Chi2022 Silentspeller
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Our full paper for SilentSpeller was accepted to CHI 2022!
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Our full paper for SilentSpeller was accepted to CHI 2022!
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Joined the BCI Society Postdoc and Student Committee to organize an open science initiative for BCI learning resources.
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Our work on SilentSpeller was featured by BuzzFeed and other media outlets.
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My position paper on Passive Haptic Rehearsal was accepted to the Intelligent Music Interfaces workshop at CHI 2022!
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Earned the PURA Travel Award to support my travel to New Orleans for CHI 2022. Thank you UROP!
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Presented on Passive Haptic Rehearsal at Georgia Tech’s Undergraduate Research Symposium and earned the 2nd place Oral Presentation Award!
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Attended CHI 2022 in New Orleans. Excellent discussions and really enjoyed meeting folks at my first in-person HCI conference!
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Started internship at Microsoft Research with the Audio and Acoustics group’s brain-computer interface team!
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Finished my internship project on tongue gesture recognition and back to Georgia Tech. Look forward to sharing our findings at ISWC/UbiComp and beyond!
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Our live demonstration of passive haptic learning earned the Best Demo Award at UbiComp 2022!
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Excited to be a member of the newly-formed Futuring SIGCHI committee!
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Graduated from my Bachelor’s in Computer Science with highest honors. Starting a Master’s degree as part of my BS/MS in January.
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I’ll be presenting my work on combining tongue gestures with gaze tracking at CHI 2023 Interactivity in April. Look forward to seeing everyone in Hamburg.
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Honored to receive the Sigma Xi Best Undergraduate Research Award and Donald V. Jackson Fellowship Award for my research involvement and leadership at Georgia Tech!
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I’ll be starting my PhD at Cornell Tech in NYC in the Fall with a Cornell Fellowship. I’ll be advised by Dr. Tanzeem Choudhury and Dr. Cheng Zhang, researching closed-loop passive interventions and intent-driven interaction.
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As a small high-school team, we competed in the IEEE Robotics and Automation Society’s Humanitarian Robotics and Technologies Challenge by applying machine learning for autonomous mine detection with a metal detector on a low-cost robot platform. We earned 3rd place in the competition and demonstrated robot at ICRA 2017 as a finalist.
organizations competition, machine-learning, robotics code Finalist Award
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I designed and applied a framework for training reinforcement learning models to control rapid-action mobile robots. My team became a finalist from among 100+ teams and earned 11th place at ICRA 2018 as the only high-school team to ever compete in the challenge.
organizations competition, machine-learning, robotics report Finalist Award
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Traditionally, exoplanet discovery relies on expert astronomers or models that integrate astronomy domain knowledge. We applied machine learning and feature extraction techniques to get 80% accuracy for stellar light curves in NASA’s Mikulski Archive, demonstrating that domain knowledge is no longer necessary for discovery of new exoplanets.
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Silent speech systems provide a means for communication to people with movement disabilities like muscular dystrophy while preserving privacy. SilentSpeller is the first-ever silent speech system capable of use with a >1000 word vocabulary while in motion. We made a novel text entry system with capacitive tongue sensing from an oral wearable device to enable a privacy-preserving alternative to speech recognition.
research assistive-technology, subtle-interaction, wearables video CHI'22 paper CHI'21 Interactivity paper UROP Outstanding Oral Presentation Award BuzzFeed
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Estimating hand poses is valuable for gesture interactions and hand tracking but often requires expensive depth cameras. Stereo cameras show multiple perspectives of the hand, allowing depth perception. We created a pipeline for estimating location of hand and finger keypoints with a stereo camera using deep convolutional neural networks.
