Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study
Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/14/4302 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850071318575185920 |
|---|---|
| author | Kyeong-Jun Seo Jinwon Lee Ji-Eun Cho Hogene Kim Jung Hwan Kim |
| author_facet | Kyeong-Jun Seo Jinwon Lee Ji-Eun Cho Hogene Kim Jung Hwan Kim |
| author_sort | Kyeong-Jun Seo |
| collection | DOAJ |
| description | Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis. |
| format | Article |
| id | doaj-art-3519f1111c304b50b44fe8940bbf788a |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-3519f1111c304b50b44fe8940bbf788a2025-08-20T02:47:21ZengMDPI AGSensors1424-82202025-07-012514430210.3390/s25144302Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot StudyKyeong-Jun Seo0Jinwon Lee1Ji-Eun Cho2Hogene Kim3Jung Hwan Kim4Department of Rehabilitation & Assistive Technology, National Rehabilitation Center, Ministry of Health and Welfare, Seoul 01022, Republic of KoreaDepartment of Industrial and Management Engineering, Gangneung-Wonju National University, Wonju 26403, Gangwon-do, Republic of KoreaDepartment of Rehabilitation & Assistive Technology, National Rehabilitation Center, Ministry of Health and Welfare, Seoul 01022, Republic of KoreaDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USADepartment of Rehabilitation Medicine, Ewha Womans University & Medical Center Mokdong Hospital, Seoul 07985, Republic of KoreaGait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis.https://www.mdpi.com/1424-8220/25/14/4302feed-forward neural network (FFNN)gait environmentwearable sensormultimodal sensor |
| spellingShingle | Kyeong-Jun Seo Jinwon Lee Ji-Eun Cho Hogene Kim Jung Hwan Kim Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study Sensors feed-forward neural network (FFNN) gait environment wearable sensor multimodal sensor |
| title | Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study |
| title_full | Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study |
| title_fullStr | Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study |
| title_full_unstemmed | Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study |
| title_short | Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study |
| title_sort | gait environment recognition using biomechanical and physiological signals with feed forward neural network a pilot study |
| topic | feed-forward neural network (FFNN) gait environment wearable sensor multimodal sensor |
| url | https://www.mdpi.com/1424-8220/25/14/4302 |
| work_keys_str_mv | AT kyeongjunseo gaitenvironmentrecognitionusingbiomechanicalandphysiologicalsignalswithfeedforwardneuralnetworkapilotstudy AT jinwonlee gaitenvironmentrecognitionusingbiomechanicalandphysiologicalsignalswithfeedforwardneuralnetworkapilotstudy AT jieuncho gaitenvironmentrecognitionusingbiomechanicalandphysiologicalsignalswithfeedforwardneuralnetworkapilotstudy AT hogenekim gaitenvironmentrecognitionusingbiomechanicalandphysiologicalsignalswithfeedforwardneuralnetworkapilotstudy AT junghwankim gaitenvironmentrecognitionusingbiomechanicalandphysiologicalsignalswithfeedforwardneuralnetworkapilotstudy |