TY - JOUR
T1 - Machine Learning-Based Unobtrusive Intake Gesture Detection via Wearable Inertial Sensors
AU - Jlailaty, Hussein Al
AU - Celik, Abdulkadir
AU - Mansour, Mohammad M.
AU - Eltawil, Ahmed
N1 - KAUST Repository Item: Exported on 2022-10-29
Acknowledgements: The authors gratefully acknowledge support from KAUST and the Smart Health Initiative at KAUST. We would like to thank Dr. Nabil Wasily for the valuable comments and feedback he gave throughout our various meetings and discussions
PY - 2022/10/25
Y1 - 2022/10/25
N2 - Dietary patterns can be the primary reason for many chronic diseases such as diabetes and obesity. State-of-the-art wearable sensor technologies can play a critical role in assisting patients in managing their eating habits by providing meaningful statistics on critical parameters such as the onset, duration, and frequency of eating. For an accurate yet fast food intake recognition, this work presents a novel Machine Learning (ML) based framework that shows promising results by leveraging optimized support vector machine (SVM) classifiers. The SVM classifiers are trained on three comprehensive datasets: OREBA, FIC, and CLEMSON. The developed framework outperforms existing algorithms by achieving F1-scores of 92%, 94%, 95%, and 85% on OREBA-SHA, OREBA-DIS, FIC, and CLEMSON datasets, respectively. In order to assess the generalization aspects, the proposed SVM framework is also trained on one of the three databases while being tested on the others and achieves acceptable F1-scores in all cases. The proposed algorithm is well suited for real-time applications since inference is made using a few support vector parameters compared to thousands in peer deep neural networks models.
AB - Dietary patterns can be the primary reason for many chronic diseases such as diabetes and obesity. State-of-the-art wearable sensor technologies can play a critical role in assisting patients in managing their eating habits by providing meaningful statistics on critical parameters such as the onset, duration, and frequency of eating. For an accurate yet fast food intake recognition, this work presents a novel Machine Learning (ML) based framework that shows promising results by leveraging optimized support vector machine (SVM) classifiers. The SVM classifiers are trained on three comprehensive datasets: OREBA, FIC, and CLEMSON. The developed framework outperforms existing algorithms by achieving F1-scores of 92%, 94%, 95%, and 85% on OREBA-SHA, OREBA-DIS, FIC, and CLEMSON datasets, respectively. In order to assess the generalization aspects, the proposed SVM framework is also trained on one of the three databases while being tested on the others and achieves acceptable F1-scores in all cases. The proposed algorithm is well suited for real-time applications since inference is made using a few support vector parameters compared to thousands in peer deep neural networks models.
UR - http://hdl.handle.net/10754/685242
UR - https://ieeexplore.ieee.org/document/9929436/
U2 - 10.1109/TBME.2022.3217196
DO - 10.1109/TBME.2022.3217196
M3 - Article
C2 - 36282827
SN - 1558-2531
JO - IEEE Transactions on Biomedical Engineering
JF - IEEE Transactions on Biomedical Engineering
ER -
