMA not only minimize the energy utilization of edge-computing based IoMT systems but it also uniformly distribute cluster heads in the network so to increase its network lifetime.
Tao Han; Lijuan Zhang; Sandeep Pirbhulal; Wanqing Wu; Victor Hugo C. de Albuquerque. A novel cluster head selection technique for edge-computing based IoMT systems. Computer Networks 2019, 158, 114 -122.
AMA StyleTao Han, Lijuan Zhang, Sandeep Pirbhulal, Wanqing Wu, Victor Hugo C. de Albuquerque. A novel cluster head selection technique for edge-computing based IoMT systems. Computer Networks. 2019; 158 ():114-122.
Chicago/Turabian StyleTao Han; Lijuan Zhang; Sandeep Pirbhulal; Wanqing Wu; Victor Hugo C. de Albuquerque. 2019. "A novel cluster head selection technique for edge-computing based IoMT systems." Computer Networks 158, no. : 114-122.
Although several studies have considered the problem of humanoid robots pushing carts, only a few have focused on the problem of robots moving heavy objects under monocular vision. This study proposes a target recognition and positioning method and a control method for a robot pushing a loaded trolley. A control system based on the humanoid robot NAO is developed and a monocular visual ranging method with segmented fitting is proposed to realize hardware control and target search and positioning for NAO. The ability of NAO to push a small cart with various weights using visual positioning is tested. The experimental results show that the average error of the monocular distance measurement method is 1.7 mm and that the target search and positioning is accurate. NAO can push a loaded cart that is 6.5 times its own weight.
Lei Zhang; Huiling Liu; Chengfang Luo; Gui‐Bin Bian; Wanqing Wu. Target recognition of indoor trolley for humanoid robot based on piecewise fitting method. International Journal of Adaptive Control and Signal Processing 2019, 33, 1319 -1327.
AMA StyleLei Zhang, Huiling Liu, Chengfang Luo, Gui‐Bin Bian, Wanqing Wu. Target recognition of indoor trolley for humanoid robot based on piecewise fitting method. International Journal of Adaptive Control and Signal Processing. 2019; 33 (8):1319-1327.
Chicago/Turabian StyleLei Zhang; Huiling Liu; Chengfang Luo; Gui‐Bin Bian; Wanqing Wu. 2019. "Target recognition of indoor trolley for humanoid robot based on piecewise fitting method." International Journal of Adaptive Control and Signal Processing 33, no. 8: 1319-1327.
Physiological signals such as electrocardiography (ECG), electromyography (EMG), and electroencephalography (EEG) could objectively reflect the functioning status of the human body and the monitoring of these signals is useful for various applications including brain computer interface, neurological rehabilitation, and long‐term healthcare monitoring. Currently, wet electrodes are commonly used for the monitoring of physiological signals and it usually requires conductive gels to achieve high quality recordings, which may cause discomfort to the patient and increase risk of skin allergy. In this study, a noncontact electrode made of a multilayer flexible printed circuit without any rigid electronic components on either side was proposed. The flexible noncontact electrode was capable of measuring physiological signals without any direct skin contact or conductive gels and could be bent freely according to the local shape to achieve optimal capacitive coupling with the skin surface. The results showed that the proposed flexible noncontact electrode could obtain different physiological signals with good quality compared with traditional wet electrodes. The ECG signals could be reliably measured with different insulation materials between the skin and the electrode, with up to five layers of insulation materials. It was also found that flexible electrode could achieve higher signal‐to‐noise ratio and therefore had better performance than traditional hard printed circuit board electrode, when measuring EMG signal through the cloth and EEG signals over the hair. The proposed method of this study might provide a novel and comfortable way to measure physiological signals for neurological rehabilitation, wearable devices, and other healthcare applications.
Shuting Liu; Mingxing Zhu; Xueyu Liu; Oluwarotimi Williams Samuel; Xin Wang; Zhen Huang; Wanqing Wu; Shixiong Chen; Guanglin Li. Flexible noncontact electrodes for comfortable monitoring of physiological signals. International Journal of Adaptive Control and Signal Processing 2019, 33, 1307 -1318.
AMA StyleShuting Liu, Mingxing Zhu, Xueyu Liu, Oluwarotimi Williams Samuel, Xin Wang, Zhen Huang, Wanqing Wu, Shixiong Chen, Guanglin Li. Flexible noncontact electrodes for comfortable monitoring of physiological signals. International Journal of Adaptive Control and Signal Processing. 2019; 33 (8):1307-1318.
Chicago/Turabian StyleShuting Liu; Mingxing Zhu; Xueyu Liu; Oluwarotimi Williams Samuel; Xin Wang; Zhen Huang; Wanqing Wu; Shixiong Chen; Guanglin Li. 2019. "Flexible noncontact electrodes for comfortable monitoring of physiological signals." International Journal of Adaptive Control and Signal Processing 33, no. 8: 1307-1318.
