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Kasebzadeh, Parinaz
Publications (3 of 3) Show all publications
Kasebzadeh, P. (2019). Learning Human Gait. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Learning Human Gait
2019 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

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.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 141
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2012
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-159760 (URN)10.3384/diss.diva-159760 (DOI)9789175190143 (ISBN)
Public defence
2019-09-27, Planck, House B, Campus Valla, Linköping, 10:15 (English)
Supervisors
Available from: 2019-08-20 Created: 2019-08-20 Last updated: 2019-09-18Bibliographically approved
Kasebzadeh, P., Fritsche, C., Hendeby, G., Gunnarsson, F. & Gustafsson, F. (2016). Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning. In: Proceedings of the 19th International Conference on Information Fusion: . Paper presented at 19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016 (pp. 379-385). IEEE conference proceedings
Open this publication in new window or tab >>Improved Pedestrian Dead Reckoning Positioning With Gait Parameter Learning
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2016 (English)In: Proceedings of the 19th International Conference on Information Fusion, IEEE conference proceedings, 2016, , p. 7p. 379-385Conference paper, Published paper (Refereed)
Abstract [en]

We consider personal navigation systems in devices equipped with inertial sensors and GPS, where we propose an improved Pedestrian Dead Reckoning (PDR) algorithm that learns gait parameters in time intervals when position estimates are available, for instance from GPS or an indoor positioning system (IPS). A novel filtering approach is proposed that is able to learn internal gait parameters in the PDR algorithm, such as the step length and the step detection threshold. Our approach is based on a multi-rate Kalman filter bank that estimates the gait parameters when position measurements are available, which improves PDR in time intervals when the position is not available, for instance when passing from outdoor to indoor environments where IPS is not available. The effectiveness of the new approach is illustrated on several real world experiments. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2016. p. 7
National Category
Signal Processing Control Engineering
Identifiers
urn:nbn:se:liu:diva-130174 (URN)000391273400052 ()978-0-9964527-4-8 (ISBN)
Conference
19th International Conference on Information Fusion (FUSION), Heidelberg, Germany, July 5-8 2016
Funder
EU, FP7, Seventh Framework Programme, 607400
Available from: 2016-07-13 Created: 2016-07-13 Last updated: 2017-02-03Bibliographically approved
Kasebzadeh, P., Fritsche, C., Özkan, E., Gunnarsson, F. & Gustafsson, F. (2015). Joint Antenna and Propagation Model Parameter Estimation using RSS measurements. In: : . Paper presented at 18th International Conference on Information Fusion, Washington D.C, USA, July 6-9, 2015 (pp. 98-103). IEEE
Open this publication in new window or tab >>Joint Antenna and Propagation Model Parameter Estimation using RSS measurements
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2015 (English)Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a semi-parametric model for RSS measurements is introduced that can be used to predict coverage in cellular radio networks. The model is composed of an empirical log-distance model and a deterministic antenna gain model that accounts for possible non-uniform base station antenna radiation. A least-squares estimator is proposed to jointly estimate the path loss and antenna gain model parameters. Simulation as well as experimental results verify the efficacy of this approach. The method can provide improved accuracy compared to conventional path loss based estimation methods. 

Place, publisher, year, edition, pages
IEEE, 2015
National Category
Signal Processing
Identifiers
urn:nbn:se:liu:diva-120962 (URN)9780982443866 (ISBN)
Conference
18th International Conference on Information Fusion, Washington D.C, USA, July 6-9, 2015
Funder
EU, European Research Council
Note

©2016 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Available from: 2015-09-01 Created: 2015-09-01 Last updated: 2016-01-11Bibliographically approved
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