Recognition of walking environments and gait period by surface electromyography
Abstract
Recognizing and predicting the movement and intention of the wearer in control of an exoskeleton robot is very challenging. It is difficult for exoskeleton robots, which measure and drive human movements, to interact with humans. Therefore, many different types of sensors are needed. When using various sensors, a data design is needed for effective sensing. An electromyographic (EMG) signal can be used to identify intended motion before the actual movement, and the delay time can be shortened via control of the exoskeleton robot. Before using a lower limb exoskeleton to help in walking, the aim of this work is to distinguish the walking environment and gait period using various sensors, including the surface electromyography (sEMG) sensor. For this purpose, a gait experiment was performed on four subjects using the ground reaction force, human-robot interaction force, and position sensors with sEMG sensors. The purpose of this paper is to show progress with the use of sEMG when recognizing walking environments and the gait period with other sensors. For effective data design, we used a combination of sensor types, sEMG sensor locations, and sEMG features. The results obtained using an individual mechanical sensor together with sEMG showed improvement compared to the case of using an individual sensor, and the combination of sEMG and position information showed the best performance in the same number of combinations of three sensors. When four sensor combinations were used, the environment classification accuracy was 96.1%, and the gait period classification accuracy was 97.8%. Vastus medialis (VM) and gastrocnemius (GAS) were the most effective combinations of two muscle types among the five sEMG sensor locations on the legs, and the results were 74.4% in pre-heel contact (preHC) and 71.7% in pre-toe-off (preTO) for environment classification, and 68.0% for gait period classification, when using only the sEMG sensor. The two effective sEMG feature combinations were “mean absolute value (MAV), zero crossings (ZC)” and “MAV, waveform length (WL)”, and the “MAV, ZC” results were 80.0%, 77.1%, and 75.5%. These results suggest that the sEMG signal can be effectively used to control an exoskeleton robot.
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This type of research is often used in hand rehabilitation training, gesture recognition systems, and prosthetic control.
“This type of research is often used in hand rehabilitation training 98 , gesture recognition systems 132 , and prosthetic control 131,133 .”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [133]
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Choi, Y., Kim, J., Lee, D., & Choi, Y. (2023) · The Journal of Korea Robotics Society · doi:10.7746/jkros.2023.18.4.496
Self-citationFeng, J., Bao, X., Kim, W. D., Wang, K., & Kim, J. (2025) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr66766.2025.11063045
Self-citationZhang, M., Sun, R., & Zhang, X. (2026) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2026.110652
Stride Smart Unlocking the Secrets of Lower Limb Gait Analysis
Ponni Bala, M., Balambigai, S., Ponkarthika, M., Saritha, B., Maheswari, S. U., & Mangai, S. (2026) · International Conference Inventive Material Science Applications · doi:10.1109/icima68728.2026.11564583
- Authors
- Kyeong, S., Shin, W., Yang, M., Heo, U., Feng, J.-R., & Kim, J.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Kim, Jung
- Venue
- Frontiers of Information Technology & Electronic Engineering
- Publisher
- Zhejiang University Press
- Published
- 2019-03
- Volume
- 20
- Issue
- 3
- Pages
- 342-352
- ISSN
- 2095-9184 (ISSN-L)
- 2095-9230
- OpenAlex
- W2936029817
- Cited by
- 68
- References
- 43
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 12 · 2023: 10 · 2022: 11 · 2021: 12 · 2020: 11 · 2019: 3
- FWCI
- 3.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Frontiers of Information Technology & Electronic Engineering (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- surface electromyography, lower limb exoskeleton, human-robot interaction, sensor fusion, ground reaction force, EMG feature extraction, mean absolute value, zero crossings, waveform length, vastus medialis, gastrocnemius
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