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Adaptive Inertial Sensor-Based Step Length Estimation Model.
Melanija Vezočnik1, Matjaz B Juric1
1Faculty of Computer and Information Science, University of Ljubljana, Večna Pot 113, 1000 Ljubljana, Slovenia.
Sensors (Basel, Switzerland)
|December 11, 2022
Summary
A new step length estimation model uses smartphone sensors to track walking. This model offers improved accuracy and is unaffected by walking speed or phone orientation, outperforming existing methods.
Area of Science:
- Biomechanics
- Sensor Technology
- Human Motion Analysis
Background:
- Pedestrian dead reckoning (PDR) relies on inertial sensors for step length estimation.
- Existing smartphone-based models often neglect human body kinematics and measured step lengths.
Purpose of the Study:
- To introduce a novel step length estimation model utilizing acceleration magnitude and step frequency.
- To develop a model grounded in the spatial positions of human anatomical landmarks during walking.
Main Methods:
- Derivation of the model using optical tracking of anatomical landmarks.
- Evaluation on a publicly available dataset with treadmill and test polygon walking modes.
- Comparison against existing step length estimation models.
Main Results:
- The proposed model achieved a 5.64 cm Mean Absolute Error (MAE) on a treadmill.
- It demonstrated a 4.55% mean walked distance error on a test polygon.
- The model outperformed all compared methods and is robust to walking speed and smartphone orientation.
Conclusions:
- The new model presents a promising alternative for step length estimation in PDR applications.
- Its accuracy and independence from external factors enhance its practical utility.
- The model integrates human kinematics with sensor data for superior performance.
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