S1601-1-3 Meal Sound Analysis Using Artificial Neural Network

In recent years, an increasing number of people suffer from lifestyle diseases, such as metabolic syndrome. Eating habits monitoring is an important parameter for life-style diseases prevention. Since, we have been developing a system to monitor meal time activities. Our system consists of two bone conduction microphones connected to a portable IC recorder that collects internal body sound data. In the meal time activities differentiation process, we adopted a wavelet function for the feature extraction. We extracted 70 feature vectors from coefficients of discrete wavelet transformation, then we selected the optimal feature vectors set using minimal-redundancy-maximal-relevance criterion (mRMR), and finally we used probabilistic neural network (PNN) to classify meal-related activities. Experiments were carried on sound data from six persons. Our model proved to achieve better classification accuracy, and selected features to be independent from individual differences.