A robust classification scheme for detection of food intake through non-invasive monitoring of chewing
- Resource Type
- Conference
- Authors
- Fontana, Juan M.; Sazonov, Edward S.
- Source
- 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE. :4891-4894 Aug, 2012
- Subject
- Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Accuracy
Support vector machines
Monitoring
Feature extraction
Obesity
Biomedical monitoring
Band pass filters
- Language
- ISSN
- 1557-170X
1094-687X
1558-4615
Automatic methods for food intake detection are needed to objectively monitor ingestive behavior of individuals in a free living environment. In this study, a pattern recognition system was developed for detection of food intake through the classification of jaw motion. A total of 7 subjects participated in laboratory experiments that involved several activities of daily living: talking, walking, reading, resting and food intake while being instrumented with a wearable jaw motion sensor. Inclusion of such activities provided a high variability to the sensor signal and thus challenged the classification task. A forward feature selection process decided on the most appropriate set of features to represent the chewing signal. Linear and RBF Support Vector Machine (SVM) classifiers were evaluated to find the most suitable classifier that can generalize the high variability of the input signal. Results showed that an average accuracy of 90.52% can be obtained using Linear SVM with a time resolution of 15 sec.