In this thesis the primary focus is on the evaluation of biomedical microwave sensor to be used in the muscle analyzer system. Lower muscle quality is one indicator that a patient can have sarcopenia. Therefore the muscle analyzer system can be a tool used in screening for sarcopenia. Sarcopenia is a progressive skeletal muscle disorder that typically affects elderly people. It is characterized by several different things, one of them is that there is an infiltration of fat into the muscle. At microwave frequencies the dielectric properties of fat are vastly different than the muscles. So, this fat infiltration creates a dielectric contrast compared to muscle without this fat infiltration that the sensors aim to detect. The muscle analyzer system is proposed to be a portable device that can be employed in clinics to assess muscle quality. The sensors are evaluated on their ability to distinguish between normal muscle tissue and muscle of lower quality. This is achieved via electromagnetic simulations, clinical trials, where the system is compared against established techniques, and phantom experiments, where artificial tissue emulating materials is used in a laboratory setting to mimick the properties of human tissues. In a initial clinical pilot study the split ring resonator sensor was used, but the results raised concerns over the penetration depth of the sensor. Therefore, three new alternative sensors were designed and evaluated via simulations. Two of the new sensors showed encouraging results, one of which has been fabricated. This sensor was used in a another clinical study.This study only had data from 4 patients, 8 measurements in total, meaning it was hard to draw any conclusions from it. The sensors used in the clinical setting as well as another were evaluated in the phantom experiments. Those experiments were exploratory because a wider frequency range was used, although some problems in the experiments were found. A secondary approach in this thesis is devoted to a data-driven approach, where a microwave sensor is simulated. The data from it is simulated and used to train a neural network to predict the dielectric properties of materials. The network predicts these properties with relatively high accuracy. However, this approach is currently limited to simulations only. Several ideas on how to improve this approach and extend it to measurements is given.