Chunzhuo Wang

Chunzhuo Wang, T. Sunil Kumar, Walter De Raedt et al.

Unhealthy dietary habits are considered as the primary cause of various chronic diseases, including obesity and diabetes. The automatic food intake monitoring system has the potential to improve the quality of life (QoL) of people with diet-related diseases through dietary assessment. In this work, we propose a novel contactless radar-based approach for food intake monitoring. Specifically, a Frequency Modulated Continuous Wave (FMCW) radar sensor is employed to recognize fine-grained eating and drinking gestures. The fine-grained eating/drinking gesture contains a series of movements from raising the hand to the mouth until putting away the hand from the mouth. A 3D temporal convolutional network with self-attention (3D-TCN-Att) is developed to detect and segment eating and drinking gestures in meal sessions by processing the Range-Doppler Cube (RD Cube). Unlike previous radar-based research, this work collects data in continuous meal sessions (more realistic scenarios). We create a public dataset comprising 70 meal sessions (4,132 eating gestures and 893 drinking gestures) from 70 participants with a total duration of 1,155 minutes. Four eating styles (fork & knife, chopsticks, spoon, hand) are included in this dataset. To validate the performance of the proposed approach, seven-fold cross-validation method is applied. The 3D-TCN-Att model achieves a segmental F1-score of 0.896 and 0.868 for eating and drinking gestures, respectively. The results of the proposed approach indicate the feasibility of using radar for fine-grained eating and drinking gesture detection and segmentation in meal sessions.

Chunzhuo Wang, Hans Hallez, Bart Vanrumste

Automated food intake gesture detection plays a vital role in dietary monitoring, enabling objective and continuous tracking of eating behaviors to support better health outcomes. Wrist-worn inertial measurement units (IMUs) have been widely used for this task with promising results. More recently, contactless radar sensors have also shown potential. This study explores whether combining wearable and contactless sensing modalities through multimodal learning can further improve detection performance. We also address a major challenge in multimodal learning: reduced robustness when one modality is missing. To this end, we propose a robust multimodal temporal convolutional network with cross-modal attention (MM-TCN-CMA), designed to integrate IMU and radar data, enhance gesture detection, and maintain performance under missing modality conditions. A new dataset comprising 52 meal sessions (3,050 eating gestures and 797 drinking gestures) from 52 participants is developed and made publicly available. Experimental results show that the proposed framework improves the segmental F1-score by 4.3% and 5.2% over unimodal Radar and IMU models, respectively. Under missing modality scenarios, the framework still achieves gains of 1.3% and 2.4% for missing radar and missing IMU inputs. This is the first study to demonstrate a robust multimodal learning framework that effectively fuses IMU and radar data for food intake gesture detection.

Chunzhuo Wang, Zhewen Xue, T. Sunil Kumar et al.

Overweight and obesity have emerged as widespread societal challenges, frequently linked to unhealthy eating patterns. A promising approach to enhance dietary monitoring in everyday life involves automated detection of food intake gestures. This study introduces a skeleton based approach using a model that combines a dilated spatial-temporal graph convolutional network (ST-GCN) with a bidirectional long-short-term memory (BiLSTM) framework, as called ST-GCN-BiLSTM, to detect intake gestures. The skeleton-based method provides key benefits, including environmental robustness, reduced data dependency, and enhanced privacy preservation. Two datasets were employed for model validation. The OREBA dataset, which consists of laboratory-recorded videos, achieved segmental F1-scores of 86.18% and 74.84% for identifying eating and drinking gestures. Additionally, a self-collected dataset using smartphone recordings in more adaptable experimental conditions was evaluated with the model trained on OREBA, yielding F1-scores of 85.40% and 67.80% for detecting eating and drinking gestures. The results not only confirm the feasibility of utilizing skeleton data for intake gesture detection but also highlight the robustness of the proposed approach in cross-dataset validation.