Dual-Branch Deep Learning for Continuous Gait Cycle Estimation with wearable IMU Sensors and Anthropometric Data
Issued Date
2025-01-01
Resource Type
ISSN
1557170X
Scopus ID
2-s2.0-105023744932
Pubmed ID
41336443
Journal Title
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS
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SCOPUS
Bibliographic Citation
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS (2025)
Suggested Citation
Hengswat K., Khawkhom A., Ngamdi N., Anopas D. Dual-Branch Deep Learning for Continuous Gait Cycle Estimation with wearable IMU Sensors and Anthropometric Data. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBS (2025). doi:10.1109/EMBC58623.2025.11251706 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/115443
Title
Dual-Branch Deep Learning for Continuous Gait Cycle Estimation with wearable IMU Sensors and Anthropometric Data
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Abstract
This study presents a CNN-LSTM model for gait cycle phase prediction using wearable IMU data and anthropometric features. A sliding window approach was applied for real-time gait estimation, with gait cycle percentage calculated via peak detection and lower limb length data. Feature selection identified key IMU-derived kinematic features, primarily joint angles, to enhance model performance. The model achieved R<sup>2</sup> = 0.911, demonstrating high accuracy across walking conditions. These findings support IMU-based gait analysis for rehabilitation, abnormal gait detection, and prosthetic control. To achieve our ultimate goal in the future, we are developing a comprehensive AI-driven system for real-time gait cycle assessment.Clinical relevance - This study supports real-time gait monitoring using wearable IMU sensors, enabling personalized rehabilitation, early abnormal gait detection, and prosthetic adaptation. The model's accuracy and reliance on kinematic data alone make it suitable for clinical and home-based gait assessment.