DC Field | Value | Language |
---|---|---|
dc.contributor.author | Shin, Jaemin | ko |
dc.contributor.author | Lee, Seungjoo | ko |
dc.contributor.author | Gong, Taesik | ko |
dc.contributor.author | Yoon, Hyungjun | ko |
dc.contributor.author | Roh, Hyunchul | ko |
dc.contributor.author | Bianchi, Andrea | ko |
dc.contributor.author | Lee, Sung-Ju | ko |
dc.date.accessioned | 2022-09-29T08:00:21Z | - |
dc.date.available | 2022-09-29T08:00:21Z | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.created | 2022-09-27 | - |
dc.date.issued | 2022-05 | - |
dc.identifier.citation | Conference on Human Factors in Computing Systems, CHI 2022 | - |
dc.identifier.uri | http://hdl.handle.net/10203/298778 | - |
dc.description.abstract | Various automated eating detection wearables have been proposed to monitor food intakes. While these systems overcome the forgetfulness of manual user journaling, they typically show low accuracy at outside-the-lab environments or have intrusive form-factors (e.g., headgear). Eyeglasses are emerging as a socially-acceptable eating detection wearable, but existing approaches require custom-built frames and consume large power. We propose MyDJ, an eating detection system that could be attached to any eyeglass frame. MyDJ achieves accurate and energy-efficient eating detection by capturing complementary chewing signals on a piezoelectric sensor and an accelerometer. We evaluated the accuracy and wearability of MyDJ with 30 subjects in uncontrolled environments, where six subjects attached MyDJ on their own eyeglasses for a week. Our study shows that MyDJ achieves 0.919 F1-score in eating episode coverage, with 4.03 × battery time over the state-of-the-art systems. In addition, participants reported wearing MyDJ was almost as comfortable (94.95%) as wearing regular eyeglasses. | - |
dc.language | English | - |
dc.publisher | Association for Computing Machinery | - |
dc.title | MyDJ: Sensing Food Intakes with an Atachable on Your Eyeglass Frame | - |
dc.type | Conference | - |
dc.identifier.wosid | 000890212503046 | - |
dc.identifier.scopusid | 2-s2.0-85130574523 | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | Conference on Human Factors in Computing Systems, CHI 2022 | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Virtual | - |
dc.identifier.doi | 10.1145/3491102.3502041 | - |
dc.contributor.localauthor | Bianchi, Andrea | - |
dc.contributor.localauthor | Lee, Sung-Ju | - |
dc.contributor.nonIdAuthor | Lee, Seungjoo | - |
dc.contributor.nonIdAuthor | Yoon, Hyungjun | - |
dc.contributor.nonIdAuthor | Roh, Hyunchul | - |
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