Body Mass Index (BMI) Of Normal and Overweight/Obese Individuals Based on Speech Signals

Conventional method for measuring Body Mass Index (BMI) for individuals are using calibrated weight scale and measuring tape. However, there are certain cases in which the conventional method of measuring the BMI is not accessible. Thus, this experiment was proposed to overcome the problem using speech approach. In order to develop an effective BMI measuring system, speech signals of 30 subjects were recorded using a microphone. The dimension of the speech signal was reduced by extracting the relevant features using LPCC, MFCC and WPT based energy and entropy features. Lastly, both kNearest Neighbour (kNN) and Probabilistic Neural Network (PNN) were used to measure the BMI of an individual. The kNN classifier (97.50%) gives promising accuracy compared to PNN classifier (96.33%).