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Step Length Estimation for Blind Walkers
Fatemeh Elyasi1, Roberto Manduchi1
1University of California, Santa Cruz, USA.
Summary
Machine learning models for estimating step length in blind pedestrians need retraining. Models trained on sighted walkers perform poorly; retraining with blind walker data significantly improves accuracy for better mobility independence.
Area of Science:
- Robotics
- Human-Computer Interaction
- Machine Learning
Background:
- Smartphone inertial data aids pedestrian dead-reckoning (PDR) for blind pedestrian mobility.
- Accurate step length estimation is crucial for PDR localization.
- Previous step length prediction models were trained exclusively on sighted individuals.
Purpose of the Study:
- To evaluate the performance of existing step length estimation models on blind pedestrians.
- To develop and validate a retrained model for improved step length prediction in blind individuals.
Main Methods:
- Collected inertial data from blind pedestrians using smartphones.
- Trained and tested machine learning models for step length estimation.
- Compared model performance using data from sighted versus blind walkers.
Main Results:
- Step length estimation models trained on sighted walkers showed poor accuracy when applied to blind walkers.
- Retraining the models with data from blind walkers significantly enhanced prediction accuracy.
- The study highlights gait differences impacting PDR performance.
Conclusions:
- Existing step length estimation models require adaptation for blind pedestrians.
- Retraining machine learning models with specific user group data is essential for accurate PDR.
- Improved PDR accuracy can enhance mobility independence for blind individuals.
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