Director: Dr. Tracy Anne Hammond

# SRL Dissertations and Theses

## Dissertations

 2016 Valentine, Stephanie. Design, Deployment, Identity, & Conformity: An Analysis of Children's Online Social Networks. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2016. First Position: TAMU Asst. Research Scientist. LinkShow Abstract: To Be Entered Show BibTex@mastersthesis{stephanievalentine2016PhD, type = {{PhD Doctoral Dissertation}}, author = {Valentine, Stephanie}, title = {Design, Deployment, Identity, \& Conformity: An Analysis of Children's Online Social Networks}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Alamudun, Folami. Analysis of Visuo-cognitive Behavior in Screening Mammography. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Oak Ridge Laboratories. LinkShow Abstract: To Be Entered Show BibTex@mastersthesis{folamialamudun2016PhD, type = {{PhD Doctoral Dissertation}}, author = {Alamudun, Folami}, title = {Analysis of Visuo-cognitive Behavior in Screening Mammography}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Kim, Hong-hoe . A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Samsung. LinkShow Abstract: TBD Show BibTex@mastersthesis{honghoekim2016PhD, type = {{PhD Doctoral Dissertation}}, author = {Kim, Hong-hoe }, title = {A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2014 Prasad, Manoj. Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motocyclists Navigation. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 183 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2014. First Position: Microsoft. LinkShow Abstract: The tactile medium of communication with users is appropriate for displaying information in situations where auditory and visual mediums are saturated. There are situations where a subject’s ability to receive information through either of these channels is severely restricted by the environment they are in or through any physical impairments that the subject may have. In this project, we have focused on two groups of users who need sustained visual and auditory focus in their task: Soldiers on the battlefield and motorcyclists. Soldiers on the battlefield use their visual and auditory capabilities to maintain awareness of their environment to guard themselves from enemy assault. One of the major challenges to coordination in a hazardous environment is maintaining communication between team members while mitigating cognitive load. Compromise in communication between team members may result in mistakes that can adversely affect the outcome of a mission. We have built two vibrotactile displays, Tactor I and Tactor II, each with nine actuators arranged in a three-by-three matrix with differing contact areas that can represent a total of 511 shapes. We used two dimensions of tactile medium, shapes and waveforms, to represent verb phrases and evaluated ability of users to perceive verb phrases the tactile code. We evaluated the effectiveness of communicating verb phrases while the users were performing two tasks simultaneously. The results showed that performing additional visual task did not affect the accuracy or the time taken to perceive tactile codes. Another challenge in coordinating Soldiers on a battlefield is navigating them to respective assembly areas. We have developed HaptiGo, a lightweight haptic ii vest that provides pedestrians both navigational intelligence and obstacle detection capabilities. HaptiGo consists of optimally-placed vibro-tactile sensors that utilize natural and small form factor interaction cues, thus emulating the sensation of being passively guided towards the intended direction. We evaluated HaptiGo and found that it was able to successfully navigate users with timely alerts of incoming obstacles without increasing cognitive load, thereby increasing their environmental awareness. Additionally, we show that users are able to respond to directional information without training. The needs of motorcyclists are different from those of Soldiers. Motorcyclists’ need to maintain visual and auditory situational awareness at all times is crucial since they are highly exposed on the road. Route guidance systems, such as the Garmin, have been well tested on automobilists, but remain much less safe for use by motorcyclists. Audio/visual routing systems decrease motorcyclists’ situational awareness and vehicle control, and thus increase the chances of an accident. To enable motorcyclists to take advantage of route guidance while maintaining situa- tional awareness, we created HaptiMoto, a wearable haptic route guidance system. HaptiMoto uses tactile signals to encode the distance and direction of approaching turns, thus avoiding interference with audio/visual awareness. Evaluations show that HaptiMoto is intuitive for motorcyclists, and a safer alternative to existing solutions. Show BibTex@mastersthesis{manojprasad2014PhD, type = {{PhD Doctoral Dissertation}}, author = {Prasad, Manoj}, title = {Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motocyclists Navigation}, school = {Texas A\&M University (TAMU)}, year = {2014}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 183 pages.}} 2013 Cummings, Danielle. Multimodal Interaction for Enhancing Team Coordination on the Battlefield. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 201 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2013. First Position: NSA. LinkShow Abstract: Team coordination is vital to the success of team missions. On the battlefield and in other hazardous environments, mission outcomes are often very unpredictable because of unforeseen circumstances and complications encountered that adversely affect team coordination. In addition, the battlefield is constantly evolving as new technology, such as context-aware systems and unmanned drones, becomes available to assist teams in coordinating team efforts. As a result, we must re-evaluate the dynamics of teams that operate in high-stress, hazardous environments in order to learn how to use technology to enhance team coordination within this new context. In dangerous environments where multi-tasking is critical for the safety and success of the team operation, it is important to know what forms of interaction are most conducive to team tasks. We have explored interaction methods, including various types of user input and data feedback mediums that can assist teams in performing unified tasks on the battlefield. We’ve conducted an ethnographic analysis of Soldiers and researched technologies such as sketch recognition, physiological data classification, augmented reality, and haptics to come up with a set of core principles to be used when de- signing technological tools for these teams. This dissertation provides support for these principles and addresses outstanding problems of team connectivity, mobility, cognitive load, team awareness, and hands-free interaction in mobile military applications. This research has resulted in the development of a multimodal solution that enhances team coordination by allowing users to synchronize their tasks while keeping an overall awareness of team status and their environment. The set of solutions we’ve developed utilizes optimal interaction techniques implemented and evaluated in related projects; the ultimate goal of this research is to learn how to use technology to provide total situational awareness and team connectivity on the battlefield. This information can be used to aid the research and development of technological solutions for teams that operate in hazardous environments as more advanced resources become available. Show BibTex@mastersthesis{daniellecummings2013PhD, type = {{PhD Doctoral Dissertation}}, author = {Cummings, Danielle}, title = {Multimodal Interaction for Enhancing Team Coordination on the Battlefield}, school = {Texas A\&M University (TAMU)}, year = {2013}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 201 pages.}} 2013 Sashikanth, Raju; Damaraju, Sriranga. An Exploration of Multi-touch Interaction Techniques. