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Dive into the research topics where Richard Brower is active.

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Featured researches published by Richard Brower.


Engineering Applications of Artificial Intelligence | 2011

Application of wearable sensors for human gait analysis using fuzzy computational algorithm

Murad Alaqtash; Huiying Yu; Richard Brower; Amr Abdelgawad; Thompson Sarkodie-Gyan

Abstract The authors have developed and tested a wearable inertial sensor system for the acquisition of gait features. The sensors were placed on anatomical segments of the lower limb: foot, shank, thigh, and hip, and the motion data were then captured in conjunction with 3D ground reaction forces (GRFs). The method of relational matrix was applied to develop a rule-based system, an intelligent fuzzy computational algorithm. The rule-based system provides a feature matrix model representing the strength of association or interaction amongst the elements of the gait functions (limb-segments accelerations and GRFs) throughout the gait cycle. A comparison between the reference rule-based data and an input test data was evaluated using a fuzzy similarity algorithm. This system was tested and evaluated using two subject groups: 10 healthy subjects were recruited to establish the reference fuzzy rule-base, and 4 relapsing remitting multiple sclerosis subjects were used as an input test data; and the grade of similarity between them was evaluated. This similarity provides a quantitative assessment of mobility state of the impaired subject. This algorithmic tool may be helpful to the clinician in the identification of pathological gait impairments, prescribe treatment, and assess the improvements in response to therapeutic intervention.


international conference of the ieee engineering in medicine and biology society | 2011

Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms

Murad Alaqtash; Thompson Sarkodie-Gyan; Huiying Yu; Olac Fuentes; Richard Brower; Amr Abdelgawad

An automated gait classification method is developed in this study, which can be applied to analysis and to classify pathological gait patterns using 3D ground reaction force (GRFs) data. The study involved the discrimination of gait patterns of healthy, cerebral palsy (CP) and multiple sclerosis subjects. The acquired 3D GRFs data were categorized into three groups. Two different algorithms were used to extract the gait features; the GRFs parameters and the discrete wavelet transform (DWT), respectively. Nearest neighbor classifier (NNC) and artificial neural networks (ANN) were also investigated for the classification of gait features in this study. Furthermore, different feature sets were formed using a combination of the 3D GRFs components (mediolateral, anterioposterior, and vertical) and their various impacts on the acquired results were evaluated. The best leave-one-out (LOO) classification accuracy 85% was achieved. The results showed some improvement through the application of a features selection algorithm based on M-shaped value of vertical force and the statistical test ANOVA of mediolateral and anterioposterior forces. The optimal feature set of six features enhanced the accuracy to 95%. This work can provide an automated gait classification tool that may be useful to the clinician in the diagnosis and identification of pathological gait impairments.


ieee international conference on rehabilitation robotics | 2007

Determination of Human Gait Phase Using Fuzzy Inference

Chad MacDonald; Darla R. Smith; Richard Brower; Martine Ceberio; Thompson Sarkodie-Gyan

This paper discusses the design and implementation of a fuzzy inference system for the recognition of human gait phases. In particular, this work focuses on using the angles of the joints of lower limb to determine the current stage of a subjects gait cycle. The fuzzy rule-based system was developed using typical joint angle trajectories over a single gait cycle. The behavior of each joint was examined to determine appropriate rules for differentiating between gait phases. The completed system was then tested using joint angle trajectories measured from healthy human test subjects and shown to be capable of reproducing the gait phase transitions found by a human expert.


ieee international conference on rehabilitation robotics | 2009

Gait Variability while walking with three different speeds

Huiying Yu; Jody Riskowski; Richard Brower; Thompson Sarkodie-Gyan

Gait Variability is defined as changes in gait parameters from one stride to the next. Gait variability increases in individuals affected by neurodegenerative conditions such as Parkinsons disease and Huntington disease, and also with falls in the elderly and incident mobility disability. In this work, we study speed-related and age-related gait variabilities in healthy adults. Ten participants, five females (three young and two middle-aged) and five males (three young and two middle-aged) were recruited to walk on an instrumented treadmill for three minutes in this study. The gait variables (stride length, stride width, stride time and stride velocity) were extracted and processed from camera motion system. Results: slow speed walking increased gait variability with all gait variables; only stride width variability was increased significantly in middle aged subjects compared with the young subjects (p≪0.05), there were no changes in other variables. Based on gait variability differences in age, height and body mass, we propose to design a knowledge-base membership function of gait variability for all the related gait variables with different heights and weights (BMI) as cofactors in each age group. An automatic diagnostic tool, Fuzzy Inferential Reasoning system, will help the clinician to identify pathological impairment from normal.


ieee international conference on rehabilitation robotics | 2007

Identification of Human Gait using Fuzzy Inferential Reasoning

Huiying Yu; Patricia Nava; Richard Brower; Martine Ceberio; Thompson Sarkodie-Gyan

