Human body motion capture systems based on inertial sensors (gyroscopes andaccelerometers) are able to track the relative motions in the body precisely, oftenwith the aid of supplementary sensors. The sensor measurements are combinedthrough a sensor fusion algorithm to create estimates of, among other parame-ters, position, velocity and orientation for each body segment. As this algorithmrequires integration of noisy measurements, some drift, especially in the positionestimate, is expected. Taking advantage of the knowledge about the tracked sub-ject, a human body, models have been developed that improve the estimates, butposition still displays drift over time.In this thesis, a GNSS receiver is added to the motion capture system to givea drift-free measurement of the position as well as a velocity measurement. Theinertial data and the GNSS data complements each other well, particularly interms of observability of global and relative motions. To enable the models of thehuman body at an early stage of the fusion of sensor data, an optimization basedmaximum a posteriori algorithm was used, which is also better suited for thenonlinear system tracked compared to the conventional method of using Kalmanfilters.One of the models that improves the position estimate greatly, without addingadditional sensing, is the contact detection, with which the velocity of a segmentis set to zero whenever it is considered stationary in comparison to the surround-ing environment, e.g. when a foot touches the ground. This thesis looks at botha scenario when this contact detection can be applied and a scenario where itcannot be applied, to see what possibilities an addition of GNSS sensor couldbring to the human body motion tracking case. The results display a notable im-provement in position, both with and without contact detection. Furthermore,the heading estimate is improved at a full-body scale and the solution makes theestimates depend less on acceleration bias estimation.These results show great potential for more accurate estimates outdoors andcould prove valuable for enabling motion tracking of scenarios where the contactdetection model cannot be used, such as e.g. biking.