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 145 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2013. First Position: Zillabyte. LinkShow Abstract: Research in multi-touch interaction has typically been focused on direct spatial manipulation; techniques have been created to result in the most intuitive mapping between the movement of the hand and the resultant change in the virtual object. As we attempt to design for more complex operations, the effectiveness of spatial manipulation as a metaphor becomes weak. We introduce two new platforms for multi-touch computing: a gesture recognition system, and a new interaction technique. I present Multi-Tap Sliders, a new interaction technique for operation in what we call non-spatial parametric spaces. Such spaces do not have an obvious literal spatial representation, (Eg.: exposure, brightness, contrast and saturation for image editing). The multi-tap sliders encourage the user to keep her visual focus on the target, instead of requiring her to look back at the interface. My research emphasizes ergonomics, clear visual design, and fluid transition between modes of operation. Through a series of iterations, I develop a new technique for quickly selecting and adjusting multiple numerical parameters. Evaluations of multi-tap sliders show improvements over traditional sliders. To facilitate further research on multi-touch gestural interaction, I developed mGestr: a training and recognition system using hidden Markov models for designing a multi-touch gesture set. Our evaluation shows successful recognition rates of up to 95%. The recognition framework is packaged into a service for easy integration with existing applications. Show BibTex@mastersthesis{sashikanthdamaraju2013PhD, type = {{PhD Doctoral Dissertation}}, author = {Sashikanth, Raju; Damaraju, Sriranga}, title = {An Exploration of Multi-touch Interaction Techniques}, school = {Texas A\&M University (TAMU)}, year = {2013}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 145 pages.}} 2010 Paulson, Brandon C.. Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition. Texas A&M University (TAMU) PhD Doctoral Dissertation. Advisor: Tracy Hammond. 217 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2010. First Position: Capsure. LinkShow Abstract: Online sketch recognition uses machine learning and artificial intelligence techniques to interpret markings made by users via an electronic stylus or pen. The goal of sketch recognition is to understand the intention and meaning of a particular user’s drawing. Diagramming applications have been the primary beneficiaries of sketch recognition technology, as it is commonplace for the users of these tools to first create a rough sketch of a diagram on paper before translating it into a machine understandable model, using computer-aided design tools, which can then be used to perform simulations or other meaningful tasks. Traditional methods for performing sketch recognition can be broken down into three distinct categories: appearance-based, gesture-based, and geometric-based. Although each approach has its advantages and disadvantages, geometric-based methods have proven to be the most generalizable for multi-domain recognition. Tools, such as the LADDER symbol description language, have shown to be capable of recognizing sketches from over 30 different domains using generalizable, geometric techniques. The LADDER system is limited, however, in the fact that it uses a low-level recognizer that supports only a few primitive shapes, the building blocks for describing higher-level symbols. Systems which support a larger number of primitive shapes have been shown to have questionable accuracies as the number of primitives increase, or they place constraints on how users must input shapes (e.g. circles can only be drawn in a clockwise motion; rectangles must be drawn starting at the top-left corner). This dissertation allows for a significant growth in the possibility of free-sketch recognition systems, those which place little to no drawing constraints on users. In this dissertation, we describe multiple techniques to recognize upwards of 18 primitive shapes while maintaining high accuracy. We also provide methods for producing confidence values and generating multiple interpretations, and explore the difficulties of recognizing multi-stroke primitives. In addition, we show the need for a standardized data repository for sketch recognition algorithm testing and propose SOUSA (sketch-based online user study application), our online system for performing and sharing user study sketch data. Finally, we will show how the principles we have learned through our work extend to other domains, including activity recognition using trained hand posture cues. Show BibTex@mastersthesis{brandonpaulson2010PhD, type = {{PhD Doctoral Dissertation}}, author = {Paulson, Brandon C.}, title = {Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition}, school = {Texas A\&M University (TAMU)}, year = {2010}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 217 pages.}} 2007 Hammond, Tracy. LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development. Massachusetts Institute of Technology (MIT) PhD Doctoral Dissertation. Advisor: Randall Davis. 495 pages. Massachusetts Institute of Technology (MIT), Cambridge, MA, USA. February, 2007. First Position: TAMU Asst. Professor. LinkShow Abstract: Diagrammatic sketching is a natural modality of human-computer interaction that can be used for a variety of tasks, for example, conceptual design. Sketch recognition systems are currently being developed for many domains. However, they require signal-processing expertise if they are to handle the intricacies of each domain, and they are time- consuming to build. Our goal is to enable user interface designers and domain experts who may not have expertise in sketch recognition to be able to build these sketch systems. We created and implemented a new framework (FLUID - facilitating user interface development) in which developers can specify a domain description indicating how domain shapes are to be recognized, displayed, and edited. This description is then automatically transformed into a sketch recognition user interface for that domain. LADDER, a language using a perceptual vocabulary based on Gestalt principles, was developed to describe how to recognize, display, and edit domain shapes. A translator and a customizable recognition system (GUILD - a generator of user interfaces using ladder descriptions) are combined with a domain description to automatically create a domain specific recognition system. With this new technology, by writing a domain description, developers are able to create a new sketch interface for a domain, greatly reducing the time and expertise for the task. Continuing in pursuit of our goal to facilitate UI development, we noted that 1) human generated descriptions contained syntactic and conceptual errors, and that 2) it is more natural for a user to specify a shape by drawing it than by editing text. However, computer generated descriptions from a single drawn example are also flawed, as one cannot express all allowable variations in a single example. In response, we created a modification of the traditional model of active learning in which the system selectively generates its own near-miss examples and uses the human teacher as a source of labels. System generated near-misses offer a number of advantages. Human generated examples are tedious to create and may not expose problems in the current concept. It seems most effective for the near-miss examples to be generated by whichever learning participant (teacher or student) knows better where the deficiencies lie; this will allow the concepts to be more quickly and effectively refined. When working in a closed domain such as this one, the computer learner knows exactly which conceptual uncertainties remain, and which hypotheses need to be tested and confirmed. The system uses these labeled examples to auto- matically build a LADDER shape description, using a modification of the version spaces algorithm that handles interrelated constraints, and which also has the ability to learn negative and disjunctive constraints. Show BibTex@mastersthesis{tracyhammond2007PhD, type = {{PhD Doctoral Dissertation}}, author = {Hammond, Tracy}, title = {LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development}, school = {Massachusetts Institute of Technology (MIT)}, year = {2007}, month = {February}, address = {Cambridge, MA, USA}, note = {Advisor: Randall Davis. 495 pages.}}

