Person:

Abowd, Gregory D.

Permanent Link

https://hdl.handle.net/1853/71042

Associated Organization(s)

Organizational Unit

School of Interactive Computing

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Now showing 1 - 10 of 64

Ignorance is Bliss: A Retrospective On My Career at Georgia Tech

( 2021-02-11) Abowd, Gregory D.

On July 15, 1994, I began my career on the faculty in the College of Computing at Georgia Tech. Throughout my career, I have cherished the over half a dozen opportunities I have had to give GVU Brown Bag talks on various research activities. My time as full-time faculty at Georgia Tech ends at the end of February 2021, and I will begin a new chapter of my career as the Dean of Engineering at Northeastern University in Boston. I would like to reflect on the 26+ years I have spent at Georgia Tech, the College of Computing, and the GVU Center and try to explain why I think this place is so special. In thinking about a theme for this talk, I was reminded that my career has been a series of shifting research agendas, each one inspired by some life events. In all cases, I was buoyed by a bevy of talented and supportive colleagues and students who gave me the courage to jump into a research topic that I didn’t know much about. That “ignorance” has allowed me to be more fearless that I had the right to be. As I jump into my next career, for which I am also blissfully ignorant, I hope I am lucky enough to be surrounded by excellence that inspires success.
Using digital technologies to support pandemic response on campus: A case study in the opportunities and challenges of WiFi

( 2020-08-27) Abowd, Gregory D.

Our campus operations were abruptly shut down on March 13, 2020 due to Covid-19, and the campus has not been the same ever since. This has impacted our educational and research mission at Georgia Tech. On the bright side, it has activated a number of collaborative efforts to help Georgia Tech prepare itself for re-opening safely. Whether or not we are successful this Fall 2020 semester, our efforts now will undoubtedly be useful for the future. Everyone has heard about the practice of contact tracing now, and the mad rush for digital solutions to fight against the spread of infectious disease. The CampusLife effort in the School of Interactive Computing (Profs. Abowd, Plötz and De Choudhury) found an opportunity to pivot our research in this direction We are exploring the opportunity to support manual practices of contact tracing with information from the campus wireless network infrastructure. I will give an overview of this effort and report on progress to date. This is very much a work in progress, but it demonstrates some important lessons for all of the GVU community. First, solutions to real problems involves lots of different skills sets and perspectives. Second, there is very interesting balance between public health and privacy, a conversation I hope to engage our community as a way of determining potential solutions.
Tools for Measuring and Understanding the Proximity of Users to Their Smartphones

(Georgia Institute of Technology, 2020-03) Park, Jung Wook ; Evans, Hayley I. ; Watson, Hue L. ; Abowd, Gregory D. ; Arriaga, Rosa I.

Two studies in ubiquitous computing examined the proximity of users to their smartphones in 2006 and in 2011. Both studies have used a passive data collection tool and the day reconstruction method. Additionally, Dey at al. adopted an online survey to validate their findings with a larger population sample. In 2019, we attempted to revisit this research topic due to the high adoption rate of smartphone and smart- watch. In our replication study, we developed a new passive data collection tool and a novel survey technique, proximity-based ecological momentary assessments. We also adopted the day reconstruction method and online survey utilized in the previous studies. This technical report presents the details of the research tools and techniques used in our study. This technical report is a supplementary material to the published article, "Growing Apart: How SmartDevices Impact the Proximity of Users to Their Smartphones", in IEEE Pervasive Computing.
Leveraging Context to Support Automated Food Recognition in Restaurants

(Georgia Institute of Technology, 2015-01) Bettadapura, Vinay ; Thomaz, Edison ; Parnam, Aman ; Abowd, Gregory D. ; Essa, Irfan

The pervasiveness of mobile cameras has resulted in a dramatic increase in food photos, which are pictures re- flecting what people eat. In this paper, we study how tak- ing pictures of what we eat in restaurants can be used for the purpose of automating food journaling. We propose to leverage the context of where the picture was taken, with ad- ditional information about the restaurant, available online, coupled with state-of-the-art computer vision techniques to recognize the food being consumed. To this end, we demon- strate image-based recognition of foods eaten in restaurants by training a classifier with images from restaurant’s on- line menu databases. We evaluate the performance of our system in unconstrained, real-world settings with food im- ages taken in 10 restaurants across 5 different types of food (American, Indian, Italian, Mexican and Thai).
A Practical Approach for Recognizing Eating Moments With Wrist-Mounted Inertial Sensing

(Georgia Institute of Technology, 2015) Thomaz, Edison ; Essa, Irfan ; Abowd, Gregory D.

Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.
Inferring Meal Eating Activities in Real World Settings from Ambient Sounds: A Feasibility Study

(Georgia Institute of Technology, 2015) Thomaz, Edison ; Zhang, Cheng ; Essa, Irfan ; Abowd, Gregory D.

Dietary self-monitoring has been shown to be an effective method for weight-loss, but it remains an onerous task despite recent advances in food journaling systems. Semi-automated food journaling can reduce the effort of logging, but often requires that eating activities be detected automatically. In this work we describe results from a feasibility study conducted in-the-wild where eating activities were inferred from ambient sounds captured with a wrist-mounted device; twenty participants wore the device during one day for an average of 5 hours while performing normal everyday activities. Our system was able to identify meal eating with an F-score of 79.8% in a person-dependent evaluation, and with 86.6% accuracy in a person-independent evaluation. Our approach is intended to be practical, leveraging off-the-shelf devices with audio sensing capabilities in contrast to systems for automated dietary assessment based on specialized sensors.
Predicting Daily Activities From Egocentric Images Using Deep Learning

(Georgia Institute of Technology, 2015) Castro, Daniel ; Hickson, Steven ; Bettadapura, Vinay ; Thomaz, Edison ; Abowd, Gregory D. ; Christensen, Henrik I. ; Essa, Irfan

We present a method to analyze images taken from a passive egocentric wearable camera along with the contextual information, such as time and day of week, to learn and predict everyday activities of an individual. We collected a dataset of 40,103 egocentric images over a 6 month period with 19 activity classes and demonstrate the benefit of state-of-the-art deep learning techniques for learning and predicting daily activities. Classification is conducted using a Convolutional Neural Network (CNN) with a classification method we introduce called a late fusion ensemble. This late fusion ensemble incorporates relevant contextual information and increases our classification accuracy. Our technique achieves an overall accuracy of 83.07% in predicting a person's activity across the 19 activity classes. We also demonstrate some promising results from two additional users by fine-tuning the classifier with one day of training data.
Feasibility of Identifying Eating Moments from First-Person Images Leveraging Human Computation

(Georgia Institute of Technology, 2013-11) Thomaz, Edison ; Parnami, Aman ; Essa, Irfan ; Abowd, Gregory D.

There is widespread agreement in the medical research community that more effective mechanisms for dietary assessment and food journaling are needed to fight back against obesity and other nutrition-related diseases. However, it is presently not possible to automatically capture and objectively assess an individual’s eating behavior. Currently used dietary assessment and journaling approaches have several limitations; they pose a significant burden on individuals and are often not detailed or accurate enough. In this paper, we describe an approach where we leverage human computation to identify eating moments in first-person point-of-view images taken with wearable cameras. Recognizing eating moments is a key first step both in terms of automating dietary assessment and building systems that help individuals reflect on their diet. In a feasibility study with 5 participants over 3 days, where 17,575 images were collected in total, our method was able to recognize eating moments with 89.68% accuracy.
Decoding Children’s Social Behavior

(Georgia Institute of Technology, 2013-06) Rehg, James M. ; Abowd, Gregory D. ; Rozga, Agata ; Romero, Mario ; Clements, Mark A. ; Sclaroff, Stan ; Essa, Irfan ; Ousley, Opal Y. ; Li, Yin ; Kim, Chanho ; Rao, Hrishikesh ; Kim, Jonathan C. ; Presti, Liliana Lo ; Zhang, Jianming ; Lantsman, Denis ; Bidwell, Jonathan ; Ye, Zhefan

We introduce a new problem domain for activity recognition: the analysis of children’s social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1–2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3–5 minute child-adult interaction. In each session, the adult examiner followed a semistructured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition.
Recognizing Water-Based Activities in the Home Through Infrastructure-Mediated Sensing

(Georgia Institute of Technology, 2012-09) Thomaz, Edison ; Bettadapura, Vinay ; Reyes, Gabriel ; Sandesh, Megha ; Schindler, Grant ; Plötz, Thomas ; Abowd, Gregory D. ; Essa, Irfan

Activity recognition in the home has been long recognized as the foundation for many desirable applications in fields such as home automation, sustainability, and healthcare. However, building a practical home activity monitoring system remains a challenge. Striking a balance between cost, privacy, ease of installation and scalability continues to be an elusive goal. In this paper, we explore infrastructure-mediated sensing combined with a vector space model learning approach as the basis of an activity recognition system for the home. We examine the performance of our single-sensor water-based system in recognizing eleven high-level activities in the kitchen and bathroom, such as cooking and shaving. Results from two studies show that our system can estimate activities with overall accuracy of 82.69% for one individual and 70.11% for a group of 23 participants. As far as we know, our work is the first to employ infrastructure-mediated sensing for inferring high-level human activities in a home setting.