The use of wearable health monitoring systems has increased in recent years, with commercial devices such as smart watches. Their functionality continues to expand, enabling users to monitor their own physiological data. However, challenges for creating a head-worn wearable for long-term monitoring lie in power consumption and memory capacity, while remaining small, comfortable, and sustainable.

The conclusion of this work showcases that the designed embedded system is able to perform continuous monitoring during human body downtime thanks to the introduced data processing pipeline and low power consumption, providing the groundwork for comfortable continuous long-term monitoring.

Embedded wearable systems increasingly enable the continuous monitoring of physiological signals in everyday environments. In this work, we use a new on-body embedded sensing system to gather accelerometer and barometer signals to estimate two physiological signals in the head-area.

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.