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Traditionally, computer vision models are trained using large datasets gathered online. We investigated a new method for training unsupervised object recognition models using egocentric computer vision from head-worn displays. In particular, we aimed to classify objects for order picking in warehouses.
research computer-vision, head-worn-displays, machine-learning
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We developed a new brain-computer interface using fNIRS to detect attempted motor movement in different regions of the body. Converting attempted motions to language to enable more versatile communication options for people with movement disabilities. For my undergraduate thesis, I explored how transitional gestures may enable higher accuracy and information transfer with brain-computer interfaces.
research assistive-technology, brain-computer-interfaces, fnirs undergraduate thesis President's Undergraduate Research Award
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Learning piano is difficult, especially for older learners with busy lives. Passive haptic learning can reduce time spent practicing piano through instructional tactile cues. We designed a custom vibrotactile haptic glove for daily wear, enabling faster learning of piano skills. I led a group of undergraduate and graduate students in manufacturing glove hardware, designing a web portal and organizing user studies to evaluate performance.
research haptics, music, wearables video CHI'22 IMI paper UbiComp'22 demo UROP Outstanding Oral Presentation Award UbiComp'22 Best Demo Award
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As part of Neuromatch Academy student collaboration, we investigated working memory activity through N-back tasks with different sequence lengths and stimuli based on the Human Connectome Project’s task-based fMRI data. We localized and characterized prominent regions of activation using GLMs.
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Despite recent attention, Horizon Worlds hasn’t been studied extensively as a social VR platform. Using ethnographic methods, my group studied the online VR community in Horizon Worlds. Based on observational reports and interviews, we found the creative community of world designers to be a prototypical community of practice for designing social VR experiences.
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For elderly people with cognitive impairments, the kitchen can be dangerous. To reduce risks of burns, falling objects and memory lapses in the kitchen, we prototyped an intelligent stovetop appliance and mobile app interface. We conducted a Wizard of Oz study on prototype and collected usability information from interviews, performance, and NASA-TLX.
coursework assistive-technology, ubiquitous-computing slides report
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With the COVID-19 pandemic, online delivery of public health messages has become a critical role for public healthcare. We examined how credibility of public health messages regarding COVID-19 varies across different platforms (Twitter, original website) and source (CDC, Georgia Department of Health, independent academics) in a controlled experiment.
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Tongue gestures are an accessible and subtle method for interacting with wearables but past studies have used custom hardware with a single sensing modality. At Microsoft Research, we used multimodal sensors in a commercial VR headset and EEG headband to build a 50,000 gesture dataset and real-time classifier. We also invented a new interaction method combining tongue and gaze to enable faster gaze-based selection in hands-free interactions.
research head-worn-displays, sensing, subtle-interaction UbiComp'22 poster talk
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The field of brain-computer interfaces (BCIs) is growing rapidly, but there’s a lack of reliable learning resources for students and new researchers. As part of my role in the Postdoc & Student Commitee of the BCI Society, I’m creating a public repository of tutorials for teaching various topics in BCI.
Naoki Kimura, Tan Gemicioglu, Jonathan Womack, Richard Li, Yuhui Zhao, Abdelkareem Bedri, Alex Olwal, Jun Rekimoto, Thad Starner
Published in Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems, 2021
Voice control provides hands-free access to computing, but there are many situations where audible speech is not appropriate. Most unvoiced speech text entry systems can not be used while on-the-go due to movement artifacts. SilentSpeller enables mobile silent texting using a dental retainer with capacitive touch sensors to track tongue movement. Users type by spelling words without voicing. In offline isolated word testing on a 1164-word dictionary, SilentSpeller achieves an average 97% character accuracy. 97% offline accuracy is also achieved on phrases recorded while walking or seated. Live text entry achieves up to 53 words per minute and 90% accuracy, which is competitive with expert text entry on mini-QWERTY keyboards without encumbering the hands.
Tan Gemicioglu, Noah Teuscher, Brahmi Dwivedi, Soobin Park, Emerson Miller, Celeste Mason, Caitlyn Seim, Thad Starner
Published in Intelligent Music Interfaces Workshop at the 2022 CHI Conference on Human Factors in Computing Systems, 2022
Passive haptic learning (PHL) uses vibrotactile stimulation to train piano songs using repetition, even when the recipient of stimulation is focused on other tasks. However, many of the benefits of playing piano cannot be acquired without actively playing the instrument. In this position paper, we posit that passive haptic rehearsal, where active piano practice is assisted by separate sessions of passive stimulation, is of greater everyday use than solely PHL. We propose a study to examine the effects of passive haptic rehearsal for self-paced piano learners and consider how to incorporate passive rehearsal into everyday practice.
