What's on Your Plate? Inferring Chinese Cuisine Intake from Wearable IMUs

TL;DR

CuisineSense is a wearable system that accurately classifies Chinese food types by analyzing hand and head motion cues, addressing privacy concerns and the diversity of Chinese cuisine for unobtrusive dietary monitoring.

Contribution

The paper introduces CuisineSense, a novel two-stage system combining smartwatch and smart glasses data for fine-grained Chinese food classification.

Findings

High accuracy in eating state detection

Effective classification across 11 Chinese food categories

Constructed a comprehensive 27.5-hour dataset

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.