A Study on Lower Limb Movement Intention Recognition Based on Multi-Source Information Fusion
Open accessAbstract
In order to enhance the suppleness of a lower limb rehabilitation medical robot during the re-habilitation process, this study proposes a multi-source information fusion lower limb motion intention recognition method based on surface electromyographic signals (sEMG) and lower limb joint angles. To solve the problem of data traffic surge during the collection process, a multi-source current limiting sliding time window algorithm (MLS) is proposed. The MLS algorithm controls the data flow through a flow limiting and sliding time window mechanism to ensure the efficiency and stability of the system in handling large data volumes. On this basis, the study combines the Back Propagation Generalized Algorithm Neural-network (BPGN) to construct a prediction model for lower limb joint angles. The experimental results show that under the same conditions of the algorithm, the fusion of multi-source information reduces the average error of knee joint angle prediction by 10.8° and the average error of ankle joint angle prediction by 7.2° compared with the method using a single lower limb joint angle signal. Under the same condition of input signal, the multivariate flow-limiting sliding time-window BPGN reduced the average knee joint error by 13.6° and the average ankle joint angle error by 8.5° compared to the BPGN intent recognition. The multivariate flow-limited sliding time window BPGN reduced the mean knee error by 11.2° and the mean ankle angle error by 7.4° compared to Radial Basis Function (RBF) Neural-network intent recognition. By integrating the sEMG signal and lower limb joint angle information, the system can more accurately capture the patient’s movement intention and realize more precise lower limb rehabilitation training.
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k-NN uses voting based on local neighborhood similarity, requires no training and dynamically adapts to new data, and is used for real-time gait anomaly detection.
“| k-NN | Voting based on local neighborhood similarity | No training required, dynamically adapts | Real-time gait anomaly detection 239,263,264 |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [264]
Cites
Chen, K., Shen, Z., Gu, W., Lyu, Z., Qi, X., Mu, Y., Ning, Y., & for the Meinian Investigator Group (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15238 · Cited by 396 · see citing works
Machine-Learning-Based Prediction of Gait Events From EMG in Cerebral Palsy Children
Christian Morbidoni, B., Cucchiarelli, A., Agostini, V., Knaflitz, M., Fioretti, S., & Nardo, F. D. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3076366 · Cited by 68 · see citing works
Fratti, R., Marini, N., Atzori, M., Müller, H., Tiengo, C., & Bassetto, F. (2024) · Sensors · doi:10.3390/s24227147 · Cited by 31 · see citing works
Rabe, K. G., Lenzi, T., & Fey, N. P. (2021) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2021.3134189 · Cited by 30 · see citing works
Exoskeleton Recognition of Human Movement Intent Based on Surface Electromyographic Signals: Review
Lu, C., Qi, Q., Liu, Y., Li, D., Xian, W., Wang, Y., Chen, C., & Xu, X. F. (2024) · IEEE Access · doi:10.1109/access.2024.3388044 · Cited by 28 · see citing works
Dynamic Muscle Fatigue State Recognition Based on Deep Learning Fusion Model
Liu, J., Tao, Q., & Wu, B. (2023) · IEEE Access · doi:10.1109/access.2023.3309741 · Cited by 21 · see citing works
Zhang, P., Gao, X., Miao, M., & Zhao, P. (2022) · Machines · doi:10.3390/machines10121125 · Cited by 20 · see citing works
On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals
Angelidou, C., & Artemiadis, P. (2023) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2023.3272355 · Cited by 19 · see citing works
Ankle Joint Torque Prediction Based on Surface Electromyographic and Angular Velocity Signals
Su, C., Chen, S., Jiang, H., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3040820 · Cited by 15 · see citing works
Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model
Zhou, Z., Tao, Q., Su, N., Liu, J., Chen, Q., & Li, B. (2024) · Sensors · doi:10.3390/s24217087 · Cited by 15 · see citing works
A Fusion Dimension Reduction Method for the Features of Surface Electromyographic Signals
Ma, L., Tao, Q., Zhang, X., & Chen, Q. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3485186 · Cited by 4 · see citing works
Khorram, A., Lin, H., & Peng, W. (2024) · International Conference on Industrial, Manufacturing, and Process Engineering (ICIMP) · doi:10.3390/engproc2024076052 · Cited by 2 · see citing works
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Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9 · see citing works
- Authors
- Zong, S., Li, W., Sun, D., Wei, X., Chen, J., Yue, Z., & Sun, D.
- Affiliations
- Changchun University (CN)
- Sellafield (United Kingdom) (GB)
- Shandong JITE Industrial Technology Company Ltd., Rizhao, Shandong, China
- Venue
- IEEE Access
- Published
- 2025
- Volume
- 13
- Pages
- 5032-5041
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4405718116
- Cited by
- 7
- References
- 12
- Citations by year
- 2026: 4 · 2025: 3
- FWCI
- 0.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Jilin Development and Reform Commission 2023 Budgeted Capital Funding (2023C043)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Keywords
- lower limb rehabilitation robot, movement intention recognition, multi-source data fusion, surface electromyography, joint angle prediction, knee joint angle, ankle joint angle, rehabilitation training
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