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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
AMC2020 is the 16th in a series of biennial international workshops on Advanced Motion Control which aims to bring together researchers from both academia and industry and to promote omnipresent motion control technologies and applications.
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
ISIE focuses on advancements in knowledge, new methods, and technologies relevant to industrial electronics, along with their applications and future developments.
Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.
IEEE Antennas and Wireless Propagation Letters (AWP Letters) will be devoted to the rapid electronic publication of short manuscripts in the technical areas of Antennas and Wireless Propagation.
The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
Electro International, 1991, 1991
2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018
Permanent facial paralysis and paresis (FP) results from damage to the facial nerve (FN), and is a debilitating condition with substantial functional and psychological consequences for the patient. Unfortunately, surgeons have few tools with which they can satisfactorily reanimate the face. Current strategies employ static (e.g., implantation of nonmuscular material in the face to aid in function/cosmesis) and dynamic options ...
IEEE Transactions on Affective Computing, 2014
In this paper we present the design of a wearable device that reads positive facial expressions using physiological signals. We first analyze facial morphology in 3 dimensions and facial electromyographic signals on different facial locations and show that we can detect electromyographic signals with high amplitude on areas of low facial mobility on the side of the face, which are ...
2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, 2015
Facial expression recognition has broad application prospects in the fields of psychological study, nursing care, Human Computer Interaction as well as affective computing. The method with surface Electromyogram (sEMG), which is one of vital bio-signals, has its superiority in several aspects such as high temporal resolution and data processing efficiency over other methods. Researches regarding EMG signal to study emotional ...
2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, 2010
This paper presents a silent-speech interface based on electromyographic (EMG) signals recorded in the facial muscles. The distinctive feature of this system is that it is based on the recognition of syllables instead of phonemes or words, which is a compromise between both approaches with advantages as (a) clear delimitation and identification inside a word, and (b) reduced set of ...
Permanent facial paralysis and paresis (FP) results from damage to the facial nerve (FN), and is a debilitating condition with substantial functional and psychological consequences for the patient. Unfortunately, surgeons have few tools with which they can satisfactorily reanimate the face. Current strategies employ static (e.g., implantation of nonmuscular material in the face to aid in function/cosmesis) and dynamic options (e.g., gracilis myoneurovascular free tissue transfer) to partially restore volitional facial function and cosmesis. Here, we propose a novel neuroprosthetic approach for facial reanimation that utilizes electromyographic (EMG) input coupled to a chronically implanted multichannel cuff electrode (MCE) to restore instantaneous, volitional, and selective hemifacial movement in a feline model. To accomplish this goal, we developed a single-channel EMG-drive current source coupled with a chronically implanted MCE via a portable microprocessor board. Our results demonstrated a successful feasibility trial in which human EMG input resulted in FN stimulation with subsequent concentric contraction of discrete regions of a feline face.
In this paper we present the design of a wearable device that reads positive facial expressions using physiological signals. We first analyze facial morphology in 3 dimensions and facial electromyographic signals on different facial locations and show that we can detect electromyographic signals with high amplitude on areas of low facial mobility on the side of the face, which are correlated to ones obtained from electrodes on traditional surface electromyographic capturing positions on top of facial muscles on the front of the face. We use a multi-attribute decision-making method to find adequate electrode positions on the side of face to capture these signals. Based on this analysis, we design and implement an ergonomic wearable device with high reliability. Because the signals are recorded distally, the proposed device uses independent component analysis and an artificial neural network to analyze them and achieve a high facial expression recognition rate on the side of the face. The recognized emotional facial expressions through the wearable interface device can be recorded during therapeutic interventions and for long-term facial expression recognition to quantify and infer the user's affective state in order to support medical professionals.
