Multimodality Sensing for Eating Recognition. Merck, C., Maher, C., Mirtchouk, M., Zheng, M., Huang, Y., & Kleinberg, S. In Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare, 2016. ACM Press. Website abstract bibtex While many sensors can monitor physical activity, there is
no device that can unobtrusively measure eating at the same
level of detail. Yet, tracking and reacting to food consumption
is key to managing many chronic diseases such as obesity
and diabetes. Eating recognition has primarily used a
single sensor at a time in a constrained environment but
sensors may fail and each may pick up different types of eating.
We present a multi-modality study of eating recognition,
which combines head and wrist motion (Google Glass,
smartwatches on each wrist), with audio (custom earbud microphone).
We collect 72 hours of data from 6 participants
wearing all sensors and eating an unrestricted set of foods,
and annotate video recordings to obtain ground truth. Using
our noise cancellation method, audio sensing alone achieved
92% precision and 89% recall in finding meals, while motion
sensing was needed to find individual intakes.
@inProceedings{
title = {Multimodality Sensing for Eating Recognition},
type = {inProceedings},
year = {2016},
identifiers = {[object Object]},
keywords = {audio,auracle,cs200,cs200-reading,imu,in-ear-mic,intake-detection,wrist},
websites = {http://www.skleinberg.org/papers/kleinberg_pervasive16.pdf},
publisher = {ACM Press},
id = {7ea74123-1e08-3d4e-9a4c-783f7e9df8e4},
created = {2018-07-12T21:31:36.559Z},
file_attached = {false},
profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
last_modified = {2018-07-12T21:31:36.559Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {merck:multi16},
source_type = {inproceedings},
notes = {Strength (1) Well-designed study and annotation process (2) 72 hours of data with an unrestricted set of food Weakness (1) ACE dataset is not completely released (2) The study was done in the laboratory (3) There is no definition of meal (4) The information of food diversity is not provided (5) The contribution of motion sensors is not clear enough},
private_publication = {false},
abstract = {While many sensors can monitor physical activity, there is
no device that can unobtrusively measure eating at the same
level of detail. Yet, tracking and reacting to food consumption
is key to managing many chronic diseases such as obesity
and diabetes. Eating recognition has primarily used a
single sensor at a time in a constrained environment but
sensors may fail and each may pick up different types of eating.
We present a multi-modality study of eating recognition,
which combines head and wrist motion (Google Glass,
smartwatches on each wrist), with audio (custom earbud microphone).
We collect 72 hours of data from 6 participants
wearing all sensors and eating an unrestricted set of foods,
and annotate video recordings to obtain ground truth. Using
our noise cancellation method, audio sensing alone achieved
92% precision and 89% recall in finding meals, while motion
sensing was needed to find individual intakes.},
bibtype = {inProceedings},
author = {Merck, Christopher and Maher, Christina and Mirtchouk, Mark and Zheng, Min and Huang, Yuxiao and Kleinberg, Samantha},
booktitle = {Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare}
}
Downloads: 0
{"_id":"w7DqHGPHZGvP3Lkvv","bibbaseid":"merck-maher-mirtchouk-zheng-huang-kleinberg-multimodalitysensingforeatingrecognition-2016","downloads":0,"creationDate":"2019-02-15T15:14:59.464Z","title":"Multimodality Sensing for Eating Recognition","author_short":["Merck, C.","Maher, C.","Mirtchouk, M.","Zheng, M.","Huang, Y.","Kleinberg, S."],"year":2016,"bibtype":"inProceedings","biburl":null,"bibdata":{"title":"Multimodality Sensing for Eating Recognition","type":"inProceedings","year":"2016","identifiers":"[object Object]","keywords":"audio,auracle,cs200,cs200-reading,imu,in-ear-mic,intake-detection,wrist","websites":"http://www.skleinberg.org/papers/kleinberg_pervasive16.pdf","publisher":"ACM Press","id":"7ea74123-1e08-3d4e-9a4c-783f7e9df8e4","created":"2018-07-12T21:31:36.559Z","file_attached":false,"profile_id":"f954d000-ce94-3da6-bd26-b983145a920f","group_id":"b0b145a3-980e-3ad7-a16f-c93918c606ed","last_modified":"2018-07-12T21:31:36.559Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"merck:multi16","source_type":"inproceedings","notes":"Strength (1) Well-designed study and annotation process (2) 