Pedestrian navigation in body-worn devices is usually based on global navigation satellite systems (GNSS), which is a sufficient solution in most outdoor applications. Pedestrian navigation indoors is much more challenging. Further, GNSS does not provide any specific information about the gait style or how the device is carried. This thesis presents three contributions for how to learn human gait parameters for improved dead-reckoning indoors, and to classify the gait style and how the device is carried, all supported with extensive test data.

The first contribution of this thesis is a novel approach to support pedestrian navigation in situations when GNSS is not available. A novel filtering approach, based on a multi-rate Kalman filter bank, is employed to learn the human gait parameters when GNSS is available using data from an inertial measurement unit (IMU). In a typical indoor-outdoor navigation application, the gait parameters are learned outdoors and then used to improve the pedestrian navigation indoors using dead-reckoning methods. The performance of the proposed method is evaluated with both simulated and experimental data.

Secondly, an approach for estimating a unique gait signature from the inertial measurements provided by IMU-equipped handheld devices is proposed. The gait signatures, defined as one full cycle of the human gait, are obtained for multiple human motion modes and device carrying poses. Then, a parametric model of each signature, using Fourier series expansion, is computed. This provides a low-dimensional feature vector that can be used in medical diagnosis of certain physical or neurological diseases, or for a generic classification service outlined below.

The third contribution concerns joint motion mode and device pose classification using the set of features described above. The features are extracted from the received IMU gait measurement and the computed gait signature. A classification framework is presented which includes standard classifiers, e.g. Gaussian process and neural network, with an additional smoothing stage based on hidden Markov model.

There seems to be a lack of publicly available data sets in these kind of applications. The extensive datasets developed in this work, primarily for performance evaluation, have been documented and published separately. In the largest dataset, several users with four body-worn devices and 17 body-mounted IMUs performed a large number of repetitive experiments, with special attention to get well annotated data with ground truth position, motion mode and device pose.