Facial expression recognition has broad application prospects in the fields of psychological study, nursing care, Human Computer Interaction as well as affective computing. The method with surface Electromyogram (sEMG), which is one of vital bio-signals, has its superiority in several aspects such as high temporal resolution and data processing efficiency over other methods. Researches regarding EMG signal to study emotional expression have started since the second half of last century. Meanwhile, studies on myoelectrical control systems focusing on the computation of bio-signal processing and data analysis have been blooming in the recent twenty years. To have a comprehensive view of utilizing facial sEMG method, a systematic review is presented in this paper for facial expression recognition from experiment design to measurement systems, and data analysis steps.
This paper presents a silent-speech interface based on electromyographic (EMG) signals recorded in the facial muscles. The distinctive feature of this system is that it is based on the recognition of syllables instead of phonemes or words, which is a compromise between both approaches with advantages as (a) clear delimitation and identification inside a word, and (b) reduced set of classification groups. This system transforms the EMG signals into robust-in- time feature vectors and uses them to train a boosting classifier. Experimental results demonstrated the effectiveness of our approach in three subjects, providing a mean classification rate of almost 70% (among 30 syllables).
This chapter contains sections titled: * Self-Understanding Microskills Defined * Contextual Integration of Self-Understanding
This chapter outlines the main components that should be considered in the development of physiologically based models of surface electromyographic (sEMG). One of the challenges in increasing the accuracy of current sEMG models is the correct identification of the model parameters. Parameter identification is particularly difficult for systems such as this in which it is not possible to accurately measure all of the physiological, anatomical, and physical properties of the system. The chapter explores the simulation of intramuscular EMG, in particular for clinical applications. One of the primary purposes of a modeling approach is to identify the mechanisms responsible for experimental observations. A successful EMG model can thereby help the researcher to relate the recorded electrical signal to the underlying processes associated with muscle contraction. Model validation remains one of the most challenging areas in EMG modeling.
Spatial-temporal relations among facial muscles carry crucial information about facial expressions yet have not been thoroughly exploited. One contributing factor for this is the limited ability of the current dynamic models in capturing complex spatial and temporal relations. Existing dynamic models can only capture simple local temporal relations among sequential events, or lack the ability for incorporating uncertainties. To overcome these limitations and take full advantage of the spatio-temporal information, we propose to model the facial expression as a complex activity that consists of temporally overlapping or sequential primitive facial events. We further propose the Interval Temporal Bayesian Network to capture these complex temporal relations among primitive facial events for facial expression modeling and recognition. Experimental results on benchmark databases demonstrate the feasibility of the proposed approach in recognizing facial expressions based purely on spatio-temporal relations among facial muscles, as well as its advantage over the existing methods.
Now a day, people are getting intensely attached to communicate with others in virtual environments using various virtual agents due to their popularity. Most of these environments used emoticon for displaying emotion of interacting partners to each other. However, emoticon based systems often makes the interaction lifeless and monotonous. This paper focus on developing a human- like virtual agent that produces facial expressions with motions (such as head movement and eye blinks) during human-computer interaction scenarios through analyzing the input text messages of users. This agent is capable to display six facial expressions namely, happy, sad, angry, disgust, fear, and surprise based on the chatting partner's input text that makes the interaction enjoyable. Experimental evaluation reveals that the proposed agent can display emotive expressions correctly 92% of the times from the users' text input and it creates a better feeling of making interaction to the partners than the emoticon bases systems.
Facial gesture recognition has become an important issue in diagnostic, medical and industrial fields. Automatic recognition of facial gestures could be considered as an important factor in human-machine interface applications. Facial gesture recognition based on surface electromyography (SEMG) has been well thought-out in the recent decade. SEMG has accurate rates for facial gesture recognition since it records the electrical potential from facial muscles. This paper presents a method for recognizing 5 different facial gestures based on forehead two-channels bioelectric-signals. The recorded signals were processed in four steps: filtration, feature extraction (RMS), thresholding, and classification. The extracted features were classified into 5 facial gesture classes (rest, smile, frown, rage, and gesturing `notch' by pulling up the eyebrows) by utilizing Fuzzy C-Means (FCM) classifier. Finally 90.8% recognition ratio has been achieved by applying our method on 4 subjects.
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