Research Article
Unintrusive Eating Recognition using Google Glass
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2015.259044, author={Shah Atiqur Rahman and Christopher Merck and Yuxiao Huang and Samantha Kleinberg}, title={Unintrusive Eating Recognition using Google Glass}, proceedings={9th International Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2015}, month={8}, keywords={eating activity recognition sensor data}, doi={10.4108/icst.pervasivehealth.2015.259044} }- Shah Atiqur Rahman
Christopher Merck
Yuxiao Huang
Samantha Kleinberg
Year: 2015
Unintrusive Eating Recognition using Google Glass
PERVASIVEHEALTH
ICST
DOI: 10.4108/icst.pervasivehealth.2015.259044
Abstract
Activity recognition has many health applications, from helping individuals track meals and exercise to providing treatment reminders to people with chronic illness and improving closed-loop control of diabetes. While eating is one of the most fundamental health-related activities, it has proven difficult to recognize accurately and unobtrusively. Body-worn and environmental sensors lack the needed specificity, while acoustic and accelerometer sensors worn around the neck may be intrusive and uncomfortable. We propose a new approach to identifying eating based on head movement data from Google Glass. We develop the Glass Eating and Motion (GLEAM) dataset using sensor data collected from 38 participants conducting a series of activities including eating. We demonstrate that head movement data are sufficient to allow recognition of eating with high precision and minimal impact on privacy and comfort.
