Wearable devices equipped with inertial and magnetic sensors are increasingly being employed for the monitoring and assessment of physical activity. The affordability, accuracy, and portability of such devices enable real-time monitoring of human movement during exercise, without requiring direct supervision by a trained professional. Furthermore, the data collected by these devices can be analyzed over time, providing valuable insights into an individual's patterns of physical activity and progress. While the initial interest focused mainly on aerobic exercises, research has recently begun to focus on strength exercises as well. The challenges researchers are facing with are primarily related to the minimization of the number and position of wearable devices on individual human body segments and the development of algorithms that will provide appropriate assessment and feedback. Addressing existing limitations and aiming for simplicity, generalizability and reliability, this thesis presents a comprehensive procedure for quantitative and qualitative monitoring of exercise performance, along with a measurement method for assessing human movement variability and movement execution metrics. The proposed method enables the detection, counting, and segmentation of repetitive movements during strength exercises without requiring prior knowledge of the specific exercise being performed. Additionally, the newly developed movement execution metric can identify incorrect repetitions during strength training exercises and distinguishing between experienced and inexperienced individuals. The scientific contributions are: Measurement method of human movement variability during exercise using sensor nodes with inertial and magnetic sensors, Movement execution metrics during exercise based on measurements using sensor nodes with inertial and magnetic sensors, Procedure for quantitative and qualitative monitoring of exercise performance using sensor nodes with inertial and magnetic sensors and measurement of heart rate.