Browsing by Author "Rikakis, Thanassis"
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- 21st Century Public Education and Beyond Boundaries Servant LeadershipRikakis, Thanassis (Virginia Tech, 2017-04-26)Provost Rikakis's presentation to the 2017 McComas Staff Leadership Seminar
- Adaptive Life-Long Learning for an Inclusive Knowledge EconomyArnold, Amy; Lindsey, Andrew; McCoy, Andrew P.; Khademian, Anne M.; Lockee, Barbara B.; Adams, Carol; Amelink, Catherine T.; Blankenship, Chip; Glover, Christopher; Harris, Chrystal; Hoyle, Clayton; Potts, Colin; Pike, Dale; Whittaker, Dale; Kjellsson, Daniel; Hare, David; Tegarden, David P.; Tinapple, David; Ucko, David; Nahapetian, Eta; Hou, Feng; Holmes, Glen A.; Keyel, Jared; Garrett, Jeff; Joo, Jenna; McPhee, Joel; Boyer, John D.; Flato, John; Lister, Jonothan; Haldane, Joseph; Greenwood, Julie; Sanders, Karen Eley; Bruce, Karla; Lindsey, Kate; Carlson, Kimberly; Wingfeld, Kristin; Hamilton, Laura; McNair, Lisa D.; Kamlet, Mark; Semmel, Marsha; Holt, Matthew; Richey, Michael; Kumar, Mukul; Spivy, Nene; Cardwell, Owen; Holloway, Rachel L.; Swearer, Randy; Hall, Ralph P.; Clark-Stallkamp, Rebecca; Mazer, Robert; Smith, Robert; Reynolds, Roger; Bess, Diego Scott; Weimer, Scott; Sagheb, Shahabedin; Garmise, Sheri; Ashburn, Sherrell; Johnson, Sylvester; Cardone, Taran; Nicewonger, Todd; Martin, Tom; Quick, Tom; Rikakis, Thanassis; Skuzinski, Thomas; Contomanolis, Manny (Calhoun Center for Higher Education Innovation, 2020-08-24)This report addresses the globalized knowledge economy in the 21st century; not only as it exists today, but the knowledge economy needed to meet the demands of tomorrow. This report proposes that in order for our knowledge economy to grow and be sustainable, it must be inclusive in ways that enable it to adapt to—and incorporate within it—the personal and professional growth of a large and diverse body of lifelong learners. In this introduction, we first define what we mean by inclusive knowledge and explain how our proposed definition expands some of the traditional understandings. We then show that an expansive and dynamic conceptualization of knowledge increases inclusion and promotes lifelong adaptive learning as a mindset and a practice.
- Automated Movement Assessment in Stroke RehabilitationAhmed, Tamim; Thopalli, Kowshik; Rikakis, Thanassis; Turaga, Pavan; Kelliher, Aisling; Huang, Jia-Bin; Wolf, Steven L. (2021-08-19)We are developing a system for long term Semi-Automated Rehabilitation At the Home (SARAH) that relies on low-cost and unobtrusive video-based sensing. We present a cyber-human methodology used by the SARAH system for automated assessment of upper extremity stroke rehabilitation at the home. We propose a hierarchical model for automatically segmenting stroke survivor's movements and generating training task performance assessment scores during rehabilitation. The hierarchical model fuses expert therapist knowledge-based approaches with data-driven techniques. The expert knowledge is more observable in the higher layers of the hierarchy (task and segment) and therefore more accessible to algorithms incorporating high level constraints relating to activity structure (i.e., type and order of segments per task). We utilize an HMM and a Decision Tree model to connect these high level priors to data driven analysis. The lower layers (RGB images and raw kinematics) need to be addressed primarily through data driven techniques. We use a transformer based architecture operating on low-level action features (tracking of individual body joints and objects) and a Multi-Stage Temporal Convolutional Network(MS-TCN) operating on raw RGB images. We develop a sequence combining these complimentary algorithms effectively, thus encoding the information from different layers of the movement hierarchy. Through this combination, we produce a robust segmentation and task assessment results on noisy, variable and limited data, which is characteristic of low cost video capture of rehabilitation at the home. Our proposed approach achieves 85% accuracy in per-frame labeling, 99% accuracy in segment classification and 93% accuracy in task completion assessment. Although the methodology proposed in this paper applies to upper extremity rehabilitation using the SARAH system, it can potentially be used, with minor alterations, to assist automation in many other movement rehabilitation contexts (i.e., lower extremity training for neurological accidents).
