Thesis DefenseFriday, December 9
Title: Recognition of Everyday Activities through Wearable Sensors and Machine Learning
Josh Cherian
10 AM Friday, December 9, 2016
Teague 326
Abstract
Over the past several years, the use of wearable devices has increased dramatically, largely due to their increasingly smaller and more personal form factors, greater sensor reliability, and increasing utility and affordability. This has helped many people live healthier lives and achieve their personal fitness goals, as they are able to quantifiably and graphically see the results of their efforts every step of the way. While these systems work well within the fitness domain, they have yet to achieve a convincing level of functionality in the larger domain of healthcare.To facilitate the increased use of wearable devices to aid in healthcare, we present a two tier recognition system for identifying health activities in real time based on accelerometer data. To do this we run a series of users studies to collect data for six everyday activities: brushing one's teeth, combing one's hair, scratching one's chin, washing one's hands, taking medication, and drinking, achieving an f-measure of 0.85 when identifying these activities in a controlled setting. To evaluate our recognition system's ability to recognize activities in a naturalistic setting, we identify instances of brushing teeth over the course of a day. We initially achieve an f-measure of 0.68; however we are able to improve this to 0.85 by proposing and extracting several novel features. Through recognition of these activities, we aim to encourage the use of wearable devices for everyday personal health management.
Biography
Josh Cherian is a MS candidate at Texas A&M University in the department of Electrical Engineering working under Dr. Tracy Hammond in the Sketch Recognition Lab. Josh completed his BS in Biomedical Engineering from Georgia Institute of Technology. His primary research interest is activity recognition, with a specific focus on recognizing daily health activities such as brushing one's teeth, washing one's hands, and taking medication.
Abstract
Over the past several years, the use of wearable devices has increased dramatically, largely due to their increasingly smaller and more personal form factors, greater sensor reliability, and increasing utility and affordability. This has helped many people live healthier lives and achieve their personal fitness goals, as they are able to quantifiably and graphically see the results of their efforts every step of the way. While these systems work well within the fitness domain, they have yet to achieve a convincing level of functionality in the larger domain of healthcare.To facilitate the increased use of wearable devices to aid in healthcare, we present a two tier recognition system for identifying health activities in real time based on accelerometer data. To do this we run a series of users studies to collect data for six everyday activities: brushing one's teeth, combing one's hair, scratching one's chin, washing one's hands, taking medication, and drinking, achieving an f-measure of 0.85 when identifying these activities in a controlled setting. To evaluate our recognition system's ability to recognize activities in a naturalistic setting, we identify instances of brushing teeth over the course of a day. We initially achieve an f-measure of 0.68; however we are able to improve this to 0.85 by proposing and extracting several novel features. Through recognition of these activities, we aim to encourage the use of wearable devices for everyday personal health management.
Biography
Josh Cherian is a MS candidate at Texas A&M University in the department of Electrical Engineering working under Dr. Tracy Hammond in the Sketch Recognition Lab. Josh completed his BS in Biomedical Engineering from Georgia Institute of Technology. His primary research interest is activity recognition, with a specific focus on recognizing daily health activities such as brushing one's teeth, washing one's hands, and taking medication.
Advisor: Dr.
Tracy Hammond
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