The restoration of healthy locomotion (gait) after stroke, traumatic brain injury, and spinal cord injury, is a major task in neurological rehabilitation. The rehabilitation process is labor intensive. Patient evaluation is often subjective, foiling determination of precise rehabilitation goals and assessment of treatment effects. To date it is the experienced clinician who continues to perform functional gait assessment and training in the absence of virtually any technological assistance. This paper introduces an algorithm capable of identifying human gait patterns. The fuzzy inferential reasoning uses typical joint angle trajectories to identify varying gait patterns. The algorithm will, thus, offer doctors, therapists, and patients a significant tool to assess the efficacy and outcomes of medical rehabilitation therapies and practices.


robotics and biomimetics | 2009

Recognition and decision-making algorithm in human locomotion based on the principles of fuzzy reasoning

Thompson Sarkodie-Gyan; Huiying Yu; Murad Alaqtash; Eric Spier; Richard Brower

The authors introduce a fuzzy rule-based algorithm to evaluate the activation patterns of muscles of the lower limb with respect to the gait phases during normal human walking. A relational matrix was established as a Cartesian product between the activation behaviors of muscles of the lower limb within the seven gait phases during normal walking. This relational matrix is an expression of the strength of association between the muscles and the gait phases. The resulting knowledge-base, therefore, depicts the relationship between the muscles in the respective gait phases during normal walking tasks. The cross-correlation between an input relational matrix and the knowledge base will provide a diagnostic assessment of the neurological state of the subject.


Medical science educator | 2013

A Laboratory for Education in Molecular Medicine: a Dedicated Resource for Medical Student Research

Curt M. Pfarr; Debra E. Bramblett; David Lee Osborne; Amy Trott; Heather Balsiger; Martine Coue; Richard Brower; Tanis Hogg

The Paul L. Foster School of Medicine in El Paso, Texas seated its inaugural class in 2009 and introduced a highly-integrated pre-clinical curriculum that provides our students with a solid introduction to the scientific principles of medicine, medical skills, early clinical experiences, ethics and professionalism. To further enhance their undergraduate training, all students additionally complete a scholarly concentration requirement called the Scholarly Activity and Research Program (SARP). Students can choose a wide variety of topics for this faculty-mentored activity; however, about two-thirds of the students choose projects relating to basic, clinical or translational research. To broaden the on-campus opportunities for students in these areas we have developed a research laboratory, called the Laboratory for Education in Molecular Medicine (LEMM), that is fully-dedicated for mentored SARP projects. This ‘community’ laboratory is housed in the Department of Medical Education and represents a unique model for the establishment and development of viable research projects. We discuss the evolution of the LEMM, its current organization and the challenges and opportunities in maintaining and growing this valuable resource.


international conference on intelligent robotics and applications | 2010

Application of wearable miniature non-invasive sensory system in human locomotion using soft computing algorithm

Murad Alaqtash; Huiying Yu; Richard Brower; Amr Abdelgawad; Eric Spier; Thompson Sarkodie-Gyan

The authors have designed and tested a wearable miniature noninvasive sensory system for the acquisition of gait features. The sensors are placed on anatomical segments of the lower limb, and motion data was then acquired in conjunction with electromyography (EMG) for muscle activities, and instrumented treadmill for ground reaction forces (GRF). A relational matrix was established between the limb-segment accelerations and the gait phases. A further relational matrix was established between the EMG data and the gait phases. With these pieces of information, a fuzzy rule-based system was established. This rule-based system depicts the strength of association or interaction between limb-segments accelerations, EMG, and gait phases. The outcome of measurements between the rule-based data and the randomized input data were evaluated using a fuzzy similarity algorithm. This algorithm offers the possibility to perform functional comparisons using different sources of information. It can provide a quantitative assessment of gait function.


Amyotrophic Lateral Sclerosis | 2003

Sixteen novel mutations in the Cu/Zn superoxide dismutase gene in amyotrophic lateral sclerosis: a decade of discoveries, defects and disputes

Peter Andersen; Katherine B. Sims; Winnie Xin; Rosemary Kiely; Gilmore O'Neill; John Ravits; Erik P. Pioro; Yadollah Harati; Richard Brower; Johanan S. Levine; Hedvika U. Heinicke; William Seltzer; Michael A. Boss; Robert H. Brown


Engineering | 2012

Case Study on Assessment of Mild Traumatic Brain Injury Using Granular Computing

Melaku A. Bogale; Huiying Yu; Thompson Sarkodie-Gyan; Murad Alaqtash; James Moody; Richard Brower

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Thompson Sarkodie-Gyan

University of Texas at El Paso

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Huiying Yu

University of Texas at El Paso

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Murad Alaqtash

University of Texas at El Paso

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Amr Abdelgawad

Texas Tech University Health Sciences Center

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Martine Ceberio

University of Texas at El Paso

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Amy Trott

Texas Tech University Health Sciences Center

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Chad MacDonald

University of Texas at El Paso

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Curt M. Pfarr

Texas Tech University Health Sciences Center

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Darla R. Smith

University of Texas at El Paso

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David Lee Osborne

Texas Tech University Health Sciences Center

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