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Step length estimation using handheld inertial sensors.

Valérie Renaudin¹, Melania Susi, Gérard Lachapelle

¹PLAN Group, Schulich School of Engineering, The University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada. gerard.lachapelle@ucalgary.ca

Sensors (Basel, Switzerland)

|September 27, 2012

Summary

A new step length model uses handheld Micro Electrical Mechanical System (MEMS) sensors to estimate distance by combining step frequency and height. This method achieves low error rates, comparable to body-fixed sensors.

Keywords:

IMU biomechanics dead reckoning handheld devices pedestrian navigation step length

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Area of Science:

Biomedical Engineering
Wearable Technology
Human Motion Analysis

Background:

Accurate step length estimation is crucial for various applications, including gait analysis and navigation.
Existing methods often rely on body-fixed sensors, limiting user convenience.
Handheld sensors offer a more accessible and less intrusive approach to motion tracking.

Purpose of the Study:

To introduce a novel step length estimation model utilizing handheld Micro Electrical Mechanical System (MEMS) sensors.
To investigate the feasibility of using handheld MEMS for reliable step frequency and length calculation.
To compare the accuracy of the proposed model with existing literature for body-fixed sensors.

Main Methods:

Developed a step length model integrating user's step frequency and height with three key parameters.
Employed the Short Time Fourier Transform (STFT) to extract frequency content from handheld sensor signals independently.
Analyzed the relationship between step and hand frequencies across different hand motions and carrying modes using synchronized sensor data.

Main Results:

The model successfully estimated step length by combining step frequency and height.
Field tests with 10 subjects demonstrated low error percentages (2.5-5%) in travelled distance.
The accuracy achieved is comparable to models utilizing body-fixed sensors.

Conclusions:

A novel and accurate step length model using handheld MEMS sensors has been developed.
The proposed method provides a viable alternative to body-fixed sensors for pedestrian navigation and gait analysis.
Further research can explore optimizing parameters for enhanced accuracy across diverse user groups and activities.