A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing
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 f...
Saved in:
| Published in | Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) Vol. 2015; p. 1029 |
|---|---|
| Main Authors | , , |
| Format | Journal Article Conference Proceeding |
| Language | English |
| Published |
United States
01.09.2015
|
| Subjects | |
| Online Access | Get full text |
| DOI | 10.1145/2750858.2807545 |
Cover
| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Abowd, Gregory D Thomaz, Edison Essa, Irfan |
| Author_xml | – sequence: 1 givenname: Edison surname: Thomaz fullname: Thomaz, Edison organization: School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA – sequence: 2 givenname: Irfan surname: Essa fullname: Essa, Irfan organization: School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA – sequence: 3 givenname: Gregory D surname: Abowd fullname: Abowd, Gregory D organization: School of Interactive Computing, Georgia Institute of Technology, Atlanta, Georgia, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29520397$$D View this record in MEDLINE/PubMed |
| BookMark | eNo9kMtqwzAURLVo6SPturuiH3AqXz0sL01I20BCSx9kaWRZTgS2bGSZkH59HeJ2NZuZA2du0YVrnUHoISbzOGb8CRJOJJdzkCThjF-ha0g5EJomN2ib4XevdLBa1TjrOt8qvcdV6_GH0e3O2R_rdnipwik2bWNc6PHBhj3eetuHaNMOLpgSr5zxwY6MT-P6sXuHLitV9-Z-yhn6fl5-LV6j9dvLapGtI8UohKjiYERRlpQDpwwIoZrolJYaNCiTSBCGcUmFTERBmEiFJExRTUUCMZDU0BkiZ-7gOnU8qLrOO28b5Y95TPKTfj7p55P-OHk8T7qhaEz53_87hf4CUAhbrg |
| ContentType | Journal Article Conference Proceeding |
| DBID | NPM ADTOC UNPAY |
| DOI | 10.1145/2750858.2807545 |
| DatabaseName | PubMed Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | PubMed |
| DatabaseTitleList | PubMed |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| ExternalDocumentID | oai:pubmedcentral.nih.gov:5839104 29520397 |
| Genre | Journal Article |
| GrantInformation_xml | – fundername: NIBIB NIH HHS grantid: U54 EB020404 |
| GroupedDBID | NPM ADTOC UNPAY |
| ID | FETCH-LOGICAL-a432t-f52e6bdd3525342003c0c93dc2c2ae7826e45836876b04696804a3c36721209e3 |
| IEDL.DBID | UNPAY |
| IngestDate | Sun Oct 26 04:10:01 EDT 2025 Thu Jan 02 22:39:23 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | false |
| IsScholarly | false |
| Keywords | Automated Dietary Assessment Activity recognition Inertial Sensors Food Journaling Dietary Intake |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-a432t-f52e6bdd3525342003c0c93dc2c2ae7826e45836876b04696804a3c36721209e3 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=http://doi.org/10.1145/2750858.2807545 |
| PMID | 29520397 |
| ParticipantIDs | unpaywall_primary_10_1145_2750858_2807545 pubmed_primary_29520397 |
| PublicationCentury | 2000 |
| PublicationDate | 20150901 |
| PublicationDateYYYYMMDD | 2015-09-01 |
| PublicationDate_xml | – month: 9 year: 2015 text: 20150901 day: 1 |
| PublicationDecade | 2010 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) |
| PublicationTitleAlternate | Proc ACM Int Conf Ubiquitous Comput |
| PublicationYear | 2015 |
| Score | 2.1052673 |
| Snippet | Recognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed... |
| SourceID | unpaywall pubmed |
| SourceType | Open Access Repository Index Database |
| StartPage | 1029 |
| Title | A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing |
| URI | https://www.ncbi.nlm.nih.gov/pubmed/29520397 http://doi.org/10.1145/2750858.2807545 |
| UnpaywallVersion | submittedVersion |
| Volume | 2015 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV27TsMwFL2i7QATIAqUR-WBhSEl9SvJWCFKhdSqAxVlivwKA2laQaqKfj12kz4kkBBTBt_Buop9z7XPOQa4WXmyJ7Y7UQnRHg2E8iSXiRdKV16ptl2XEyf3B7w3ok9jNt4SZHev79uU3Tn38ZCFLefZYmt9BWqcWcxdhdpoMOy8ll49v0TuFJT9eTYTXwuRpjuVo3sIj2v9TUEYeW_Nc9lSy592jH9M6gjqW3UeGm7qzjHsmewEXjqoFDyJFK2NwpFFpKikCC1tLHIA0X4m05WwDblDWLRwy9ybuDcjjEZOC2gXfYo-HbM9e6vDqPvwfN_zykcTPEEJzr2EYcOl1s7mlFBHPVO-iohWWGFhLB7gxl2VcrsLStcb89CngijCbSuI_ciQU6hm08ycAwqoaispA57IkGqfRHZzFEIrgzVXlMkGnBU5jmeFM0aMI4ZtXNCA203SN4OFBJrFZfriMn0X_4i9hAMLWFjB8bqCav4xN9cWFOSyCZXBsN8s_4tvmjeyPA |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV09T8MwED1BO8AEiALlSx5YGFxSfyUZK0SpkKg6UFGmyF9hIE0rSFXRX4_dhLYSSIgpg2-wTrHvnf3eM8DV0pM9dd2JTqnBLJQaK6FSHClfXplxXZcXJz_2RW_IHkZ8tCbIbl7ftxm_8e7jEY9a3rPF1fptqAvuMHcN6sP-oPNSefX8ErlRUHZm-VR-zmWWbVSO7h7cf-tvSsLIW2tWqJZe_LRj_GNS-9BYq_PQYFV3DmDL5ofw3EGV4Elm6NsoHDlEiiqK0MLFIg8Q3Wc8WQrbkD-ERXO_zPHYvxlhDfJaQLfoM_Thme35awOG3bun2x6uHk3AklFS4JQTK5Qx3uaUMk8904GOqdFEE2kdHhDWX5UKtwsq3xuLKGCSaipcK0iC2NIjqOWT3J4ACplua6VCkaqImYDGbnOU0mhLjNCMqyYclzlOpqUzRkJiTlxc2ITrVdJXg6UEmidV-pIqfaf_iD2DXQdYeMnxOoda8T6zFw4UFOqy-iO-AHIEsTA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=Proceedings+of+the+2015+ACM+International+Joint+Conference+on+Pervasive+and+Ubiquitous+Computing&rft.atitle=A+practical+approach+for+recognizing+eating+moments+with+wrist-mounted+inertial+sensing&rft.date=2015-09-01&rft_id=info:doi/10.1145%2F2750858.2807545&rft.externalDocID=oai%3Apubmedcentral.nih.gov%3A5839104 |