{"ID":2844959,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.05292","arxiv_id":"2511.05292","title":"What's on Your Plate? Inferring Chinese Cuisine Intake from Wearable IMUs","abstract":"Accurate food intake detection is vital for dietary monitoring and chronic disease prevention. Traditional self-report methods are prone to recall bias, while camera-based approaches raise concerns about privacy. Furthermore, existing wearable-based methods primarily focus on a limited number of food types, such as hamburgers and pizza, failing to address the vast diversity of Chinese cuisine. To bridge this gap, we propose CuisineSense, a system that classifies Chinese food types by integrating hand motion cues from a smartwatch with head dynamics from smart glasses. To filter out irrelevant daily activities, we design a two-stage detection pipeline. The first stage identifies eating states by distinguishing characteristic temporal patterns from non-eating behaviors. The second stage then conducts fine-grained food type recognition based on the motions captured during food intake. To evaluate CuisineSense, we construct a dataset comprising 27.5 hours of IMU recordings across 11 food categories and 10 participants. Experiments demonstrate that CuisineSense achieves high accuracy in both eating state detection and food classification, offering a practical solution for unobtrusive, wearable-based dietary monitoring.The system code is publicly available at https://github.com/joeeeeyin/CuisineSense.git.","short_abstract":"Accurate food intake detection is vital for dietary monitoring and chronic disease prevention. Traditional self-report methods are prone to recall bias, while camera-based approaches raise concerns about privacy. Furthermore, existing wearable-based methods primarily focus on a limited number of food types, such as ham...","url_abs":"https://arxiv.org/abs/2511.05292","url_pdf":"https://arxiv.org/pdf/2511.05292v1","authors":"[\"Jiaxi Yin\",\"Pengcheng Wang\",\"Han Ding\",\"Fei Wang\"]","published":"2025-11-07T14:54:37Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":607332,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2844959,"paper_url":"https://arxiv.org/abs/2511.05292","paper_title":"What's on Your Plate? Inferring Chinese Cuisine Intake from Wearable IMUs","repo_url":"https://github.com/joeeeeyin/CuisineSense.git","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}