Fillia Makedon

University of Texas at Arlington, Arlington, Texas, United States

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Publications (252)121.11 Total impact

  • Shawn N. Gieser · Angie Boisselle · Fillia Makedon ·
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    ABSTRACT: Cerebral Palsy is a motor disability that occurs in early childhood. Conventional therapy methods have proven useful for upper extremity rehabilitation, but can lead to non-compliance due to children getting bored with the repetition of exercises. Virtual reality and game-like simulations of conventional methods have proven to lead to higher rates of compliance, the patient being more engaged during exercising, and yield better performance during exercises. Most games are good at keeping players engaged, but does not focus on exercising fine motor control functions. In this paper, we present an analysis of classification techniques for static hand gestures. We also present a prototype of a game-like simulation of matching static hand gestures in order to increase motor control of the hand.
    DIGITAL HUMAN MODELING. APPLICATIONS IN HEALTH, SAFETY, ERGONOMICS AND RISK MANAGEMENT: ERGONOMICS AND HEALTH 2015, Edited by Vincent G. Duffy, 07/2015: chapter Real-Time Static Gesture Recognition for Upper Extremity Rehabilitation Using the Leap Motion: pages 144-154; Springer., ISBN: 9783319210704
  • Alexandros Papangelis · Fillia Makedon · Robert Gatchel ·
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    ABSTRACT: We present an Adaptive Dialogue System, able to make conversation in natural language with post-traumatic stress disorder (PTSD)-suffering users, and guide the way that allows eliciting information about their disorder and progress of their treatment. As the conversation unfolds, the system gathers information enabling us to calculate a PTSD score and perform an initial screening of the users. We generate the system's response in real time and continuously monitor an estimate of the user's emotional state. If a user becomes frustrated, angry, sad, etc., we provide encouragement and observe any changes in the dialogue. The system will be trained with data from real and simulated interactions, using automatic virtual agents. Last, we present our plans for evaluation with PTSD-suffering and non-PTSD-suffering users.
    Journal of Applied Biobehavioral Research 09/2014; 19(3). DOI:10.1111/jabr.12025
  • Zikos D · Tsiakas K · Qudah F · Athitsos V · Makedon F ·
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    ABSTRACT: In this paper, we investigate the performance of a series of classification methods for the prediction of the hospital Length of Stay (LOS), based on two temporally sequential clinical scenarios. We used a 2012 Medicare Provider Analysis and Review (MedPar) dataset, which contains records of Medicare beneficiaries who used inpatient hospital services. Our subset included 300,000 randomly selected cases. During the prepossessing we added new features and linked our data with external datasets, using common key identifiers. In the first scenario our goal was to predict the LOS using a subset of information which is readily available to the clinician upon the patient admission, while the second scenario assumes that there is available additional data (information on the patient diagnosis and clinical procedures). For our experiments we used three different classifiers: Naïve Bayes, AdaBoost and C4.5 Decision tree, for two different LOS cut-off points (4 day and 12 day hospital stay). The overall performance of our classifiers was ranging from fair to very good. On the other hand the true positive rate, that is the correct classification of the long hospital stays, was low, with an exception of Naïve Bayes, which demonstrated significantly better performance in the second scenario. Our results indicate that Naïve Bayes may be used for the prediction of the in-hospital LOS. Our analysis also indicates that the MedPar data combined with other data resources has the potential to provide a good basis for robust prediction analytics in hospitals.
    7th International Conference on Pervasive Technologies related to Assistive Environments, Rhodes Island, Greece; 05/2014
  • Hua Wang · Heng Huang · Fillia Makedon ·
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    ABSTRACT: Human emotion detection is of substantial importance in a variety of pervasive applications in assistive environments. Because facial expressions provide a key mechanism for understanding and conveying emotion, automatic emotion detection through facial expression recognition has attracted increased attention in both scientific research and practical applications in recent years. Traditional facial expression recognition methods normally use only one type of facial expression data, either static data extracted from one single face image or motion-dependent data obtained from dynamic face image sequences, but seldom employ both. This work proposes to place the emotion detection problem under the framework of Discriminant Laplacian Embedding (DLE) to integrate these two types of facial expression data in a shared subspace, such that the advantages of both of them are exploited. Due to the reinforcement between the two types of facial features, the new data representation is more discriminative and easier to classify. Encouraging experimental results in empirical studies demonstrate the practical usage of the proposed DLE method for emotion detection.
    Universal Access in the Information Society 03/2014; 13(1). DOI:10.1007/s10209-013-0312-5 · 0.48 Impact Factor
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    Universal Access in the Information Society 03/2014; 13(1). DOI:10.1007/s10209-013-0311-6 · 0.48 Impact Factor

