Online Resource
Linkopings Universitet
UID:
almahu_9949460350802882
Edition:
1st ed.
ISBN:
9789175190143
Content:
This dissertation by Parinaz Kasebzadeh focuses on the study of human gait patterns to enhance pedestrian navigation, particularly in environments where GNSS is unavailable. The work introduces innovative methods using a multi-rate Kalman filter bank and inertial measurement units (IMUs) to classify gait styles and improve positioning indoors. It addresses the challenges of pedestrian dead reckoning and the classification of motion modes and device poses. The research includes extensive datasets for performance evaluation and has applications in medical diagnostics, particularly for detecting neurological disorders through gait analysis. The intended audience includes researchers and practitioners in electrical engineering and technology fields.
Note:
Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgments -- Contents -- Notation -- Introduction -- Background -- Pedestrian Dead Reckoning Positioning -- Statistical Machine Learning -- Applications -- Gait Parameter Estimation -- IMU Dataset For Motion and Device Mode -- Asynchronous Averaging of Gait Cycles -- Joint Pedestrian Motion State and Device Pose Classification -- Conclusion and Future Work -- Summary and Future Work -- Bibliography
Language:
English
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