Motion Intent Recognition in Intelligent Lower Limb Prosthesis Using One‐Dimensional Dual‐Tree Complex Wavelet Transforms
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Abstract
The motion intent recognition via lower limb prosthesis can be regarded as a kind of short‐term action recognition, where the major issue is to explore the gait instantaneous conversion (known as transitional pattern) between each two adjacent different steady states of gait mode. Traditional intent recognition methods usually employ a set of statistical features to classify the transitional patterns. However, the statistical features of the short‐term signals via the instantaneous conversion are empirically unstable, which may degrade the classification accuracy. Bearing this in mind, we introduce the one‐dimensional dual‐tree complex wavelet transform (1D‐DTCWT) to address the motion intent recognition via lower limb prosthesis. On the one hand, the local analysis ability of the wavelet transform can amplify the instantaneous variation characteristics of gait information, making the extracted features of instantaneous pattern between two adjacent different steady states more stable. On the other hand, the translation invariance and direction selectivity of 1D‐DTCWT can help to explore the continuous features of patterns, which better reflects the inherent continuity of human lower limb movements. In the experiments, we have recruited ten able‐bodied subjects and one amputee subject and collected data by performing five steady states and eight transitional states. The experimental results show that the recognition accuracy of the able‐bodied subjects has reached 98.91%, 98.92%, and 97.27% for the steady states, transitional states, and total motion states, respectively. Furthermore, the accuracy of the amputee has reached 100%, 91.16%, and 90.27% for the steady states, transitional states, and total motion states, respectively. The above evidence finally indicates that the proposed method can better explore the gait instantaneous conversion (better expressed as motion intent) between each two adjacent different steady states compared with the state‐of‐the‐art.
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Wavelet Transform (WT) performs time-frequency localized analysis, captures transient features (e.g., muscle bursts), and is used for EMG dynamic motion pattern decomposition.
“| Wavelet Transform (WT) | Time-frequency localized analysis | Capture transient features (e.g., muscle | EMG dynamic motion pattern |”
From Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction (2026) · cites this paper as [136]
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Cites
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
The dual-tree complex wavelet transform
Selesnick, I., Baraniuk, R. G., & Kingsbury, N. G. (2005) · IEEE Signal Processing Magazine · doi:10.1109/msp.2005.1550194 · Cited by 2,474 · see citing works
State of the Art and Future Directions for Lower Limb Robotic Exoskeletons
Young, A. J., & Ferris, D. P. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2521160 · Cited by 909 · see citing works
Limb Amputation and Limb Deficiency: Epidemiology and Recent Trends in the United States
Dillingham, T. R., Pezzin, L. E., & MacKenzie, E. J. (2002) · Southern Medical Journal · doi:10.1097/00007611-200208000-00018 · Cited by 822 · see citing works
A Strategy for Identifying Locomotion Modes Using Surface Electromyography
Huang, H., Kuiken, T., & Lipschutz, R. D. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2008.2003293 · Cited by 517 · see citing works
3D-printed upper limb prostheses: a review
ten Kate, J., Smit, G., & Breedveld, P. (2017) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2016.1253117 · Cited by 375 · see citing works
Predictors of quality of life among individuals who have a lower limb amputation
Asano, M., Rushton, P. W., Miller, W. C., & Deathe, B. A. (2008) · Prosthetics and Orthotics International · doi:10.1080/03093640802024955 · Cited by 272 · see citing works
An online fault detection model and strategies based on SVM-grid in clouds
Zhang, P., Shu, S., & Zhou, M. (2018) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510817 · Cited by 238 · see citing works
Adapala, R. K., Thoppil, R. J., Luther, D. J., Paruchuri, S. M., Meszaros, J. G., Chilian, W. M., & Thodeti, C. K. (2012) · Journal of Molecular and Cellular Cardiology · doi:10.1016/j.yjmcc.2012.10.016 · Cited by 215 · see citing works
Su, B., Wang, J., Liu, S., Sheng, M., Jiang, J., & Xiang, K. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2909585 · Cited by 177 · see citing works
Self-citationThe World Report on Disability
Bickenbach, J. E. (2011) · Disability & Society · doi:10.1080/09687599.2011.589198 · Cited by 175 · see citing works
Bisio, I., Delfino, A., Lavagetto, F., & Sciarrone, A. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2628938 · Cited by 168 · see citing works
