Automatic gait EVENT detection in older adults during perturbed walking
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 fo...
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| Format: | Article |
| Language: | English |
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BMC
2025-02-01
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| Series: | Journal of NeuroEngineering and Rehabilitation |
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| Online Access: | https://doi.org/10.1186/s12984-025-01560-9 |
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| _version_ | 1850184966420299776 |
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| author | Shuaijie Wang Kazi Shahrukh Omar Fabio Miranda Tanvi Bhatt |
| author_facet | Shuaijie Wang Kazi Shahrukh Omar Fabio Miranda Tanvi Bhatt |
| author_sort | Shuaijie Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ff74fd80fc9a421980db27b5bc4e30db |
| institution | OA Journals |
| issn | 1743-0003 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of NeuroEngineering and Rehabilitation |
| spelling | doaj-art-ff74fd80fc9a421980db27b5bc4e30db2025-08-20T02:16:54ZengBMCJournal of NeuroEngineering and Rehabilitation1743-00032025-02-0122111010.1186/s12984-025-01560-9Automatic gait EVENT detection in older adults during perturbed walkingShuaijie Wang0Kazi Shahrukh Omar1Fabio Miranda2Tanvi Bhatt3Department of Physical Therapy, University of Illinois ChicagoDepartment of Computer Science, University of Illinois ChicagoDepartment of Computer Science, University of Illinois ChicagoDepartment of Physical Therapy, University of Illinois ChicagoAbstract 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.https://doi.org/10.1186/s12984-025-01560-9SlipTripDeep learningGait detectionBi-GRU model |
| spellingShingle | Shuaijie Wang Kazi Shahrukh Omar Fabio Miranda Tanvi Bhatt Automatic gait EVENT detection in older adults during perturbed walking Journal of NeuroEngineering and Rehabilitation Slip Trip Deep learning Gait detection Bi-GRU model |
| title | Automatic gait EVENT detection in older adults during perturbed walking |
| title_full | Automatic gait EVENT detection in older adults during perturbed walking |
| title_fullStr | Automatic gait EVENT detection in older adults during perturbed walking |
| title_full_unstemmed | Automatic gait EVENT detection in older adults during perturbed walking |
| title_short | Automatic gait EVENT detection in older adults during perturbed walking |
| title_sort | automatic gait event detection in older adults during perturbed walking |
| topic | Slip Trip Deep learning Gait detection Bi-GRU model |
| url | https://doi.org/10.1186/s12984-025-01560-9 |
| work_keys_str_mv | AT shuaijiewang automaticgaiteventdetectioninolderadultsduringperturbedwalking AT kazishahrukhomar automaticgaiteventdetectioninolderadultsduringperturbedwalking AT fabiomiranda automaticgaiteventdetectioninolderadultsduringperturbedwalking AT tanvibhatt automaticgaiteventdetectioninolderadultsduringperturbedwalking |