## Master's Theses

 2017 Cherian, Josh. Recognition of Everyday Activities through Wearable Sensors and Machine Learning. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. First Position: TAMU PhD Student. LinkShow Abstract: Over the past several years, the use of wearable devices has increased dramatically,primarily for fitness monitoring, largely due to their greater sensor reliability, increasedfunctionality, smaller size, increased ease of use, and greater affordability.These deviceshave helped many people of all ages live healthier lives and achieve their personal fitnessgoals, as they are able to see quantifiable and graphical results of their efforts every stepof the way (i.e. in real-time). Yet, while these device systems work well within the fitnessdomain, they have yet to achieve a convincing level of functionality in the larger domainof healthcare. As an example, according to the Alzheimer’s Association, there are currently approxi-mately 5.5 million Americans with Alzheimer’s Disease and approximately 5.3 million ofthem are over the age of 65, comprising 10% of this age group in the U.S. The economictoll of this disease is estimated to be around $259 billion. By 2050 the number of Amer-icans with Alzheimer’s disease is predicted to reach around 16 million with an economictoll of over$1 trillion. There are other prevalent and chronic health conditions that arecritically important to monitor, such as diabetes, complications from obesity, congestiveheart failure, and chronic obstructive pulmonary disease (COPD) among others. The goal of this research is to explore and develop accurate and quantifiable sensingand machine learning techniques for eventual real-time health monitoring by wearabledevice systems. To that end, a two-tier recognition system is presented that is designed toidentify health activities in a naturalistic setting based on accelerometer data of commonactivities. In Tier I a traditional activity recognition approach is employed to classify shortwindows of data, while in Tier II these classified windows are grouped to identify instancesof a specific activity. Everyday activities that were explored in this research include brushing one’s teeth, combing one’s hair, scratching one’s chin, washing one’s hands,taking medication, and drinking. Results show that an F-measure of 0.83 is achievablewhen identifying these activities from each other and an F-measure of of 0.82 is achievablewhen identifying instances of brushing teeth over the course of a day. Show BibTex@mastersthesis{joshcherian2017MS, type = {{MS Master's Thesis}}, author = {Cherian, Josh}, title = {Recognition of Everyday Activities through Wearable Sensors and Machine Learning}, school = {Texas A\&M University (TAMU)}, year = {2017}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond \& Theresa Maldonado. }} 2017 Bhat, Aqib Niaz. Sketchography - Automatic grading of map sketches for geography education. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. LinkShow Abstract: TBD Show BibTex@mastersthesis{aqibbhat2017MS, type = {{MS Master's Thesis}}, author = {Bhat, Aqib Niaz}, title = {Sketchography - Automatic grading of map sketches for geography education}, school = {Texas A\&M University (TAMU)}, year = {2017}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2017 Herrera Camara, Jorge Ivan. bFlow2code - From Hand-drawn Flowchart to Code Execution. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. LinkShow Abstract: TBD Show BibTex@mastersthesis{jorgecamara2017MS, type = {{MS Master's Thesis}}, author = {Herrera Camara, Jorge Ivan}, title = {bFlow2code - From Hand-drawn Flowchart to Code Execution}, school = {Texas A\&M University (TAMU)}, year = {2017}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2017 Villanueva Luna, Nahum. ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. LinkShow Abstract: TBD Show BibTex@mastersthesis{nahumvillanueva2017MS, type = {{MS Master's Thesis}}, author = {Villanueva Luna, Nahum}, title = {ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience}, school = {Texas A\&M University (TAMU)}, year = {2017}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Copesetty, Siddhartha Karthik . Labeling by Example. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2016. First Position: Uber. LinkShow Abstract: TBD Show BibTex@mastersthesis{siddharthakarthik2016MS, type = {{MS Master's Thesis}}, author = {Copesetty, Siddhartha Karthik }, title = {Labeling by Example}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Keshavabhotla, Swarna. PerSketchTivity: Recognition and Progressive Learning Analysis. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2016. First Position: FactSet Research Systems. LinkShow Abstract: TBD Show BibTex@mastersthesis{swarnakeshavabhotla2016MS, type = {{MS Master's Thesis}}, author = {Keshavabhotla, Swarna}, title = {PerSketchTivity: Recognition and Progressive Learning Analysis}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Ashok Kumar, Shalini Priya. Evaluation of Conceptual Sketches on Stylus-based Devices. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Google. LinkShow Abstract: TBD Show BibTex@mastersthesis{shaliniashokkumar2016MS, type = {{MS Master's Thesis}}, author = {Ashok Kumar, Shalini Priya}, title = {Evaluation of Conceptual Sketches on Stylus-based Devices}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Kaul, Purnendu. Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing). Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Walmart Technologies. LinkShow Abstract: TBD Show BibTex@mastersthesis{purnendukaul2016MS, type = {{MS Master's Thesis}}, author = {Kaul, Purnendu}, title = {Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2016 Ray, Jaideep. Finding Similar Sketches. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. First Position: Facebook. LinkShow Abstract: TBD Show BibTex@mastersthesis{jaideepray2016MS, type = {{MS Master's Thesis}}, author = {Ray, Jaideep}, title = {Finding Similar Sketches}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2015 Guo, Shiqiang (Frank). ResuMatcher: A Personalized Resume-Job Matching System. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Treacy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2015. First Position: Amazon. LinkShow Abstract: TBD Show BibTex@mastersthesis{shiqiangguo2015MS, type = {{MS Master's Thesis}}, author = {Guo, Shiqiang (Frank)}, title = {ResuMatcher: A Personalized Resume-Job Matching System}, school = {Texas A\&M University (TAMU)}, year = {2015}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Treacy Hammond. }} 2014 Yin, Zhangliang. Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. August, 2014. First Position: Amazon. LinkShow Abstract: TBD Show BibTex@mastersthesis{zhengliangyin2014MS, type = {{MS Master's Thesis}}, author = {Yin, Zhangliang}, title = {Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese}, school = {Texas A\&M University (TAMU)}, year = {2014}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2012 Kim, Hong-Hoe. Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 105 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2012. First Position: TAMU PhD Student. LinkShow Abstract: The current education systems in elementary schools are usually using traditional teaching methods such as paper and pencil or drawing on the board. The benefit of paper and pencil is their ease of use. Researchers have tried to bring this ease of use to computer-based educational systems through the use of sketch-recognition. Sketch-recognition allows students to draw naturally while at the same time receiving automated assistance and feedback from the computer. There are many sketch-based educational systems for children. However, current sketch-based educational systems use the same sketch recognizer for both adults and children. The problem of this approach is that the recognizers are trained by using sample data drawn by adults, even though the drawing patterns of children and adults are markedly different. We propose that if we make a separate recognizer for children, we can increase the recognition accuracy of shapes drawn by children. By creating a separate recognizer for children, we improved the recognition accuracy of children’s drawings from 81.25% (using the adults’ threshold) to 83.75% (using adjusted threshold for children). Additionally, we were able to automatically distinguish children’s drawings from adults’ drawings. We correctly identified the drawer’s age (age 3, 4, 7, or adult) with 78.3%. When distinguishing toddlers (age 3 and 4) from matures (age 7 and adult), we got a precision of 95.2% using 10-fold cross validation. When we removed adults and distinguished between toddlers and 7 year olds, we got a precision of 90.2%. Distinguishing between 3, 4, and 7 year olds, we got a precision of 86.8%. Furthermore, we revealed that there is a potential gender difference since our recognizer was more accurately able to recognize the drawings of female children (91.4%) than the male children (85.4%). Finally, this paper introduces a sketch-based teaching assistant tool for children, EasySketch, which teaches children how to draw digits and characters. Children can learn how to draw digits and characters by instructions and feedback. Show BibTex@mastersthesis{honghoekim2012MS, type = {{MS Master's Thesis}}, author = {Kim, Hong-Hoe}, title = {Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications}, school = {Texas A\&M University (TAMU)}, year = {2012}, month = {December}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 105 pages.}} 2012 Logsdon, Drew. Arm-Hand-Finger Video Game Interaction. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 108 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2012. First Position: IBM. LinkShow Abstract: Show BibTex@mastersthesis{drewlogsdon2012MS, type = {{MS Master's Thesis}}, author = {Logsdon, Drew}, title = {Arm-Hand-Finger Video Game Interaction}, school = {Texas A\&M University (TAMU)}, year = {2012}, month = {December}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 108 pages.}} 2012 Lucchese, George. Sketch Recognition on Mobile Devices. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 54 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2012. First Position: IBM. LinkShow Abstract: Sketch recognition allows computers to understand and model hand drawn sketches and diagrams. Traditionally sketch recognition systems required a pen based PC interface, but powerful mobile devices such as tablets and smartphones can provide a new platform for sketch recognition systems. We describe a new sketch recognition library, Strontium (SrL) that combines several existing sketch recognition libraries modified to run on both personal computers and on the Android platform. We analyzed the recognition speed and accuracy implications of performing low-level shape recognition on smartphones with touch screens. We found that there is a large gap in recognition speed on mobile devices between recognizing simple shapes and more complex ones, suggesting that mobile sketch interface designers limit the complexity of their sketch domains. We also found that a low sampling rate on mobile devices can affect recognition accuracy of complex and curved shapes. Despite this, we found no evidence to suggest that using a finger as an input implement leads to a decrease in simple shape recognition accuracy. These results show that the same geometric shape recognizers developed for pen applications can be used in mobile applications, provided that developers keep shape domains simple and ensure that input sampling rate is kept as high as possible. Show BibTex@mastersthesis{georgelucchese2012MS, type = {{MS Master's Thesis}}, author = {Lucchese, George}, title = {Sketch Recognition on Mobile Devices}, school = {Texas A\&M University (TAMU)}, year = {2012}, month = {December}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 54 pages.}} 2012 Li, Wenzhe. Acoustic Based Sketch Recognition. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 91 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2012. First Position: USC PhD Student, Goldman Sachs. LinkShow Abstract: Show BibTex@mastersthesis{wenzheli2012MS, type = {{MS Master's Thesis}}, author = {Li, Wenzhe}, title = {Acoustic Based Sketch Recognition}, school = {Texas A\&M University (TAMU)}, year = {2012}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 91 pages.}} 2012 Vides Ceron, Francisco. TAYouKi: A Sketch-Based Tutoring System for Young Kids. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 129 pages. Texas A&M University (TAMU), College Station, TX, USA. August, 2012. First Position: PayPal. LinkShow Abstract: Intelligent tutoring systems (ITS) have proven to be effective tools for aiding in the instruction of new skills for young kids; however, interaction methods that employ traditional input devices such as the keyboard and mouse may present barriers to children who have yet learned how to write. Existing applications which utilize pen- input devices better mimic the physical act of writing, but few provide useful feedback to the users. This thesis presents a system specifically designed to serve as a useful tool in teaching children how to draw basic shapes, and helping them develop basic drawing and writing skills. The system uses a combination of sketch recognition techniques to interpret the handwritten strokes from sketches of the children, and then provides intelligent feedback based on what they draw. Our approach provides a virtual coach to assist teachers teaching the critical skills of drawing and handwriting. We do so by guiding children through a set of exercises of increasing complexity according to their progress, and at the same time keeping track of students' performance and engagement, giving them differentiated instruction and feedback. Our system would be like a virtual Teaching Assistant for Young Kids, hence we call it TAYouKi. We collected over five hundred hand-drawn shapes from grownups that had a clear understanding of what a particular geometric shape should look like. We used this data to test the recognition of our system. Following, we conducted a series of case studies with children in age group three to six to test the interactivity efficacy of the system. The studies served to gain important insights regarding the research challenges in different domains. Results suggest that our approach is appealable and engaging to children and can help in more effectively teach them how to draw and write. Show BibTex@mastersthesis{franciscovides2012MS, type = {{MS Master's Thesis}}, author = {Vides Ceron, Francisco}, title = {TAYouKi: A Sketch-Based Tutoring System for Young Kids}, school = {Texas A\&M University (TAMU)}, year = {2012}, month = {August}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 129 pages.}} 2010 Taele, Paul. Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 96 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2010. First Position: TAMU PhD Student. LinkShow Abstract: One of the challenges students face in studying an East Asian (EA) language (e.g., Chinese, Japanese, and Korean) as a second language is mastering their selected language’s written component. This is especially true for students with native fluency of English and deficient written fluency of another EA language. In order to alleviate the steep learning curve inherent in the properties of EA languages’ complicated writing scripts, language instructors conventionally introduce various written techniques such as stroke order and direction to allow students to study writing scripts in a systematic fashion. Yet, despite the advantages gained from written technique instruction, the physical presence of the language instructor in conventional instruction is still highly desirable during the learning process; not only does it allow instructors to offer valuable real-time critique and feedback interaction on students’ writings, but it also allows instructors to correct students’ bad writing habits that would impede mastery of the written language if not caught early in the learning process. The current generation of computer-assisted language learning (CALL) applications specific to written EA languages have therefore strived to incorporate writing-capable modalities in order to allow students to emulate