  • HCI International, Heraklion, Greece; 01/2014
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    ABSTRACT: The monitoring of sleep patterns is of major importance for various reasons such as the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality, and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a combination of non-invasive sensors to assess and report sleep patterns: a contact-based pressure mattress and a non-contact 3D image acquisition device, which can complement each other. To evaluate our system, we used real data collected in Heracleia Lab’s assistive living apartment. Our system uses Machine Learning techniques to automatically analyze the collected data and recognize sleep patterns. It is non-invasive, as it does not disrupt the user’s usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.
    Personal and Ubiquitous Computing 01/2014; 18(1). DOI:10.1007/s00779-012-0623-1 · 1.52 Impact Factor
  • Shawn N. Gieser · Vangelis Metsis · Fillia Makedon ·

    the 7th International Conference; 01/2014
  • Hua Wang · Heng Huang · Monica Basco · Molly Lopez · Fillia Makedon ·
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    ABSTRACT: Automatic patient thought record categorization (TR) is important in cognitive behavior therapy, which is an useful augmentation of standard clinic treatment for major depressive disorder. Because both collecting and labeling TR data are expensive, it is usually cost prohibitive to require a large amount of TR data, as well as their corresponding category labels, to train a classification model with high classification accuracy. Because in practice we only have very limited amount of labeled and unlabeled training TR data, traditional semi-supervised learning methods and transfer learning methods, which are the most commonly used strategies to deal with the lack of training data in statistical learning, cannot work well in the task of automatic TR categorization. To address this challenge, we propose to tackle the TR categorization problem from a new perspective via self-taught learning, an emerging technique in machine learning. Self-taught learning is a special type of transfer learning. Instead of requiring labeled data from an auxiliary domain that are relevant to the classification task of interest as in traditional transfer learning methods, it learns the inherent structures of the auxiliary data and does not require their labels. As a result, a classifier achieves decent classification accuracy using the limited amount of labeled TR texts, with the assistance from the large amount of text data obtained from some inexpensive, or even no-cost, resources. That is, a cost-effective TR categorization system can be built that may be particularly useful for diagnosis of patients and training of new therapists. By further taking into account the discrete nature input text data, instead of using the traditional Gaussian sparse coding in self-taught learning, we use exponential family sparse coding to better simulate the distribution of the input data. We apply the proposed method to the task of classifying patient homework texts. Experimental results show the effectiveness of the proposed automatic TR classification framework.
    Personal and Ubiquitous Computing 01/2014; 18(1). DOI:10.1007/s00779-012-0614-2 · 1.52 Impact Factor
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    ABSTRACT: The special issue of the journal of Pervasive Ubiquitous Computing (PUC) focused on the successful organization of the 3rd and 4th International Conferences on 'PErvasive Technologies Related to Assistive Environments (PETRA 2010 and 2011)'. The PETRA 2010 and 2011 were successfully held in Corfu, Greece and Herakleion, Crete, Greece. These two conferences received funding from the US National Science Foundation along with support from the Hellenic National Center for Scientific Research-Demokritos, and many other organizations. The focus of the special issue was how and what new technological innovations could assist and empower an elder or disabled human in everyday life. It focused on research involving the design, development, evaluation and use of emerging pervasive technologies for assistive environments for the disabled and the senior citizen, taking into account the target audience of the journal.