The Parkinson’s disease is a neurodegenerative disorder that affects around 10 million people in the world and is slightly more prevalent in males. It is characterized by the loss of neurons in a region of the brain known as substantia nigra. The neurons of this region are responsible for synthesizing the neurotransmitter dopamine, and a decrease in the production of this substance may cause motor symptoms, a characteristic of the disease. To obtain a definitive diagnosis, the patient’s medical history is analyzed and the subject submitted to a series of clinical exams. One of these exams that takes place in the clinical environment comprises asking the patient to create a series of specific drawings. Our work is based on asking the patients to draw using a software developed for this specific purpose. The drawings will then be passed through a series of image methods to reduce noises and extract the characteristics of 11 metrics of each drawing; finally, these 11 metrics will be stored. Machine learning techniques such as optimum-path forest (OPF), support vector machines (SVM), and Naive Bayes use the dataset to search and learn of the characteristics for the process of classifying individuals distributed into two classes: sick and healthy.
Lucas S. Bernardo; Angeles Quezada; Roberto Munoz; Fernanda Martins Maia; Clayton R. Pereira; Wanqing Wu; Victor Hugo C. de Albuquerque. Handwritten pattern recognition for early Parkinson’s disease diagnosis. Pattern Recognition Letters 2019, 125, 78 -84.
AMA StyleLucas S. Bernardo, Angeles Quezada, Roberto Munoz, Fernanda Martins Maia, Clayton R. Pereira, Wanqing Wu, Victor Hugo C. de Albuquerque. Handwritten pattern recognition for early Parkinson’s disease diagnosis. Pattern Recognition Letters. 2019; 125 ():78-84.
Chicago/Turabian StyleLucas S. Bernardo; Angeles Quezada; Roberto Munoz; Fernanda Martins Maia; Clayton R. Pereira; Wanqing Wu; Victor Hugo C. de Albuquerque. 2019. "Handwritten pattern recognition for early Parkinson’s disease diagnosis." Pattern Recognition Letters 125, no. : 78-84.
Techniques in ophthalmic surgery require higher precision while cataract capsulorhexis calls for enough operating space. There is no specific methods focusing on improving the safety and flexibility of Capsulorhexis operation in recent study. Traditionally, Guidance Virtual Fixture based on parameter curve failed to be flexible, while Forbidden-Regain Virtual Fixture using PD control failed to be steady enough. This paper proposes a teleoperation control scheme using integrated virtual fixture to assist in performing cataract capsulorhexis, improving operation performance in precision and flexibility. Specifically, based on the ring pipe model presented simulating the working path and permitted operating space, the control scheme is combined attractive error-reducing constraints with variety stiffness. It works by providing composite feedback force to the master-side via haptic device and letting the user aware if he on the right path and assist him in correcting operation mistakes. Experimental evaluation of the functionality of the control scheme was tested under four different configurations to verify the effectiveness of each algorithm and the combination. Significantly, the average error of operation reduces from 0.56mm to 0.33mm by 41% under the control scheme and reduces from 0.56mm to 0.11mm by 80% under the control scheme combined with motion scaling, demonstrating improvement in control by providing accurate assistance to the operator.
Weipeng Liu; Yaoguang Su; Wanqing Wu; Chen Xin; Zeng-Guang Hou; Gui-Bin Bian. An operating smooth man–machine collaboration method for cataract capsulorhexis using virtual fixture. Future Generation Computer Systems 2019, 98, 522 -529.
AMA StyleWeipeng Liu, Yaoguang Su, Wanqing Wu, Chen Xin, Zeng-Guang Hou, Gui-Bin Bian. An operating smooth man–machine collaboration method for cataract capsulorhexis using virtual fixture. Future Generation Computer Systems. 2019; 98 ():522-529.
Chicago/Turabian StyleWeipeng Liu; Yaoguang Su; Wanqing Wu; Chen Xin; Zeng-Guang Hou; Gui-Bin Bian. 2019. "An operating smooth man–machine collaboration method for cataract capsulorhexis using virtual fixture." Future Generation Computer Systems 98, no. : 522-529.
Dust accumulation on the photovoltaic (PV) panels is one of the important factors that influence the PV power generation efficiency. Autonomous robot is one of the promising way to clean the PV panels effectively, in which the absorbing flow rate to a large extent determines the cleaning performance and usage effectiveness of power supply. In this paper, the air-solid two-phase flow control equations for the dust absorbing process, the mechanical behavior of the dust particles and the relationship between the pressure distribution and the dust particle velocity are analyzed theoretically for Computational Fluid Dynamics (CFD) simulation. Orthogonal experiment method is employed to optimize the structure parameters for suction inlet of PV panel cleaning robots. The suction inlet height, inlet width, outlet height, outlet width and necking radius are simplified as 16 sets of typical test optimization problem. The fluid calculation software ANSYS Fluent is adopted to execute the simulation. The optimal result is that the inlet width is 650 mm, the inlet height is 6 mm, the outlet width is 175 mm, the outlet height is 100 mm and the necking radius is 350 mm. Experiment based on the optimized structural parameters was carried out, which implies the compatibility with the simulating result. And the average wind speed at the suction inlet is 41.8 m/s, which verifies the structure optimization.