their studies outside the classroom setting. Several factors such as constrained writing styles, and weak feedback and assessment capabilities limit these existing applications and their employed techniques from closely mimicking the benefits that language instructors continue to offer. In this thesis, I describe my geometric-based sketch recognition approach to several writing scripts in the EA languages while addressing the issues that plague existing CALL applications and the handwriting recognition techniques that they utilize. The approach takes advantage of A Language to Describe, Display, and Editing in Sketch Recognition (LADDER) framework to provide users with valuable feedback and assessment that not only recognizes the visual correctness of students’ written EA Language writings, but also critiques the technical correctness of their stroke order and direction. Furthermore, my approach provides recognition independent of writing style that allows students to learn with natural writing through size- and amount-independence, thus bridging the gap between beginner applications that only recognize single-square input and expert tools that lack written technique critique. Show BibTex@mastersthesis{paultaele2010MS, type = {{MS Master's Thesis}}, author = {Taele, Paul}, title = {Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages}, school = {Texas A\&M University (TAMU)}, year = {2010}, month = {December}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 96 pages.}} 2010 Wolin, Aaron. Segmenting Hand-Drawn Strokes. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 160 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2010. First Position: Credera. LinkShow Abstract: Pen-based interfaces utilize sketch recognition so users can create and interact with complex, graphical systems via drawn input. In order for people to freely draw within these systems, users’ drawing styles should not be constrained. The low-level techniques involved with sketch recognition must then be perfected, because poor low-level accuracy can impair a user’s interaction experience. Corner finding, also known as stroke segmentation, is one of the first steps to free-form sketch recognition. Corner finding breaks a drawn stroke into a set of primitive symbols such as lines, arcs, and circles, so that the original stroke data can be transformed into a more machine-friendly format. By working with sketched primitives, drawn objects can then be described in a visual language, noting what primitive shapes have been drawn and the shapes’ geometric relationships to each other. We present three new corner finding techniques that improve segmentation accuracy. Our first technique, MergeCF, is a multi-primitive segmenter that splits drawn strokes into primitive lines and arcs. MergeCF eliminates extraneous primitives by merging them with their neighboring segments. Our second technique, ShortStraw, works with polyline-only data. Polyline segments are important since many domains use simple polyline symbols formed with squares, triangles, and arrows. Our ShortStraw algorithm is simple to implement, yet more powerful than previous polyline work in the corner finding literature. Lastly, we demonstrate how a combination technique can be used to pull the best corner finding results from multiple segmentation algorithms. This combination segmenter utilizes the best corners found from other segmentation techniques, eliminating many false negatives (missed primitive segmentations) from the final, low-level results. We will present the implementation and results from our new segmentation techniques, showing how they perform better than related work in the corner finding field. We will also discuss limitations of each technique, how we have sought to overcome those limitations, and where we believe the sketch recognition subfield of corner finding is headed. Show BibTex@mastersthesis{aaronwolin2010MS, type = {{MS Master's Thesis}}, author = {Wolin, Aaron}, title = {Segmenting Hand-Drawn Strokes}, school = {Texas A\&M University (TAMU)}, year = {2010}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 160 pages.}} 2009 Dixon, Daniel. A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw. Texas A&M University (TAMU) MS Master's Thesis. Advisor: Tracy Hammond. 114 pages. Texas A&M University (TAMU), College Station, TX, USA. December, 2009. First Position: ReelFX. LinkShow Abstract: When asked to draw, most people are hesitant because they believe themselves unable to draw well. A human instructor can teach students how to draw by encouraging them to practice established drawing techniques and by providing personal and directed feedback to foster their artistic intuition and perception. This thesis describes the first methodology for a computer application to mimic a human instructor by providing direction and feedback to assist a student in drawing a human face from a photograph. Nine design principles were discovered and developed for providing such instruction, presenting reference media, giving corrective feedback, and receiving actions from the student. Face recognition is used to model the human face in a photograph so that sketch recognition can map a drawing to the model and evaluate it. New sketch recognition techniques and algorithms were created in order to perform sketch understanding on such subjective content. After two iterations of development and user studies for this methodology, the result is a computer application that can guide a person toward producing his/her own sketch of a human model in a reference photograph with step-by- step instruction and computer generated feedback. Show BibTex@mastersthesis{danieldixon2009MS, type = {{MS Master's Thesis}}, author = {Dixon, Daniel}, title = {A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw}, school = {Texas A\&M University (TAMU)}, year = {2009}, month = {December}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 114 pages.}} 2001 Hammond, Tracy. Ethnomathematics: Concept Definition and Research Perspectives. MA Master's Thesis. Advisor: Ellen Marakowitz. 57 pages. New York, NY, USA. February, 2001. First Position: MIT PhD Student. LinkShow Abstract: Although the term ethnomathematics has been in use in the anthropological literature for quite sometime now, a standard definition of the construct has yet to emerge. More than one definition exists, causing confusion and inhibiting systematic research on the subject. Most definitions loosely refer to it as the study of mathematical ideas of non-literate peoples (e.g., Ascher and Ascher, 1997), thereby ignoring or underplaying its profound relationship to culture. More importantly, current definitions are restrictive and too narrow to adequately explain phenomena that rightfully fall within its realm. Providing a conceptually grounded definition is a necessary first step to galvanize the thinking and investigative activity on the subject. My aim in this thesis is to offer such a definition and to descriptively examine its relevance for theory building and research on ethnomathematics. I start with a brief review of the current definitions of ethnomathematics, highlighting their parochial nature. I then propose an over-arching definition that derives its grounding from interaction and reciprocity-based models. My definition suggests ethnomathematics as the study of the evolution of mathematics that has shaped, and in turn shaped by, the values of groups of people. I then use this definition to historically examine how mathematics, despite its universality and constancy themes, suffers from culture-based disparities and has been influenced in its development by various social groups over time. Specifically, I examine the role of culture in the learning and use of math, gender capabilities in math, and how even racism has played a significant part in the evolution of math. Show BibTex@mastersthesis{tracyhammond2001MA, type = {{MA Master's Thesis}}, author = {Hammond, Tracy}, title = {Ethnomathematics: Concept Definition and Research Perspectives}, school = {}, year = {2001}, month = {February}, address = {New York, NY, USA}, note = {Advisor: Ellen Marakowitz. 57 pages.}}

