A Comparative Study of Food Intake Detection Using Artificial Neural Network and Support Vector Machine
- Resource Type
- Conference
- Authors
- Farooq, Muhammad; Fontana, Juan M.; Boateng, Akua F.; Mccrory, Megan A.; Sazonov, Edward
- Source
- 2013 12th International Conference on Machine Learning and Applications Machine Learning and Applications (ICMLA), 2013 12th International Conference on. 1:153-153 Dec, 2013
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Artificial neural networks
Support vector machines
Accuracy
Neurons
Kernel
Monitoring
Frequency-domain analysis
Food intake detection
Neural Net
SVM
chewing
eating disorder
wearable sensors
- Language
In Machine Learning applications, the selection of the classification algorithm depends on the problem at hand. This paper provides a comparison of the performance of the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) for food intake detection. A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were used to train both types of classifiers. Data were collected from 12 subjects in free-living for a period of 24-hrs under unrestricted conditions. ANN with a different number of hidden layer neurons and SVMs with different kernels were trained using a leave one out cross validation scheme. ANN achieved an average accuracy of 86.86 ± 6.5 % whereas SVM (with linear kernel) achieved an average classification accuracy of 81.93 ± 9.22 %. Data collected from an independent subject in a separate study were used to evaluate the performance of these classifiers in-terms of the number of meals detected per day resulting in an accuracy of 72.72% for ANN and 63.63% for SVM. The results suggest that ANN may perform better than SVM for this specific problem.