Shibo Cai; Guanjun Bao; Xiaolong Ma; Wanqing Wu; Gui-Bin Bian; Joel J.P.C. Rodrigues; Victor Hugo C. de Albuquerque. Parameters optimization of the dust absorbing structure for photovoltaic panel cleaning robot based on orthogonal experiment method. Journal of Cleaner Production 2019, 217, 724 -731.
AMA StyleShibo Cai, Guanjun Bao, Xiaolong Ma, Wanqing Wu, Gui-Bin Bian, Joel J.P.C. Rodrigues, Victor Hugo C. de Albuquerque. Parameters optimization of the dust absorbing structure for photovoltaic panel cleaning robot based on orthogonal experiment method. Journal of Cleaner Production. 2019; 217 ():724-731.
Chicago/Turabian StyleShibo Cai; Guanjun Bao; Xiaolong Ma; Wanqing Wu; Gui-Bin Bian; Joel J.P.C. Rodrigues; Victor Hugo C. de Albuquerque. 2019. "Parameters optimization of the dust absorbing structure for photovoltaic panel cleaning robot based on orthogonal experiment method." Journal of Cleaner Production 217, no. : 724-731.
Internet of Medical Things (IoMTs) is a building block for modern healthcare having enormously stringent resource constraints thus lightweight health data security and privacy are crucial requirements. A critical issue in implementing security for the streaming health information is to offer data privacy and validation of a patient’s information over networking environment in a resource efficient manner. Therefore, we developed a biometric-based security framework for resource-constrained wearable health monitoring systems by extracting heartbeats from ECG signals. It is analyzed that time-domain based biometric features play a significant role in optimizing security in IoMT based medical applications. Moreover, resource optimization model based on utility function is proposed for clinical information transmission in IoMT. In this study, ECG signals from 40 healthy subjects were employed comprising lab environment and publicly available database i-e-physionet. The experimental results validate that proposed framework requires less processing time and energy consumption (0.0068ms and 0.196 microJoule/Byte) then Alarm-net (0.0128ms and 0.351 microJoule/Byte) and BSN-care (0.0175ms and 0.53 microJoule/Byte). Moreover, from the results, it is also observed that biometric key generation mechanism not only provide random and unique keys but it also offer a trade-off between security and resource optimization. Thus, it can be concluded that the proposed framework has got both social and economic significance for real-time healthcare applications.
Sandeep Pirbhulal; Oluwarotimi Williams Samuel; Wanqing Wu; Arun Kumar Sangaiah; Guanglin Li. A joint resource-aware and medical data security framework for wearable healthcare systems. Future Generation Computer Systems 2019, 95, 382 -391.
AMA StyleSandeep Pirbhulal, Oluwarotimi Williams Samuel, Wanqing Wu, Arun Kumar Sangaiah, Guanglin Li. A joint resource-aware and medical data security framework for wearable healthcare systems. Future Generation Computer Systems. 2019; 95 ():382-391.
Chicago/Turabian StyleSandeep Pirbhulal; Oluwarotimi Williams Samuel; Wanqing Wu; Arun Kumar Sangaiah; Guanglin Li. 2019. "A joint resource-aware and medical data security framework for wearable healthcare systems." Future Generation Computer Systems 95, no. : 382-391.
The video salient object detection (SOD) is the first step for the devices in the Internet of Things (IoT) to understand the environment around them. The video SOD needs the objects’ motion information in contiguous video frames as well as spatial contrast information from a single video frame. A large number of IoT devices’ computing power is not sufficient to support the existing SOD methods’ expensive computational complexity in emotion estimation, because they might have low hardware configurations (e.g., surveillance camera, and smartphone). In order to model the objects’ motion information efficiently for SOD, we propose an end-to-end video SOD algorithm with an efficient representation of the objects’ motion information. This algorithm contains two major parts: a 3D convolution-based X-shape structure that directly represents the motion information in successive video frames efficiently, and 2D densely connected convolutional neural networks (DenseNet) with pyramid structure to extract the rich spatial contrast information in a single video frame. Our method not only can maintain a small number of parameters as the 2D convolutional neural network but also represents spatiotemporal information uniformly that enables it can be trained end-to-end. We evaluate our proposed method on four benchmark datasets. The results show that our method achieves state-of-the-art performance compared with the other five methods.
Shizhou Dong; Zhifan Gao; Sandeep Pirbhulal; Gui-Bin Bian; Heye Zhang; Wanqing Wu; Shuo Li. IoT-based 3D convolution for video salient object detection. Neural Computing and Applications 2019, 32, 735 -746.
AMA StyleShizhou Dong, Zhifan Gao, Sandeep Pirbhulal, Gui-Bin Bian, Heye Zhang, Wanqing Wu, Shuo Li. IoT-based 3D convolution for video salient object detection. Neural Computing and Applications. 2019; 32 (3):735-746.
Chicago/Turabian StyleShizhou Dong; Zhifan Gao; Sandeep Pirbhulal; Gui-Bin Bian; Heye Zhang; Wanqing Wu; Shuo Li. 2019. "IoT-based 3D convolution for video salient object detection." Neural Computing and Applications 32, no. 3: 735-746.