
NRI: Considerate Co-robot Intelligence through Ubiquitous Human State Awareness, National Science Foundation, National Robotics Initiative (NRI), 2014-2017, PI: Weihua Sheng and Co-PI: Guoliang Fan. Summary: The objective of this project is to develop a new theoretical/algorithmic framework and an open hardware/software platform for considerate co-robot intelligence, enabling a co-robot to assist humans in their daily lives in a proactive way while still having the freedom to do its routine work. Such considerate intelligence is developed through ubiquitous human state awareness-knowing human’s activity and location in an indoor environment without constantly following and watching the human. This capability is realized through wearable sensing and computing from a human-based perspective. The major research efforts consist of four parts: co-robot semantic mapping through human-environment interaction; human activity and location inference using minimal motion sensor data; activity prediction and behavioral anomaly detection based on human state awareness; experimental evaluation using open hardware/software platforms and a case study evaluating the effectiveness of considerate co-robot intelligence in elderly fall prevention, detection and intervention.
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A Tool for Posture Assessment and Personalized Training, OCAST, Health Research Program, 2012-2015, PI: Guoliang Fan. Summary: Virtually all adults have experienced one or more brief episodes of musculoskeletal pain associated with injury, overuse, and more often, poor posture and gait. Recurrent or chronic musculoskeletal pain problems are also common (10-40%). The estimated total annual cost of productive time lost due to back pain, arthritis and other musculoskeletal pain problems was US$ 41.7 billion in 2002. Dynamic posture/gait assessment and personalized training/rehabilitation are the best remedies for detecting the early sign of poor posture/gait, preventing the deterioration of various musculoskeletal pains and improving posture and gait through targeted intervention. However, existing clinic tools are mainly used for static posture assessment or qualitative gait analysis, and have limited capability for dynamic and quantitative posture/gait assessment that is more useful a nd informative for diagnosis, treatment and prevention. This project seeks an integrated multi-sensor approach to develop a comprehensive yet economic clinical tool for dynamic posture/gait assessment, which is expected to surpass all existing ones by providing accurate and quantitative functional motion analysis as well as targeted intervention and personalized training via serious health games.
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Vision-based Clinical Markerless Gait Stability Analysis, OCAST, Health Research Program, 2009-2012, PI: Guoliang Fan, Collaborator: Dr. Li-Shan Chou, University of Oregon.
Summary: Falls among the elderly population are prevalent, dangerous, and costly. Early fall-risk detection would greatly enhance our ability to design interventions for preventing fall injuries in community-dwelling elderly. This project seeks a vision-based prototype system that is able to assess gait instability in clinical settings with normal video cameras. This project seeks an integrated vision-based approach for gait instability analysis, and our goal is to promote the feasibility and applicability of markerless motion capture for detecting early fall-risk in the elderly in an uncontrolled environment. The fundamental assumption is that a new human gait motion can be extrapolated from a set of representative human motions with sufficient accuracy and
robustness for gait in stability assessment. The long term goal will be a feasible solution to clinical markerless gait stability analysis that has tremendous potential value for monitoring safe ambulation and recognizing accidental falls in a video surveillance environment.
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Video-based Assessment of Crew Vibration during Rocket Launch,
Oklahoma NASA EPSCoR, 2009, PI: Guoliang Fan.
Summary: This project aims to develop a practical prototype system for video-based human motion analysis that is used for crew vibration assessment during rocket launch. This topic emerges as an important issue when NASA
is planning to bring a new crew exploration vehicle into serve after shuttle retirement for which the new rocket lunch system has to be much more powerful and stronger, and consequentially could be more dangerous to the safety of crew due to the significant vibration created during rocket lunch. It is critical to understand the effects of such vibrations on the crew members to ensure that they are safe and able to perform their tasks effectively. In this project, we will extend our existing human motion analysis research effort for real-time vibration assessment.
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Multiple Domain Particle Filters with Dual-Band
Sensing for Multi-Contextual Tracking and Recognition,
Army Research Office (ARO), 2008-2011, PI: Joseph Havlicek (OU) and Co-PI: Guoliang Fan.