- Calibrating Video Capture Systems To Aid Automated Analysis And Expert Rating Of Human Movement PerformanceYeshala, Sai krishna (Virginia Tech, 2022-06-27)We propose a methodology for calibrating the activity space and the cameras involved in video capture systems for upper extremity stroke rehabilitation. We discuss an in-home stroke rehabilitation system called Semi-Automated Rehabilitation At Home System (SARAH) and a clinic-based system called Action Research Arm Test (ARAT) developed by the Interactive Neuro-Rehabilitation Lab (INR) at Virginia Tech. We propose a calibration workflow for achieving invariant video capture across multiple therapy sessions. This ensures that the captured data is less noisy. In addition, there is prior knowledge of the captured activity space and patient location in the video frames provided to the Computer Vision algorithms analyzing the captured data. Such a standardized calibration approach improved machine learning analysis of patient movements and a higher rate of agreement across multiple therapists regarding the captured patient performance. We further propose a Multi-Camera Calibration approach to perform stereo camera calibration in SARAH and ARAT capture systems to help perform a 3D reconstruction of the activity space from 2D videos. The importance of the proposed activity space and camera calibration workflows, including new research paths opened as a result of our approach, are discussed in this thesis.
- Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation ApplicationsSarker, Anik; Emenonye, Don-Roberts; Kelliher, Aisling; Rikakis, Thanassis; Buehrer, R. Michael; Asbeck, Alan T. (MDPI, 2022-03-16)For upper extremity rehabilitation, quantitative measurements of a person’s capabilities during activities of daily living could provide useful information for therapists, including in telemedicine scenarios. Specifically, measurements of a person’s upper body kinematics could give information about which arm motions or movement features are in need of additional therapy, and their location within the home could give context to these motions. To that end, we present a new algorithm for identifying a person’s location in a region of interest based on a Bluetooth received signal strength (RSS) and present an experimental evaluation of this and a different Bluetooth RSS-based localization algorithm via fingerprinting. We further present algorithms for and experimental results of inferring the complete upper body kinematics based on three standalone inertial measurement unit (IMU) sensors mounted on the wrists and pelvis. Our experimental results for localization find the target location with a mean square error of 1.78 m. Our kinematics reconstruction algorithms gave lower errors with the pelvis sensor mounted on the person’s back and with individual calibrations for each test. With three standalone IMUs, the mean angular error for all of the upper body segment orientations was close to 21 degrees, and the estimated elbow and shoulder angles had mean errors of less than 4 degrees.
- Envisioning Virginia Tech Beyond Boundaries: A 2047 VisionBlieszner, Rosemary; Grant, Alan L.; Rikakis, Thanassis (Virginia Tech, 2016-11)A framework prepared by Beyond Boundaries participants.
- HOMER: An Interactive System for Home Based Stroke RehabilitationKelliher, Aisling; Choi, Jinwoo; Huang, Jia-Bin; Rikakis, Thanassis; Kitani, Kris (ACM, 2017)Delivering long term, unsupervised stroke rehabilitation in the home is a complex challenge that requires robust, low cost, scalable, and engaging solutions. We present HOMER, an interactive system that uses novel therapy artifacts, a computer vision approach, and a tablet interface to provide users with a flexible solution suitable for home based rehabilitation. HOMER builds on our prior work developing systems for lightly supervised rehabilitation use in the clinic, by identifying key features for functional movement analysis, adopting a simplified classification assessment approach, and supporting transferability of therapy outcomes to daily living experiences through the design of novel rehabilitation artifacts. A small pilot study with unimpaired subjects indicates the potential of the system in effectively assessing movement and establishing a creative environment for training.
- Human Computer Interaction for Complex Machine LearningZilevu, Kobla Setor (Virginia Tech, 2022-05-09)This dissertation focuses on taking a human-centric approach to utilize human intelligence best to inform machine learning models. More specifically, the complex relationship between the changes in movement functionality to movement quality. I designed and evaluated the Tacit Computable Empowering methodology across two domains: in-home rehabilitation and clinical assessment. My methodology has three main objectives: first, to transform tacit expert knowledge into explicit knowledge. Second, to transform explicit knowledge into a computable framework that machine learning can understand and replicate. Third, synergize human intelligence with computational machine learning to empower, not replace, the human. Finally, my methodology uses assistive interfaces to allow clinicians and machine learning models to draw parallels between movement functionality and movement quality. The results from my dissertation inform researchers and clinicians on how best to create a standardized framework to capture and assess human movement data for embodied learning scenarios
- Interactive Interfaces for Capturing and Annotating Videos of Human MovementZilevu, Kobla Setor (Virginia Tech, 2019-07-11)In this thesis, I describe the iterative service design process I used in identifying and understanding the needs of diverse stakeholders, the development of technologies to support their mutually beneficial needs, and the evaluation of the end-user experience with these technologies. Over three iterative design cycles, the set of identified end-user customers expanded to include the patient, the supervising therapist, the annotating therapist, and other members of the development team. Multiple versions of interactive movement capture and annotation tools were developed as the needs of these stakeholders were clarified and evolved, and the optimal data forms and structures became evident. Interactions between the stakeholders and the developed technologies operating in various environments were evaluated and assessed to help improve and optimize the entire service ecosystem. Results and findings from these three design cycles are being used to direct and shape my ongoing and future doctoral research