 2017 Leland, Jake. Recognizing Seatbelt-Fastening Activity Using Wearable Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. 43 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Coauthored with Ellen Stanfill. LinkShow Abstract: Many fatal car accidents involve victims who were not wearing a seatbelt, even though systems for detecting such behavior and intervening to correct it already exist. Activity recognition using wearable sensors has been previously applied to many health-related fields with high accuracy. In this paper, activity recognition is used to generate an algorithm for real-time recognition of putting on a seatbelt, using a smartwatch. Initial data was collected from twelve participants to determine the validity of the approach. Novel features were extracted from the data and used to classify the action, with a final accuracy of 1.000 and an F-measure of 1.000 using the MultilayerPerceptron classifier using laboratory collected data. Then, an iterative real-time recognition user study was conducted to investigate classification accuracy in a naturalistic setting. The F-measure of naturalistic classification was 0.825 with MultilayerPerceptron. This work forms the basis for further studies which will aim to provide user feedback to increase seatbelt use. Show BibTex@mastersthesis{jakeleland2017BS, type = {{Undergraduate Honors Thesis}}, author = {Leland, Jake}, title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology}, school = {Texas A\&M University (TAMU)}, year = {2017}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Ellen Stanfill.}} 2017 Stanfill, Ellen. Recognizing Seatbelt-Fastening Activity Using Wearable Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. 43 pages. Texas A&M University (TAMU), College Station, TX, USA. May, 2017. Coauthored with Jake Leland. LinkShow Abstract: Many fatal car accidents involve victims who were not wearing a seatbelt, even though systems for detecting such behavior and intervening to correct it already exist. Activity recognition using wearable sensors has been previously applied to many health-related fields with high accuracy. In this paper, activity recognition is used to generate an algorithm for real-time recognition of putting on a seatbelt, using a smartwatch. Initial data was collected from twelve participants to determine the validity of the approach. Novel features were extracted from the data and used to classify the action, with a final accuracy of 1.000 and an F-measure of 1.000 using the MultilayerPerceptron classifier using laboratory collected data. Then, an iterative real-time recognition user study was conducted to investigate classification accuracy in a naturalistic setting. The F-measure of naturalistic classification was 0.825 with MultilayerPerceptron. This work forms the basis for further studies which will aim to provide user feedback to increase seatbelt use. Show BibTex@mastersthesis{ellenstanfill2017BS, type = {{Undergraduate Honors Thesis}}, author = {Stanfill, Ellen}, title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology}, school = {Texas A\&M University (TAMU)}, year = {2017}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Jake Leland.}} 2016 Brhlik, David. Enhancing Blind Navigation with the Use of Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. Coauthored with Temiloluwa Otuyelu and Chad Young. LinkShow Abstract: The goal of this research is to design and develop a wearable technology that will enable blind and visually impaired users to accomplish regular navigation without the assistance of another person, a guidance animal, or a cane. This new technology will have the distinct advantage of being more discreet and user friendly than an animal or cane, allowing the user to feel more comfortable as they use the device. Extensive research will be performed to determine the best user interface; this includes the location of the sensors on the body and how the device will communicate with the user. Potential devices could be designed to be worn on the shoes, belt, hat, glasses, or any number of other locations. The device may communicate with the wearer using vibrations, pressure, or sound. The best combination of wearability and communication will be built for user testing. This research will enable the visually impaired population to navigate more quickly, easily, and discretely; and help them learn their surroundings. Show BibTex@mastersthesis{davidbrhlik2016BS, type = {{Undergraduate Honors Thesis}}, author = {Brhlik, David}, title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with Temiloluwa Otuyelu and Chad Young.}} 2016 Otuyelu,Temiloluwa. Enhancing Blind Navigation with the Use of Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. Coauthored with David Brhlik and Chad Young. LinkShow Abstract: The goal of this research is to design and develop a wearable technology that will enable blind and visually impaired users to accomplish regular navigation without the assistance of another person, a guidance animal, or a cane. This new technology will have the distinct advantage of being more discreet and user friendly than an animal or cane, allowing the user to feel more comfortable as they use the device. Extensive research will be performed to determine the best user interface; this includes the location of the sensors on the body and how the device will communicate with the user. Potential devices could be designed to be worn on the shoes, belt, hat, glasses, or any number of other locations. The device may communicate with the wearer using vibrations, pressure, or sound. The best combination of wearability and communication will be built for user testing. This research will enable the visually impaired population to navigate more quickly, easily, and discretely; and help them learn their surroundings. Show BibTex@mastersthesis{temiotuyelu2016BS, type = {{Undergraduate Honors Thesis}}, author = {Otuyelu,Temiloluwa}, title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with David Brhlik and Chad Young.}} 2016 Young, Chad. Enhancing Blind Navigation with the Use of Wearable Sensor Technology. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond & Theresa Maldonado. Texas A&M University (TAMU), College Station, TX, USA. May, 2016. Coauthored with Temiloluwa Otuyelu and David Bhrlik. LinkShow Abstract: The goal of this research is to design and develop a wearable technology that will enable blind and visually impaired users to accomplish regular navigation without the assistance of another person, a guidance animal, or a cane. This new technology will have the distinct advantage of being more discreet and user friendly than an animal or cane, allowing the user to feel more comfortable as they use the device. Extensive research will be performed to determine the best user interface; this includes the location of the sensors on the body and how the device will communicate with the user. Potential devices could be designed to be worn on the shoes, belt, hat, glasses, or any number of other locations. The device may communicate with the wearer using vibrations, pressure, or sound. The best combination of wearability and communication will be built for user testing. This research will enable the visually impaired population to navigate more quickly, easily, and discretely; and help them learn their surroundings. Show BibTex@mastersthesis{chadyoung2016BS, type = {{Undergraduate Honors Thesis}}, author = {Young, Chad}, title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology}, school = {Texas A\&M University (TAMU)}, year = {2016}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond \& Theresa Maldonado. Coauthored with Temiloluwa Otuyelu and David Bhrlik.}} 2012 Regmi, Sarin. Haptigo Tactile Navigation System. Texas A&M University (TAMU) Undergraduate Honors Thesis. Advisor: Tracy Hammond. Texas A&M University (TAMU), College Station, TX, USA. May, 2012. LinkShow Abstract: TBD Show BibTex@mastersthesis{sarinregmi2012BS, type = {{Undergraduate Honors Thesis}}, author = {Regmi, Sarin}, title = {Haptigo Tactile Navigation System}, school = {Texas A\&M University (TAMU)}, year = {2012}, month = {May}, address = {College Station, TX, USA}, note = {Advisor: Tracy Hammond. }} 2011 Valentine, Stephanie. A Shape Comparison Technique for Use in Sketch-based Tutoring Systems. St. Mary's University of Minnesota Undergraduate Honors Thesis. Advisor: Ann Smith & Tracy Hammond. St. Mary's University of Minnesota, Winona, MN, USA. May, 2011. First Position: TAMU PhD Student. LinkShow Abstract: To Be Entered Show BibTex@mastersthesis{stephanievalentine2011BS, type = {{Undergraduate Honors Thesis}}, author = {Valentine, Stephanie}, title = {A Shape Comparison Technique for Use in Sketch-based Tutoring Systems}, school = {St. Mary's University of Minnesota}, year = {2011}, month = {May}, address = {Winona, MN, USA}, note = {Advisor: Ann Smith \& Tracy Hammond. }}

