Food Consumption Detection Through Hand Movements Using Smartwatch Sensors and Deep Learning Approaches
2
Issued Date
2025-01-01
Resource Type
ISSN
23673370
eISSN
23673389
Scopus ID
2-s2.0-105021004151
Journal Title
Lecture Notes in Networks and Systems
Volume
1553 LNNS
Start Page
273
End Page
285
Rights Holder(s)
SCOPUS
Bibliographic Citation
Lecture Notes in Networks and Systems Vol.1553 LNNS (2025) , 273-285
Suggested Citation
Hnoohom N., Mekruksavanich S., Jitpattanakul A. Food Consumption Detection Through Hand Movements Using Smartwatch Sensors and Deep Learning Approaches. Lecture Notes in Networks and Systems Vol.1553 LNNS (2025) , 273-285. 285. doi:10.1007/978-3-031-99958-1_20 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/113060
Title
Food Consumption Detection Through Hand Movements Using Smartwatch Sensors and Deep Learning Approaches
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
Tracking food intake is crucial for maintaining well-being and managing various medical conditions. Traditional methods like manual records or food journals are often cumbersome and error-prone. This research introduces a novel method for identifying food intake by analyzing hand motions recorded by wristwatch sensors using deep learning algorithms. Data was collected from 51 participants wearing smartwatches with accelerometer and gyroscope sensors during eating and non-eating activities. The raw sensor data was pre-processed and segmented into defined-length windows, from which significant features were extracted. Several advanced deep learning models, including CNN and a hybrid CNN-ResBiLSTM model, were developed and trained to classify the sensor data into food consumption and nonconsumption activities. The models were evaluated using accuracy, loss, and F1-score metrics with 5-fold cross-validation. The CNN-ResBiLSTM model achieved the highest accuracy, 96.67% without and 98.05% with data augmentation using SMOTE, outperforming other baseline models. These results suggest that wristwatch sensors combined with deep learning algorithms offer a discreet and efficient method for tracking food intake, which could aid in monitoring and improving eating habits for various health purposes. This study contributes to the growing field of automated dietary monitoring, highlighting the potential of wearable technology and AI in promoting healthy eating behaviors.