Even though much research has been conducted on body worn sensors, little research has been performed on specialized (non-ear-worn) headworn sensors. These sensors could be used to perform a variety of health related research. Therefore, this thesis focusses on a new low-power specialized (non-ear-worn) headworn sensor platform and explores how it can be improved.

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.