TY - GEN
T1 - Unintrusive eating recognition using Google Glass
AU - Rahman, Shah Atiqur
AU - Merck, Christopher
AU - Huang, Yuxiao
AU - Kleinberg, Samantha
N1 - Publisher Copyright:
© 2015 ICST.
PY - 2015/12/8
Y1 - 2015/12/8
N2 - 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.
AB - 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.
KW - activity recognition
KW - eating
KW - sensor data
UR - http://www.scopus.com/inward/record.url?scp=84963775573&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84963775573&partnerID=8YFLogxK
U2 - 10.4108/icst.pervasivehealth.2015.259044
DO - 10.4108/icst.pervasivehealth.2015.259044
M3 - Conference contribution
AN - SCOPUS:84963775573
T3 - Proceedings of the 2015 9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015
SP - 108
EP - 111
BT - Proceedings of the 2015 9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015
T2 - 9th International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2015
Y2 - 20 May 2015 through 23 May 2015
ER -