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Robust and adaptive terrain classification and gait event detection system
Usman Qamar Shaikh1,2, Muhammad Shahzaib2, Sadia Shakil2,3
1Institute of Biomedical Technologies, Auckland University of Technology, Auckland, New Zealand.
Heliyon
|November 29, 2023
Summary
This study introduces a real-time gait event detection system using inertial sensors for diverse terrains. It accurately identifies terrain types and key gait events, enabling portable rehabilitation applications.
Area of Science:
- Biomechanics
- Wearable technology
- Sensor systems
Background:
- Real-time gait event detection (GED) is crucial for gait analysis and fitness tracking.
- Current GED systems face challenges in diverse and uncontrolled terrains.
- Accurate detection of gait events across varied surfaces remains an open research problem.
Purpose of the Study:
- To develop a real-time, inertial sensor-based gait event detection system.
- To enable robust GED across diverse terrains, including flat ground and stairs.
- To create a computationally efficient system for portable applications.
Main Methods:
- Utilized an inertial sensor-based approach for data acquisition.
- Developed algorithms for real-time classification of three terrain types: flat-walk, stair-ascend, and stair-descend.
- Implemented and validated the system on a low-cost microcontroller.
Main Results:
- Achieved an average terrain classification accuracy of 99%.
- Successfully detected key gait events: toe-strike, heel-rise, toe-off, and heel-strike.
- Demonstrated real-time performance and computational efficiency.
Conclusions:
- The proposed inertial sensor-based GED system is highly accurate for diverse terrains.
- The system's efficiency and real-time capability make it suitable for portable rehabilitation devices.
- This technology offers potential for enhanced gait monitoring in dynamic environments.
