On Predicting Transitions to Compliant Surfaces in Human Gait via Neural and Kinematic Signals
Open accessAbstract
Walking surfaces of varying compliance are encountered frequently in everyday life C, and transitions between them are usually not a challenging task for most people. The human brain, based on feedback from the environment, as well as previous experience, controls the lower limb dynamics to transition to new surfaces ensuring stability and safety. However, this is not always possible for people with lower limb impairments, especially those using wearable (orthotic) or prosthetic devices. Current control methodologies for lower limb wearables and powered ankle prostheses have successfully replicated conditions for walking on rigid surfaces. However, agility and walking stability on non-flat and compliant surfaces remain a significant challenge for individuals with gait disabilities. C There is therefore the need to incorporate the human wearer in the loop and proactively adjust their control to transition to surfaces of different compliance. This work proposes a subject-specific pattern recognition (PR) and classification strategy using kinematic data and surface electromyographic (EMG) signals to recognize user intent to transition from a rigid to a compliant surface. Using a k-Nearest Neighbors (k-NN) methodology in combination with an Artificial Neural Network (ANN), our strategy can accurately predict upcoming surface stiffness transitions C in real time. C This would allow for a fast parameter control of the prosthesis C or wearable device and for adaptation to the new terrain. Classification results after employing the proposed strategy reach a prediction accuracy of up to 87.5%, proving that C predicting transitions to compliant surfaces in real time is feasible and efficient. The proposed framework can lead to increased robustness and safety of lower-limb prosthetic C or wearable devices that will eventually improve the quality of life of individuals living with C a lower limb impairment.
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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 [239]
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Cites
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192 · see citing works
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865 · see citing works
Heart Disease and Stroke Statistics—2020 Update: A Report From the American Heart Association
Virani, S. S., Alonso, A., Benjamin, E. J., Bittencourt, M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Chang, A. R., Cheng, S., Delling, F. N., et al. (2020) · Circulation · doi:10.1161/cir.0000000000000757 · Cited by 9,175 · see citing works
Development of recommendations for SEMG sensors and sensor placement procedures
Hermens, H. J., Freriks, B., Disselhorst-Klug, C., & Rau, G. (2000) · Journal of Electromyography and Kinesiology · doi:10.1016/s1050-6411(00)00027-4 · Cited by 7,213 · see citing works
Estimating the Prevalence of Limb Loss in the United States: 2005 to 2050
Ziegler‐Graham, K., MacKenzie, E. J., Ephraim, P. L., Travison, T. G., & Brookmeyer, R. (2008) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2007.11.005 · Cited by 2,823 · see citing works
A new strategy for multifunction myoelectric control
Hudgins, B., Parker, P., & Scott, R. N. (1993) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.204774 · Cited by 2,149 · see citing works
Engelbrecht, A. P. (2007) · Wiley eBooks · doi:10.1002/9780470512517 · Cited by 1,459 · see citing works
SMOTE for high-dimensional class-imbalanced data
Blagus, R., & Lusa, L. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-106 · Cited by 1,155 · see citing works
Gait Analysis: Normal and Pathological Function
Grant, A. D. (2010) · JAMA · doi:10.1001/jama.2010.1210 · Cited by 1,140 · see citing works
k-Nearest Neighbour Classifiers - A Tutorial
Cunningham, P., & Delany, S. J. (2021) · ACM Computing Surveys · doi:10.1145/3459665 · Cited by 1,033 · see citing works
The prevalence and risk factors of falling and fear of falling among lower extremity amputees
Miller, W. C., Speechley, M. R., & Deathe, B. A. (2001) · Archives of Physical Medicine and Rehabilitation · doi:10.1053/apmr.2001.24295 · Cited by 644 · see citing works
Brockett, C., & Chapman, G. J. (2016) · Orthopaedics and Trauma · doi:10.1016/j.mporth.2016.04.015 · Cited by 597 · see citing works
Huang, H., Zhang, F., Hargrove, L. J., Dou, Z., Rogers, D. R., & Englehart, K. B. (2011) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2011.2161671 · Cited by 542 · 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
Runners adjust leg stiffness for their first step on a new running surface
Ferris, D. P., Liang, K., & Farley, C. T. (1999) · Journal of Biomechanics · doi:10.1016/s0021-9290(99)00078-0 · Cited by 409 · see citing works
Patla, A. E., & Vickers, J. N. (2003) · Experimental Brain Research · doi:10.1007/s00221-002-1246-y · Cited by 393 · see citing works
Mahlknecht, P., Kiechl, S. J., Bloem, B. R., Willeit, J., Scherfler, C., Gasperi, A., Rungger, G., Poewe, W. H., & Seppi, K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069627 · Cited by 275 · see citing works
Time-Varying Ankle Mechanical Impedance During Human Locomotion
Lee, H., & Hogan, N. (2014) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2014.2346927 · Cited by 170 · see citing works
Summary of Human Ankle Mechanical Impedance During Walking
