Developed a Bi-GRU-based automated gait event detection model achieving >97% accuracy and <14 ms mean error in both regular and perturbed walking.
Analyzed gait data from 307 healthy older adults, demonstrating the model’s robustness across challenging perturbed walking scenarios where traditional force plate methods fail.
Abstract
Accurate detection of gait events in older adults, particularly during perturbed walking, is essential for evaluating balance control and fall risk. Traditional force plate-based methods often face limitations in perturbed walking scenarios due to the difficulty in landing cleanly on the force plates. Subsequently, previous studies have not addressed gait event automatic detection methods for perturbed walking. This study introduces an automated gait event detection method using a bidirectional gated recurrent unit (Bi-GRU) model, leveraging ground reaction force, joint angles, and marker data, for both regular and perturbed walking scenarios from 307 healthy older adults. Our marker-based model achieved over 97% accuracy with a mean error of less than 14 ms in detecting touchdown (TD) and liftoff (LO) events for both walking scenarios. The results highlight the efficacy of kinematic approaches, demonstrating their potential in gait event detection for clinical settings. When integrated with wearable sensors or computer vision techniques, these methods enable real-time, precise monitoring of gait patterns, which is helpful for applying personalized programs for fall prevention. This work takes a significant step forward in automated gait analysis for perturbed walking, offering a reliable method for evaluating gait patterns, balance control, and fall risk in clinical settings.
Accurate detection of gait events in older adults, particularly during perturbed walking, is essential for evaluating balance control and fall risk. Traditional force plate-based methods often face limitations in perturbed walking scenarios due to the difficulty in landing cleanly on the force plates. Subsequently, previous studies have not addressed gait event automatic detection methods for perturbed walking. This study introduces an automated gait event detection method using a bidirectional gated recurrent unit (Bi-GRU) model, leveraging ground reaction force, joint angles, and marker data, for both regular and perturbed walking scenarios from 307 healthy older adults. Our marker-based model achieved over 97% accuracy with a mean error of less than 14 ms in detecting touchdown (TD) and liftoff (LO) events for both walking scenarios. The results highlight the efficacy of kinematic approaches, demonstrating their potential in gait event detection for clinical settings. When integrated with wearable sensors or computer vision techniques, these methods enable real-time, precise monitoring of gait patterns, which is helpful for applying personalized programs for fall prevention. This work takes a significant step forward in automated gait analysis for perturbed walking, offering a reliable method for evaluating gait patterns, balance control, and fall risk in clinical settings.
@article{wang2025automatic,title={Automatic Gait EVENT Detection in Older Adults During Perturbed Walking},doi={https://doi.org/10.1186/s12984-025-01560-9},author={Wang, Shuaijie and Omar, Kazi Shahrukh and Miranda, Fabio and Bhatt, Tanvi},journal={Journal of NeuroEngineering and Rehabilitation},volume={22},number={1},pages={40},year={2025},publisher={Springer}}
Automatic Gait EVENT Detection in Older Adults During Perturbed WalkingJournal of NeuroEngineering and Rehabilitation, 2025