Tracking food intake provides a valuable source of information to gain insights in dietary habits for the health industry. Currently, the main method to track food intake to is do it manually. To take a step towards tracking food intake manually, this these aims to answer the following research question: ”How to detect chewing episodes with an IMU sensor around the ear?”. The IMU collect x, y, z axis data for the accerelometer an gyrscope. The data of the axis was down sampled to 20Hz and passed through a low-pass filter. Chewing detecting was done by determining chewing episode in time win- dows of 30 seconds. Feature were extracted from those time windows. Then random forest, decision tree, k-neareast neigbours, support vector machine and logistic regression machine learning models were used to classify the data, for which f1-scored higher than 0.8 have been achieved. Therefore it can be concluded is it possible to detect chewing episodes with a single IMU around the ear. Feature selection and performance analysis has been done, and it seem to be that features that use auto correlation and Fast Fourier Transform (FFT), play a significant role increasing the classification performance. They are computationally expensive and are not ideal for embedded system. However there is room for finding features that are less computationally expensive. ... Read more