Naoki Kimura, Tan Gemicioglu, Jonathan Womack, Richard Li, Yuhui Zhao, Abdelkareem Bedri, Zixiong Su, Alex Olwal, Jun Rekimoto, Thad Starner
Published in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 2022
Speech is inappropriate in many situations, limiting when voice control can be used. Most unvoiced speech text entry systems can not be used while on-the-go due to movement artifacts. Using a dental retainer with capacitive touch sensors, SilentSpeller tracks tongue movement, enabling users to type by spelling words without voicing. SilentSpeller achieves an average 97% character accuracy in offline isolated word testing on a 1164-word dictionary. Walking has little effect on accuracy; average offline character accuracy was roughly equivalent on 107 phrases entered while walking (97.5%) or seated (96.5%). To demonstrate extensibility, the system was tested on 100 unseen words, leading to an average 94% accuracy. Live text entry speeds for seven participants averaged 37 words per minute at 87% accuracy. Comparing silent spelling to current practice suggests that SilentSpeller may be a viable alternative for silent mobile text entry.
Asha Bhandarkar, Tan Gemicioglu, Brahmi Dwivedi, Caitlyn Seim, Thad Starner
Published in Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2022
Passive haptic learning (PHL) is a method for learning piano pieces through repetition of haptic stimuli while a user is focused on other daily tasks. In combination with active practice techniques, this method is a powerful tool for users to learn piano pieces with less time spent in active practice while also reducing cognitive effort and increasing retention. We propose a demo combining these two learning methods, in which attendees will engage in a short active practice session followed by a passive practice session using vibrotactile haptic gloves. Attendees will be able to experience the effects of passive haptic learning for themselves as well as gauge their mastery of the piece with a final performance at the end of the demo.
Tan Gemicioglu, Mike Winters, Yu-Te Wang, Ivan Tashev
Published in Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2022
Head worn displays are often used in situations where users’ hands may be occupied or otherwise unusable due to permanent or situational movement impairments. Hands-free interaction methods like voice recognition and gaze tracking allow accessible interaction with reduced limitations for user ability and environment. Tongue gestures offer an alternative method of private, hands-free and accessible interaction. However, past tongue gesture interfaces come in intrusive or otherwise inconvenient form factors preventing their implementation in head worn displays. We present a multimodal tongue gesture interface using existing commercial headsets and sensors only located in the upper face. We consider design factors for choosing robust and usable tongue gestures, introduce eight gestures based on the criteria and discuss early work towards tongue gesture recognition with the system.
gesture, poster, sensing, subtle-interaction doi paper poster
Tan Gemicioglu, Yuhui Zhao, Melody Jackson, Thad Starner
Published in Proceedings of the 10th International Brain-Computer Interface Meeting, 2023
BCIs using imagined or executed movement enable subjects to communicate by performing gestures in sequential patterns. Conventional interaction methods have a one-to-one mapping between movements and commands but new methods such as BrainBraille have instead used a pseudo-binary encoding where multiple body parts are tensed simultaneously. However, non-invasive BCI modalities such as EEG and fNIRS have limited spatial specificity, and have difficulty distinguishing simultaneous movements. We propose a new method using transitions in gesture sequences to combinatorially increase possible commands without simultaneous movements. We demonstrate the efficacy of transitional gestures in a pilot fNIRS study where accuracy increased from 81% to 92% when distinguishing transitions of two movements instead of two movements independently. We calculate ITR for a potential transitional version of BrainBraille, where ITR would increase from 143bpm to 218bpm.
Tan Gemicioglu, R. Michael Winters, Yu-Te Wang, Thomas M. Gable, Ann Paradiso, Ivan J. Tashev
Published in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, 2023
Gaze tracking allows hands-free and voice-free interaction with computers, and has gained more use recently in virtual and augmented reality headsets. However, it traditionally uses dwell time for selection tasks, which suffers from the Midas Touch problem. Tongue gestures are subtle, accessible and can be sensed non-intrusively using an IMU at the back of the ear, PPG and EEG. We demonstrate a novel interaction method combining gaze tracking with tongue gestures for gaze-based selection faster than dwell time and multiple selection options. We showcase its usage as a point-and-click interface in three hands-free games and a musical instrument.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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