When a patient is in a hospital, it is very important to monitor their vital signs. Doctors and nurses use this information to assess the condition of the patient. Most of the existing vital signs measurement devices need physical contact with the patient. This thesis focuses on a non-contact vital signs estimation method. Using a mmWave radar, one or more persons in the view of the sensor are being monitored. This monitoring consists of finding the chest region of a person, and monitoring this chest for vibrations. These vibrations are caused by breathing in and out, and the beating of the heart. Using signal processing, these vibrations can be converted to a heart rate and a respiration rate.

This thesis is about getting insight in the already available options regarding vital signs monitoring, programming the Texas Instruments IWR6843ISK mmWave radar module to estimate the vital signs of multiple persons and validating this project against trusted vital signs monitors.

The implemented solution which followed from this project is able to track multiple persons inside the radar view, and is able to measure the vital signs for up to four persons in real-time. The mean accuracy gained for one person heart rate estimation is 10.8%, the mean accuracy gained for one person respiration rate estimation is 7.6%. The mean observed accuracy for multiple person heart rate estimation is 13.4%, the multiple person respiration rate mean accuracy is 10.6%.

Items being misplaced in warehouses easily get lost. To combat this, warehouses have to send people in scanning all barcodes in the warehouse. This is highly inefficient, which is why Eonics wants to build a drone handling this. There are options out there to scan barcodes, but none of them match the requirements laid out by Eonics. Among these requirements are a lightweight camera, such as a GoPro, and a recording distance of 1.5-2 metres. This report will look and see if these requirements are feasible. Techniques used in this report are Mathematical Morphology, Maximally Stable Extremal Regions, Convolutional Neural Networks, Gradiental Difference and Direction Estimation with Region Extraction. The report concludes in stating that interpreting the barcodes is not possible with mere software under these requirements. The maximal distance we were able to interpret barcodes from, based on a 4K image, was around 1 metre. Continuing the trend, we would need at least an 8K camera to detect from a distance of 1.5 metres. Detection however, is less difficult and is feasible from a distance of 1.5-2 metres. The report also derives an function to use to calculate the maximum distance a barcode can be interpreted from, based on the details of the barcode and camera. Finally, research is done regarding using hardware solutions, such as a zoom-lens, which has promising results.