Lee, H., Rouse, E. J., & Krebs, H. I. (2016) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2016.2601613 · Cited by 149 · see citing works
Liu, M., Wang, D., & Huang, H. (2015) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2420539 · Cited by 145 · see citing works
Movement: How the Brain Communicates with the World
Schwartz, A. B. (2016) · Cell · doi:10.1016/j.cell.2016.02.038 · Cited by 131 · see citing works
Muscle Power Compensatory Mechanisms in Below-Knee Amputee Gait
Sadeghi, H., Allard, P., & Duhaime, M. (2001) · American Journal of Physical Medicine & Rehabilitation · doi:10.1097/00002060-200101000-00007 · Cited by 122 · see citing works
Huang, S., & Ferris, D. P. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-55 · Cited by 101 · see citing works
Gaze Behavior of Young and Older Adults During Stair Walking
Zietz, D., & Hollands, M. A. (2009) · Journal of Motor Behavior · doi:10.3200/jmbr.41.4.357-366 · Cited by 94 · see citing works
Kim, J., Major, M. J., Hafner, B. J., et al. (2018) · PM&R · doi:10.1016/j.pmrj.2018.08.385 · Cited by 88 · see citing works
Gates, D. H., Dingwell, J. B., Scott, S. J., et al. (2012) · Gait & Posture · doi:10.1016/j.gaitpost.2011.12.019 · Cited by 78 · see citing works
Skidmore, J., Barkan, A. R., & Artemiadis, P. (2014) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2014.2350456 · Cited by 39 · see citing works
Self-citationCompliant terrain legged locomotion using a viscoplastic approach
Vasilopoulos, V., Paraskevas, I. S., & Papadopoulos, E. G. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6943251 · Cited by 32 · see citing works
Variable Stiffness Treadmill (VST): A novel tool for the investigation of gait
Barkan, A. R., Skidmore, J., & Artemiadis, P. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907266 · Cited by 32 · see citing works
Self-citationUnderstanding lower limb muscle volume adaptations to amputation
Henson, D. P., Edgar, C. E., Ding, Z., et al. (2021) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2021.110599 · Cited by 30 · see citing works
Muscular activity characteristics associated with preparation for gait transition
Li, L., & Ogden, L. L. (2012) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2012.04.006 · Cited by 24 · see citing works
Skidmore, J., & Artemiadis, P. (2016) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-016-0140-y · Cited by 24 · see citing works
Self-citationF-VESPA: A Kinematic-based Algorithm for Real-time Heel-strike Detection During Walking
Karakasis, C., & Artemiadis, P. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636335 · Cited by 15 · see citing works
Self-citationA Systematic Method for Outlier Detection in Human Gait Data
Hobbs, B., & Artemiadis, P. (2022) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr55369.2022.9896411 · Cited by 13 · see citing works
Self-citationSkidmore, J., Barkan, A. R., & Artemiadis, P. (2014) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2014.6942841 · Cited by 11 · see citing works
Self-citationDrolet, M., Yumbla, E. Q., Hobbs, B., et al. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197536 · Cited by 11 · see citing works
Yumbla, E. Q., Obeng, R. A., Ward, J. A., et al. (2019) · International Conference on Rehabilitation Robotics · doi:10.1109/icorr.2019.8779403 · Cited by 6 · see citing works
Sarroca, N., Bartolomé, M. J. L., Valero, J., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10143119 · Cited by 3 · see citing works
Anticipated muscular synchronization between walking on flat and cross-sloped surface
(2016) · Gait & Posture · doi:10.1016/j.gaitpost.2016.07.124 · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- K. Reardon, "Analyzing anticipatory muscle tensing as a measure of prospective action," Ph.D. dissertation, College William Mary-Arts Sci., Psychol. Dept., Williamsburg, VA, USA, 2009.
- P. Cunningham and S. J. Delany, "k-Nearest neighbour classifiers: 2nd edition (with Python examples)," ACM Comput. Surv., vol. 54, pp. 128:1-128:25, Apr. 2020.
Cited by
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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
A Study on Lower Limb Movement Intention Recognition Based on Multi-Source Information Fusion
Zong, S., Li, W., Sun, D., Wei, X., Chen, J., Yue, Z., & Sun, D. (2025) · IEEE Access · doi:10.1109/access.2024.3521510 · Cited by 7 · see citing works
- Authors
- Angelidou, C., & Artemiadis, P.
- Affiliations
- University of Delaware (US)
- Venue
- IEEE Transactions on Neural Systems and Rehabilitation Engineering
- Published
- 2023
- Volume
- 31
- Pages
- 2214-2223
- ISSN
- 1534-4320 (ISSN-L)
- 1558-0210
- OpenAlex
- W4367727890
- Cited by
- 19
- References
- 40
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 3 · 2023: 1
- FWCI
- 1.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Transactions on Neural Systems and Rehabilitation Engineering (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (2020009, 2015786, 2025797, 2018905)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Keywords
- compliant surfaces, surface electromyography, lower limb prosthesis, user intention recognition, k-nearest neighbors, artificial neural networks, gait stability, lower limb impairment
- MeSH terms
- Amputees, Artificial Limbs, Biomechanical Phenomena, Gait, Humans, Quality of Life, Walking, Lower Extremity
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