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Seamless Temporal Gait Evaluation during Walking and Running Using Two IMU Sensors
Summary
This study introduces a finite state machine (FSM) framework using IMU sensors to accurately identify walking and running gaits and their distinct phases. This method enables flexible gait analysis with automatic speed change detection.
Area of Science:
- Biomechanics
- Wearable Technology
- Human Motion Analysis
Background:
- Gait analysis is crucial for understanding human locomotion and detecting abnormalities.
- Existing methods often require complex setups or lack seamless transition detection between walking and running.
Purpose of the Study:
- To develop a framework for extracting gait events and temporal features during walking and running using IMU sensors.
- To automatically detect speed changes and transitions between gait types.
Main Methods:
- Constructed a finite state machine (FSM) with transition rules based on data from two IMU sensors attached to shoes.
- Defined specific states to recognize walking's double support phase and running's double flight phase.
- Implemented an automatic speed change detection algorithm using a moving average filter on motion intensity data.
Main Results:
- The FSM framework accurately distinguished between walking and running gaits.
- Detailed gait phases (double support, double flight) were successfully extracted.
- The system demonstrated effective automatic detection of speed changes and gait transitions.
Conclusions:
- The proposed FSM framework provides a flexible and accurate method for gait analysis.
- This approach facilitates seamless recognition of gait type and phase transitions during treadmill locomotion.
- Findings support advancements in wearable-based gait monitoring and analysis systems.
