Inertial sensors and magnetometers are abundant in today's society, where they can be found in many of our everyday electronic devices, such as smart phones or smart watches. Their primary function is to measure the movement and orientation of the device and provide this information for the apps that request it.

This licenciate thesis explores the use of these types of sensors in biomedical applications. Specifically, how these sensors can be used to analyze human movement and work as a tool for assessment of human balance and movement disorders. The methods presented in this thesis deal with mathematical modeling of the sensors, their relationship to the biomechanical models that are used to describe the dynamics of human movement and how we can combine these models to describe the mechanisms behind human balance and quantify the symptoms of movement disorders.

The main contributions come in the form of four papers. A practical calibration method for accelerometers is presented in Paper I, that deals with compensation of intrinsic sensor errors that are common for relatively cheap sensors that are used in e.g. smart phones. In Paper II we present an experimental evaluation and minor extension of methods that are used to determine the position of the joints in the biomecanical model, using inertial sensor data alone. Paper III deals with system identification of nonlinear controllers operating in closed loop, which is a method that can be used to model the neuromuscular control mechanisms behind human balance. In Paper IV we propose a novel method for quantification of hand tremor, a primary symptom of neurological disorders such as Parkinson's disease (PD) or Essential tremor (ET), where we make use of data collected from sensors in a smart phone. The thesis also contains an introduction to the sensors, biomechanical modeling, neuromuscular control and the various estimation and modeling techniques that are used throughout the thesis.