Young, A. J., & Hargrove, L. J. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2412461 · Cited by 143 · see citing works
Zheng, E., & Wang, Q. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2529581 · Cited by 84 · see citing works
Naik, G. R., Suviseshamuthu, E. S., Arjunan, S. P., Acharyya, A., Kumar, D., Ramanujam, A., & Nguyen, H. T. (2018) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2018.2796070 · Cited by 84 · see citing works
Application of Higher Order Statistics to Surface Electromyogram Signal Classification
Nazarpour, K., Sharafat, A. R., & Firoozabadi, M. (2007) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.894829 · Cited by 81 · see citing works
Real-Time Hybrid Locomotion Mode Recognition for Lower Limb Wearable Robots
Parri, A., Yuan, K., Marconi, D., Yan, T., Crea, S., Munih, M., Lova, R. M., Vitiello, N., & Wang, Q. (2017) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2017.2755048 · Cited by 79 · see citing works
Performance of Random Forest and SVM in Face Recognition
Kremić, E., & Subaşı, A. (2016) · The International Arab Journal of Information Technology · Cited by 76 · see citing works
Intent pattern recognition of lower-limb motion based on mechanical sensors
Liu, Z., Lin, W., Geng, Y., & Yang, P. (2017) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2017.7510619 · Cited by 66 · see citing works
Bluetooth Based Indoor Positioning Using Machine Learning Algorithms
Sthapit, P., Gang, H.-S., & Pyun, J.-Y. (2018) · IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) · doi:10.1109/icce-asia.2018.8552138 · Cited by 55 · see citing works
A sensor-to-segment calibration method for motion capture system based on low cost MIMU
Choe, N., Zhao, H., Qiu, S., & So, Y. (2018) · Measurement · doi:10.1016/j.measurement.2018.07.078 · Cited by 50 · see citing works
Yang, P., Zhang, F., & Yang, G. (2018) · IEEE Access · doi:10.1109/access.2018.2797072 · Cited by 43 · see citing works
Source Camera Identification With Dual-Tree Complex Wavelet Transform
Zeng, H., Wan, Y., Deng, K., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2968855 · Cited by 31 · see citing works
Literature review and current trends on transfemoral powered prosthetics
Lara-Barrios, C. M., Ortega, A. B., Guzmán-Valdivia, C. H., & Valles, K. D. B. (2017) · Advanced Robotics · doi:10.1080/01691864.2017.1402704 · Cited by 30 · see citing works
Computational model for the recognition of lower limb movement using wearable gyroscope sensor
Hussain, T., Maqbool, H. F., Iqbal, N., et al. (2019) · International Journal of Sensor Networks · doi:10.1504/ijsnet.2019.099230 · Cited by 29 · see citing works
Weighted Feature Gaussian Kernel SVM for Emotion Recognition
Wei, W., & Jia, Q. (2016) · Computational Intelligence and Neuroscience · doi:10.1155/2016/7696035 · Cited by 26 · see citing works
Zheng, E., Wang, Q., & Qiao, H. (2019) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2019.2934525 · Cited by 19 · see citing works
Leineweber, M. J., Orozco, M. D. G., & Andrysek, J. (2019) · Medical Engineering & Physics · doi:10.1016/j.medengphy.2019.06.013 · Cited by 10 · see citing works
Noise suppression in the dual-tree complex wavelet domain for seismic signal
Meng, K., Li, J., & Li, Y. (2016) · Journal of Petroleum Exploration and Production Technology · doi:10.1007/s13202-016-0272-4 · Cited by 5 · see citing works
Hierarchical Human Action Recognition with Self-Selection Classifiers via Skeleton Data*
Su, B., Wu, H., 盛, M. 敏. S., et al. (2018) · Communications in Theoretical Physics · doi:10.1088/0253-6102/70/5/633 · Cited by 1 · see citing works
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- [20]Sheng M. Liu S. Su B. Motion intent recognition of intelligent lower limb prosthesis based on GMM-HMM Chinese Journal of Scientific Instrument 2019 40 5 169 178
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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
- Sheng, M., Wang, W.-J., Tong, T.-T., Yang, Y.-Y., Chen, H.-L., & Su, B.-Y.
- Affiliations
- Anqing Normal University (CN)
- Corresponding author
- Chen, Hui-Lin
- Venue
- Computational Intelligence and Neuroscience
- Publisher
- Wiley
- Published
- 2021-01
- Volume
- 2021
- Issue
- 1
- Pages
- 5631730-5631730
- ISSN
- 1687-5265 (ISSN-L)
- 1687-5273
- PMC
- PMC8635934
- OpenAlex
- W4226143558
- Cited by
- 7
- References
- 30
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 1 · 2022: 1
- FWCI
- 0.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Computational Intelligence and Neuroscience (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Nature (61603003)
- Anhui Provincial Department of Education (gxbjZD26)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- lower limb prosthesis, dual-tree complex wavelet transform, gait transition, amputees, gait feature extraction, translation invariance, direction selectivity
- MeSH terms
- Algorithms, Amputees, Artificial Limbs, Humans, Motion, Wavelet Analysis
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