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@mastersthesis{stephanievalentine2016PhD,  type = {{PhD Doctoral Dissertation}},  author = {Valentine, Stephanie},  title = {Design, Deployment, Identity, \& Conformity: An Analysis of Children's Online Social Networks},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{folamialamudun2016PhD,  type = {{PhD Doctoral Dissertation}},  author = {Alamudun, Folami},  title = {Analysis of Visuo-cognitive Behavior in Screening Mammography},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{honghoekim2016PhD,  type = {{PhD Doctoral Dissertation}},  author = {Kim, Hong-hoe },  title = {A Fine Motor Skill Classifying Framework to Support Children's Self-regulation Skills and School Readiness},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{manojprasad2014PhD,  type = {{PhD Doctoral Dissertation}},  author = {Prasad, Manoj},  title = {Designing Tactile Interfaces for Abstract Interpersonal Communication, Pedestrian Navigation and Motocyclists Navigation},  school = {Texas A\&M University (TAMU)},  year = {2014},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 183 pages.}}

@mastersthesis{daniellecummings2013PhD,  type = {{PhD Doctoral Dissertation}},  author = {Cummings, Danielle},  title = {Multimodal Interaction for Enhancing Team Coordination on the Battlefield},  school = {Texas A\&M University (TAMU)},  year = {2013},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 201 pages.}}

@mastersthesis{sashikanthdamaraju2013PhD,  type = {{PhD Doctoral Dissertation}},  author = {Sashikanth, Raju; Damaraju, Sriranga},  title = {An Exploration of Multi-touch Interaction Techniques},  school = {Texas A\&M University (TAMU)},  year = {2013},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 145 pages.}}

@mastersthesis{brandonpaulson2010PhD,  type = {{PhD Doctoral Dissertation}},  author = {Paulson, Brandon C.},  title = {Rethinking pen input interaction: Enabling freehand sketching through improved primitive recognition},  school = {Texas A\&M University (TAMU)},  year = {2010},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 217 pages.}}

@mastersthesis{tracyhammond2007PhD,  type = {{PhD Doctoral Dissertation}},  author = {Hammond, Tracy},  title = {LADDER: A Perceptually-Based Language to Simplify Sketch Recognition User Interface Development},  school = {Massachusetts Institute of Technology (MIT)},  year = {2007},  month = {February},   address = {Cambridge, MA, USA},  note = {Advisor: Randall Davis. 495 pages.}}

@mastersthesis{joshcherian2017MS,  type = {{MS Master's Thesis}},  author = {Cherian, Josh},  title = {Recognition of Everyday Activities through Wearable Sensors and Machine Learning},  school = {Texas A\&M University (TAMU)},  year = {2017},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond \& Theresa Maldonado. }}

@mastersthesis{aqibbhat2017MS,  type = {{MS Master's Thesis}},  author = {Bhat, Aqib Niaz},  title = {Sketchography - Automatic grading of map sketches for geography education},  school = {Texas A\&M University (TAMU)},  year = {2017},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{jorgecamara2017MS,  type = {{MS Master's Thesis}},  author = {Herrera Camara, Jorge Ivan},  title = {bFlow2code - From Hand-drawn Flowchart to Code Execution},  school = {Texas A\&M University (TAMU)},  year = {2017},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{nahumvillanueva2017MS,  type = {{MS Master's Thesis}},  author = {Villanueva Luna, Nahum},  title = {ARCaching: Using Augmented Reality on Mobile Devices to Improve Geocacher Experience},  school = {Texas A\&M University (TAMU)},  year = {2017},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{siddharthakarthik2016MS,  type = {{MS Master's Thesis}},  author = {Copesetty, Siddhartha Karthik },  title = {Labeling by Example},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{swarnakeshavabhotla2016MS,  type = {{MS Master's Thesis}},  author = {Keshavabhotla, Swarna},  title = {PerSketchTivity: Recognition and Progressive Learning Analysis},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{shaliniashokkumar2016MS,  type = {{MS Master's Thesis}},  author = {Ashok Kumar, Shalini Priya},  title = {Evaluation of Conceptual Sketches on Stylus-based Devices},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{purnendukaul2016MS,  type = {{MS Master's Thesis}},  author = {Kaul, Purnendu},  title = {Gaze Assisted Classification of On-Screen Tasks (by Difficulty Level) and User Activities (Reading, Writing/Typing, Image-Gazing)},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{jaideepray2016MS,  type = {{MS Master's Thesis}},  author = {Ray, Jaideep},  title = {Finding Similar Sketches},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{shiqiangguo2015MS,  type = {{MS Master's Thesis}},  author = {Guo, Shiqiang (Frank)},  title = {ResuMatcher: A Personalized Resume-Job Matching System},  school = {Texas A\&M University (TAMU)},  year = {2015},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Treacy Hammond. }}

@mastersthesis{zhengliangyin2014MS,  type = {{MS Master's Thesis}},  author = {Yin, Zhangliang},  title = {Chinese Calligraphist: A Sketch Based Learning Tool for Learning Written Chinese},  school = {Texas A\&M University (TAMU)},  year = {2014},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{honghoekim2012MS,  type = {{MS Master's Thesis}},  author = {Kim, Hong-Hoe},  title = {Analysis of Children's Sketches to Improve Recognition Accuracy in Sketch-Based Applications},  school = {Texas A\&M University (TAMU)},  year = {2012},  month = {December},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 105 pages.}}

@mastersthesis{drewlogsdon2012MS,  type = {{MS Master's Thesis}},  author = {Logsdon, Drew},  title = {Arm-Hand-Finger Video Game Interaction},  school = {Texas A\&M University (TAMU)},  year = {2012},  month = {December},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 108 pages.}}