    Personal and Ubiquitous Computing 01/2014; 18(1). DOI:10.1007/s00779-012-0616-0 · 1.52 Impact Factor
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    Vangelis Metsis · Fillia Makedon · Dinggang Shen · Heng Huang ·
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    ABSTRACT: Array comparative genomic hybridization (aCGH) is a newly introduced method for the detection of copy number abnormalities associated with human diseases with special focus on cancer. Specific patterns in DNA copy number variations (CNVs) can be associated with certain disease types and can facilitate prognosis and progress monitoring of the disease. Machine learning techniques have been used to model the problem of tissue typing as a classification problem. Feature selection is an important part of the classification process, because many biological features are not related to the diseases and confuse the classification tasks. Multiple feature selection methods have been proposed in the different domains where classification has been applied. In this work, we will present a new feature selection method based on structured sparsity-inducing norms to identify the informative aCGH biomarkers which can help us classify different disease subtypes. To validate the performance of the proposed method, we experimentally compare it with existing feature selection methods on four publicly available aCGH data sets. In all empirical results, the proposed sparse learning based feature selection method consistently outperforms other related approaches. More important, we carefully investigate the aCGH biomarkers selected by our method, and the biological evidences in literature strongly support our results.
    IEEE/ACM Transactions on Computational Biology and Bioinformatics 01/2014; 11(1):168-181. DOI:10.1109/TCBB.2013.141 · 1.44 Impact Factor
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    ABSTRACT: This paper presents a new, publicly available dataset, aimed to be used as a benchmark for Point of Gaze (PoG) detection algorithms. The dataset consists of two modalities that can be combined for PoG definition: (a) a set of videos recording the eye motion of human participants as they were looking at, or following, a set of predefined points of interest on a computer visual display unit (b) a sequence of 3D head poses synchronized with the video. The eye motion was recorded using a Mobile Eye-XG, head mounted, infrared monocular camera and the head position by using a set of Vicon motion capture cameras. The ground truth of the point of gaze and head location and direction in the three-dimensional space are provided together with the data. The ground truth regarding the point of gaze is known in advance since the participants are always looking at predefined targets on a monitor.
    Journal on Multimodal User Interfaces 11/2013; 7(3). DOI:10.1007/s12193-013-0121-4 · 0.80 Impact Factor
  • Georgios Galatas · Gerasimos Potamianos · Fillia Makedon ·
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    ABSTRACT: We investigate the performance of our audio-visual speech recognition system in both English and Greek under the influence of audio noise. We present the architecture of our recently built system that utilizes information from three streams including 3-D distance measurements. The feature extraction approach used is based on the discrete cosine transform and linear discriminant analysis. Data fusion is employed using state-synchronous hidden Markov models. Our experiments were conducted on our recently collected database under a multi-speaker configuration and resulted in higher performance and robustness in comparison to an audio-only recognizer.
    Proceedings of the 15th international conference on Human-Computer Interaction: interaction modalities and techniques - Volume Part IV; 07/2013
  • Georgios Galatas · Alexandros Papangelis · Fillia Makedon ·