Summary: This research
focuses on two important ATR-related
research issues that extend our previous ATR research. The
first one is how to incorporate additional and relevant
contextual information to improve the ATR performance by
better handling the uncertainty and incompleteness in the
observed infrared
imagery, which includes a geometric camera model, a background
representation, as well as a 3D target representation and
motion models. The second one is how to develop a generative
graphical model
to integrate tracking, recognition and
learning in one computational flow where these ATR tasks are
systematically and jointly formulated as one inference
problem involving multi-contextual information. Also, we will collaborate with the OU team
led by Dr. Joseph Havlicek to validate and test our algorithms
in an experimental platform that involves dual-band sensing
and multiple-domain target modeling.
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CAREER:
Advanced Statistical Modeling Approaches for Structured Video
Representation and Research Oriented Multidisciplinary Education,
National Science Foundation, 2004-2009, PI: Guoliang Fan.
Summary:
This research aims to develop structured video representations via advanced statistical modeling approaches. The
goal is to
enhance interpretability and manipulability of visual data at both the
object-level and
the
scene-level. Specifically, at the objective level, we are interested
in developing
new human representations for integrated pose recognition, localization,
segmentation, and tracking as well as human motion estimation. At the scene
level, we are focused on exploring and learning semantic structures from
sports or commercial videos. These two studies are complementary and
inter-related that address fundamental issues in computer vision, with widespread applications,
including multimedia, digital library, visual surveillance,
entertainment, and human-computer interface, etc.
Moreover, the proposed research would have significant contribution to industry standards.
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Integrated
Target Detection, Tracking, Classification, and Learning for Dual-band
Infrared Imagery, Army Research Office (ARO), 2004-2007, PI: Joseph
Havlicek (OU) and Co-PI: Guoliang Fan.
Summary:
Summary:
Infrared (IR) automatic target recognition ( ATR) systems are employed by
all three services in variety of critical military system to detect,
track, and classify targets based their thermal signatures. In this
project, we want to enhance the overall ATR performance by addressing
three major issues, appearance representation, dynamic modeling, and
integrated algorithm implementation. The research outcomes will lead to an unified ATR system
which can incorporate both motion and visual cues into a Bayesian estimation framework
for integrated detection, tracking, recognition and learning.
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Advanced Retinal Imaging for Non-invasive Disease Study, OCAST
(Oklahoma Center for Advancement and Science Technology) Health Research
Program, 2003-2006, PI: Guoliang Fan, and Co-PI: Gary Yen.
Summary:
If detected early, ninety-five percent of the severe vision loss from
diabetic retinopathy is
preventable; yet, 40,000 people still go blind each year from this
disease. Failure to undergo universally recommended annual eye
examinations is the primary cause of this continued loss of sight.
Digital retinal imaging systems, conveniently located in the primary
care environment and connected to expert graders via computer networks,
provide "old-standard" quality evaluations. To efficiently scale these
systems,
software
tools are needed to certify image quality before the patient leaves the
imaging center, facilitate grading, and support the grader training
processes. In this project, we
aim to develop and validate advanced retinal imaging approaches
to (1) assess and control the quality of retinal imaging, (2) promote
disease detection, staging and monitoring by novel 2-D/3-D image
annotation methods, and (3) invoke a hierarchical grading process to
optimize the involvement of graders of different levels.
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Using a Multi-Resolution GIS-Modeling Approach to Evaluate the Carbon
Sequestration Potential in
Texas County,
Oklahoma, Oklahoma NASA EPSCoR, Feb. 2005-July 2005, PI: Mahesh
Rao (Geography), Co-PI: Guoliang Fan and Johnson Thomas (CS)
Summary:
This project aims to evaluate the carbon sequestration potential in
Oklahoma panhandle using a multi-resolution GIS-based modeling approach.
Owing to USDA's largest program, conservation Reserve Program (CRP),
a majority of the areas in the region are sustainable, in spite of
intensive agriculture. Our project seeks to evaluate the long-term
carbon sequestering potential of CRP in Texas County
,
Oklahoma . Specifically, we will develop a multi-resolution remote sensing
approach that involves MODIS and Landsat data as well as two GIS models from NASA
and USDA, i.e., CASA (Carnegie Ames Stanford Approach) and SWAT (Soil
and Water Assessment Tool). Moreover, interactive querying tools will be
developed and fused with the ArcGIS interface using .NET programming
modules. Our long-term goal is to develop
an integrated query-based Web-GIS Decision Support System (DSS) to help
evaluate the environmental benefits of CRP and manage future
enrollments.