@mastersthesis{georgelucchese2012MS,  type = {{MS Master's Thesis}},  author = {Lucchese, George},  title = {Sketch Recognition on Mobile Devices},  school = {Texas A\&M University (TAMU)},  year = {2012},  month = {December},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 54 pages.}}

@mastersthesis{wenzheli2012MS,  type = {{MS Master's Thesis}},  author = {Li, Wenzhe},  title = {Acoustic Based Sketch Recognition},  school = {Texas A\&M University (TAMU)},  year = {2012},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 91 pages.}}

@mastersthesis{franciscovides2012MS,  type = {{MS Master's Thesis}},  author = {Vides Ceron, Francisco},  title = {TAYouKi: A Sketch-Based Tutoring System for Young Kids},  school = {Texas A\&M University (TAMU)},  year = {2012},  month = {August},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 129 pages.}}

@mastersthesis{paultaele2010MS,  type = {{MS Master's Thesis}},  author = {Taele, Paul},  title = {Freehand Sketch Recognition for Computer-Assisted Language Learning of Written East Asian Languages},  school = {Texas A\&M University (TAMU)},  year = {2010},  month = {December},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 96 pages.}}

@mastersthesis{aaronwolin2010MS,  type = {{MS Master's Thesis}},  author = {Wolin, Aaron},  title = {Segmenting Hand-Drawn Strokes},  school = {Texas A\&M University (TAMU)},  year = {2010},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 160 pages.}}

@mastersthesis{danieldixon2009MS,  type = {{MS Master's Thesis}},  author = {Dixon, Daniel},  title = {A Methodology for Using Assistive Sketch Recognition For Improving a Person’s Ability to Draw},  school = {Texas A\&M University (TAMU)},  year = {2009},  month = {December},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 114 pages.}}

@mastersthesis{tracyhammond2001MA,  type = {{MA Master's Thesis}},  author = {Hammond, Tracy},  title = {Ethnomathematics: Concept Definition and Research Perspectives},  school = {},  year = {2001},  month = {February},   address = {New York, NY, USA},  note = {Advisor: Ellen Marakowitz. 57 pages.}}

@mastersthesis{jakeleland2017BS,  type = {{Undergraduate Honors Thesis}},  author = {Leland, Jake},  title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},  school = {Texas A\&M University (TAMU)},  year = {2017},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Ellen Stanfill.}}

@mastersthesis{ellenstanfill2017BS,  type = {{Undergraduate Honors Thesis}},  author = {Stanfill, Ellen},  title = {Recognizing Seatbelt-Fastening Activity Using Wearable Technology},  school = {Texas A\&M University (TAMU)},  year = {2017},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. 43 pages. Coauthored with Jake Leland.}}

@mastersthesis{davidbrhlik2016BS,  type = {{Undergraduate Honors Thesis}},  author = {Brhlik, David},  title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond \& Theresa Maldonado.  Coauthored with Temiloluwa Otuyelu and Chad Young.}}

@mastersthesis{temiotuyelu2016BS,  type = {{Undergraduate Honors Thesis}},  author = {Otuyelu,Temiloluwa},  title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond \& Theresa Maldonado.  Coauthored with David Brhlik and Chad Young.}}

@mastersthesis{chadyoung2016BS,  type = {{Undergraduate Honors Thesis}},  author = {Young, Chad},  title = {Enhancing Blind Navigation with the Use of Wearable Sensor Technology},  school = {Texas A\&M University (TAMU)},  year = {2016},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond \& Theresa Maldonado.  Coauthored with Temiloluwa Otuyelu and David Bhrlik.}}

@mastersthesis{sarinregmi2012BS,  type = {{Undergraduate Honors Thesis}},  author = {Regmi, Sarin},  title = {Haptigo Tactile Navigation System},  school = {Texas A\&M University (TAMU)},  year = {2012},  month = {May},   address = {College Station, TX, USA},  note = {Advisor: Tracy Hammond. }}

@mastersthesis{stephanievalentine2011BS,  type = {{Undergraduate Honors Thesis}},  author = {Valentine, Stephanie},  title = {A Shape Comparison Technique for Use in Sketch-based Tutoring Systems},  school = {St. Mary's University of Minnesota},  year = {2011},  month = {May},   address = {Winona, MN, USA},  note = {Advisor: Ann Smith \& Tracy Hammond. }}

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\subsection{Dissertations and Theses}\subsubsection{PhD Dissertations}\begin{enumerate}  \item \bibentry{stephanievalentine2016PhD}  \item \bibentry{folamialamudun2016PhD}  \item \bibentry{honghoekim2016PhD}  \item \bibentry{manojprasad2014PhD}  \item \bibentry{daniellecummings2013PhD}  \item \bibentry{sashikanthdamaraju2013PhD}  \item \bibentry{brandonpaulson2010PhD}  \item \bibentry{tracyhammond2007PhD}\end{enumerate}\subsubsection{Master's Theses}\begin{enumerate}  \item \bibentry{joshcherian2017MS}  \item \bibentry{aqibbhat2017MS}  \item \bibentry{jorgecamara2017MS}  \item \bibentry{nahumvillanueva2017MS}  \item \bibentry{siddharthakarthik2016MS}  \item \bibentry{swarnakeshavabhotla2016MS}  \item \bibentry{shaliniashokkumar2016MS}  \item \bibentry{purnendukaul2016MS}  \item \bibentry{jaideepray2016MS}  \item \bibentry{shiqiangguo2015MS}  \item \bibentry{zhengliangyin2014MS}  \item \bibentry{honghoekim2012MS}  \item \bibentry{drewlogsdon2012MS}  \item \bibentry{georgelucchese2012MS}  \item \bibentry{wenzheli2012MS}  \item \bibentry{franciscovides2012MS}  \item \bibentry{paultaele2010MS}  \item \bibentry{aaronwolin2010MS}  \item \bibentry{danieldixon2009MS}  \item \bibentry{tracyhammond2001MA}\end{enumerate}\subsubsection{Undergraduate Honor's Theses}\begin{enumerate}  \item \bibentry{jakeleland2017BS}  \item \bibentry{ellenstanfill2017BS}  \item \bibentry{davidbrhlik2016BS}  \item \bibentry{temiotuyelu2016BS}  \item \bibentry{chadyoung2016BS}  \item \bibentry{sarinregmi2012BS}  \item \bibentry{stephanievalentine2011BS}\end{enumerate}