    International Conference on Multimedia and Human-Computer Interaction; 07/2013
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    Dimitrios Zikos · George Galatas · Vangelis Metsis · Fillia Makedon ·
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    ABSTRACT: In this paper we describe CABROnto, which is a web ontology for the semantic representation of the computer assisted brain trauma rehabilitation. This is a novel and emerging domain, since it employs the use of robotic devices, adaptation software and machine learning to facilitate interactive and adaptive rehabilitation care. We used Protégé 4.2 and Protégé-Owl schema editor. The primary goal of this ontology is to enable the reuse of the domain knowledge. CABROnto has nine main classes, more than 50 subclasses, existential and cardinality restrictions. The ontology can be found online at Bioportal.
    Studies in health technology and informatics 07/2013; 190:100-102. DOI:10.3233/978-1-61499-276-9-100
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    Michael Gardner · Vangelis Metsis · Eric Becker · Fillia Makedon ·
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    ABSTRACT: Cerebral Palsy (CP) is a central nervous system disorder affecting 2 out of every 1000 births. CP limits a person's muscular function. Rehabilitative, touch-screen gaming promises to assist in developing muscle tone and dexterity for hemi-paretic CP patients as well as help therapists keep track of a patient's performance over time. However, most systems fail to take into account other factors of the disorder that could affect the scoring of a patient differently on various assessment days - specifically attention deficit or distraction. Cerebral Palsy is frequently associated with diagnosed Attention Deficit (Hyperactive) Disorder (ADD/ADHD), but even if a child does not fall within that definition, pediatric CP patients are often easily distracted. In this work we perform attention deficit simulation experiments with able-bodied users playing three rehabilitative games as well as similar computer generated data and propose a methodology to model and eliminate the effects of attention deficit or distraction from the scoring scheme used to evaluate the patient's motor abilities and progress over time.
    Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments; 05/2013
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    Georgios Galatas · Dimitrios Zikos · Fillia Makedon ·
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    ABSTRACT: In this paper, we describe a novel methodology which employs machine learning as an alternative means to explore hospital characteristics and client satisfaction, for decision making and improved quality of care. We applied well known feature selection and data mining algorithms such as forward selection and Naïve Bayes respectively, to determine patient satisfaction, which is an important indicator of quality of care in hospital settings. Our dataset comprised of three types of data, (i) patient perception about received care, (ii) nurse perception about the working environment and (iii) organizational attributes of the hospital. Our experimental results exhibited high classification accuracy (87%), allowing valid conclusions to be reached about the organizational and workforce factors which attribute to patient satisfaction. Our findings were validated using traditional statistical methods such as binomial correlation and linear regression.
    Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments; 05/2013
  • Shawn N. Gieser · Eric Becker · Fillia Makedon ·
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    ABSTRACT: Rheumatoid Arthritis is a chronic disease that leads to swelling and inflammation of the joints and even spread to surrounding tissues and blood vessels. Physical therapy has been used successfully to slow the effects of this degenerative disease. Patients, however, do not want to do these exercises due to the fact they are boring and repetitive. In this paper, we introduce the first steps in creating a virtual environment using a CAVE System for the physical therapy sessions where the user will be engaged and motived to complete the exercises prescribed by his or her doctor.
    Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments; 05/2013

  • 3rd Robotics in Assistive Environments; 05/2013
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    ABSTRACT: In this paper, we present an ongoing effort to develop a robust omnidirectional robotic platform for outdoor operation on non-smooth surfaces. The design of an off-road, low-cost omniwheel is presented along with a suspension system that will allow the platform to traverse rough terrain. We also provide a control architecture based on the open-source Robotic Operating System (ROS).

Publication Stats

2k Citations
121.11 Total Impact Points


  • 2007-2014
    • University of Texas at Arlington
      • Department of Computer Sciences & Engineering
      Arlington, Texas, United States
  • 1991-2006
    • Dartmouth College
      • Department of Computer Science
      Hanover, New Hampshire, United States
  • 2004
    • Dartmouth–Hitchcock Medical Center
      LEB, New Hampshire, United States
  • 1990-1993
    • Massachusetts Institute of Technology
      • Laboratory for Computer Science
      Cambridge, Massachusetts, United States
  • 1989-1991
    • University of Texas at Dallas
      • Department of Computer Science
      Richardson, Texas, United States
  • 1982
    • Illinois Institute of Technology
      Chicago, Illinois, United States
  • 1970
    • Université de Bretagne Sud
      Lorient, Brittany, France