72 hours of data with an unrestricted set of food Weakness (1) ACE dataset is not completely released (2) The study was done in the laboratory (3) There is no definition of meal (4) The information of food diversity is not provided (5) The contribution of motion sensors is not clear enough","private_publication":false,"abstract":"While many sensors can monitor physical activity, there is\nno device that can unobtrusively measure eating at the same\nlevel of detail. Yet, tracking and reacting to food consumption\nis key to managing many chronic diseases such as obesity\nand diabetes. Eating recognition has primarily used a\nsingle sensor at a time in a constrained environment but\nsensors may fail and each may pick up different types of eating.\nWe present a multi-modality study of eating recognition,\nwhich combines head and wrist motion (Google Glass,\nsmartwatches on each wrist), with audio (custom earbud microphone).\nWe collect 72 hours of data from 6 participants\nwearing all sensors and eating an unrestricted set of foods,\nand annotate video recordings to obtain ground truth. Using\nour noise cancellation method, audio sensing alone achieved\n92% precision and 89% recall in finding meals, while motion\nsensing was needed to find individual intakes.","bibtype":"inProceedings","author":"Merck, Christopher and Maher, Christina and Mirtchouk, Mark and Zheng, Min and Huang, Yuxiao and Kleinberg, Samantha","booktitle":"Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare","bibtex":"@inProceedings{\n title = {Multimodality Sensing for Eating Recognition},\n type = {inProceedings},\n year = {2016},\n identifiers = {[object Object]},\n keywords = {audio,auracle,cs200,cs200-reading,imu,in-ear-mic,intake-detection,wrist},\n websites = {http://www.skleinberg.org/papers/kleinberg_pervasive16.pdf},\n publisher = {ACM Press},\n id = {7ea74123-1e08-3d4e-9a4c-783f7e9df8e4},\n created = {2018-07-12T21:31:36.559Z},\n file_attached = {false},\n profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},\n group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},\n last_modified = {2018-07-12T21:31:36.559Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {merck:multi16},\n source_type = {inproceedings},\n notes = {Strength (1) Well-designed study and annotation process (2) 72 hours of data with an unrestricted set of food Weakness (1) ACE dataset is not completely released (2) The study was done in the laboratory (3) There is no definition of meal (4) The information of food diversity is not provided (5) The contribution of motion sensors is not clear enough},\n private_publication = {false},\n abstract = {While many sensors can monitor physical activity, there is\nno device that can unobtrusively measure eating at the same\nlevel of detail. Yet, tracking and reacting to food consumption\nis key to managing many chronic diseases such as obesity\nand diabetes. Eating recognition has primarily used a\nsingle sensor at a time in a constrained environment but\nsensors may fail and each may pick up different types of eating.\nWe present a multi-modality study of eating recognition,\nwhich combines head and wrist motion (Google Glass,\nsmartwatches on each wrist), with audio (custom earbud microphone).\nWe collect 72 hours of data from 6 participants\nwearing all sensors and eating an unrestricted set of foods,\nand annotate video recordings to obtain ground truth. Using\nour noise cancellation method, audio sensing alone achieved\n92% precision and 89% recall in finding meals, while motion\nsensing was needed to find individual intakes.},\n bibtype = {inProceedings},\n author = {Merck, Christopher and Maher, Christina and Mirtchouk, Mark and Zheng, Min and Huang, Yuxiao and Kleinberg, Samantha},\n booktitle = {Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare}\n}","author_short":["Merck, C.","Maher, C.","Mirtchouk, M.","Zheng, M.","Huang, Y.","Kleinberg, S."],"urls":{"Website":"http://www.skleinberg.org/papers/kleinberg_pervasive16.pdf"},"bibbaseid":"merck-maher-mirtchouk-zheng-huang-kleinberg-multimodalitysensingforeatingrecognition-2016","role":"author","keyword":["audio","auracle","cs200","cs200-reading","imu","in-ear-mic","intake-detection","wrist"],"downloads":0},"search_terms":["multimodality","sensing","eating","recognition","merck","maher","mirtchouk","zheng","huang","kleinberg"],"keywords":["audio","auracle","cs200","cs200-reading","imu","in-ear-mic","intake-detection","wrist"],"authorIDs":[]}