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Development of a Web-GIS Decision Support System for Environmental Water
Quality and Resource Management, OSU Environmental Institute's Water
Research Center, 2003-2004, PI: Johnson Thomas (Computer Science),
Co-PI: Guoliang Fan, and Co-PI: Mahesh Rao (Geography).
Summary:
Conservation programs such as the USDA Conservation Reserve Program (CRP)
address environmental concerns including the decline of riparian areas
on private lands. The CRP Program encourages
farmers
to plant long-term resource-conserving covers to improve soil, water and
wildlife resources. On Oct.
1st, 2002 , USDA announced payments of nearly $1.6 billion
for CRP. This calls for sophisticated, accurate, and timely
decision-support aids and research tool to evaluate and justify the
environmental and water benefits of the CRP program. The overall goal of
this proposal is to design and develop
a prototype web-GIS Decision
Support System (DSS) aimed at aiding USDA/NRCS to better manage
and plan CRP enrollments. Our
proposed DSS will be based on
the emerging industry-standard ArcIMS GIS platform and will integrate a
mapping component AFIRS (Automated Feature Information Retrieval System)
and a modeling component SWAT (Soil and Water Assessment Tool).
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Toward
an Integrated Web-GIS Decision Support System for Evaluating USDA's
Conservation Reserve Program (CRP), Oklahoma NASA EPSCoR Research
Initiation Grant, 2003, PI: Guoliang Fan, Co-PI: Johnson Thomas
(Computer Science), and Co-PI: Mahesh Rao (Geography).
Summary:
This project aims to develop a prototype of an integrated Web-GIS
Decision Support System (DSS) for USDA Conservation
Reserve Program (CRP), i.e., CRP-DSS. CRP is a voluntary program to
provide incentives for farmers and ranchers to strengthen environmental
stewardship of their lands, and gives producers additional resources to
reduce topsoil erosion,
increase wildlife habitat and improve air and water quality on these
lands. However, CRP has been criticized for administrative shortcomings
and failure to achieve ancillary environmental objectives, such as
improving wildlife habitat and
promoting water quality. Key
to the inefficiency of current CRP procedures is the lack of automated
decision-support tools. The objective of this project is to develop
accurate and timely decision-support aids and research tools to
delineate and evaluate CRP. The evaluation
of CRP is based on a GIS-based environmental modeling approach. More
importantly, we will propose a prototype CRP-DSS
that will be interfaced with the Internet and be capable of accessing
databases in a distributed environment. Our long-term goal is to develop
an integrated Web-GIS DSS to help USDA manage, plan, and prioritize CRP
enrollments, leading to maximum environmental benefit within the
budget constraints.

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Developing a GIS-based Tool for Automated Feature Information Retrieval
from Multisource Geospatial Data: Application to CRP Mapping at Texas
County, Oklahoma, OSU Environmental Institute's Water Research
Center, July 2002-June 2003, PI: Guoliang Fan and Co-PI: Mahesh Rao (Geography).
Summary:
This project aims at developing a GIS-based tool, Automated Feature
Information Retrieval System, to delineate USDA's Conservation Reserve
Program (CRP) tracts. In addition to the satellite imagery, AFIRS will
involve multisource geospatial data to achieve accurate and robust
feature extractions. Specifically, a GIS
database consisting of multisource data, including Landsat TM imagery,
soils, elevation, and slope data, etc., will be developed for Texas
Count y,
OK, which ranks first in the state for CRP enrollments. Based on
this database and reference data, AFIRS will be trained, developed, and
verified. Two machine learning approaches, i.e., decision tree
classifier (DTC) and support vector machine (SVM), are applied
to develop AFIRS in this work. Experimental results show that
significant improvements can be obtained by incorporating GIS ancillary
data and other derived features. This work validates the applicability
of machine learning approaches to the real-world remote sensing
applications.
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