Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup
pdf (الإنجليزية)

كيفية الاقتباس

Enhanced Gait Phases Recognition by EMG and Kinematics Information Fusion and a Minimal Recording Setuping Setup. (2024). مجلة الخوارزمي الهندسية, 20(2), 89-93. https://doi.org/10.22153/kej.2024.05.002

تواريخ المنشور

الملخص

The limited mobility of lower limb amputees highlights the need for advancements in prosthetic control strategies to restore natural locomotion. This paper proposes an information fusion approach for gait phase recognition using surface electromyography (sEMG) and kinematics data. Time-domain (TD) features were extracted from the myoelectric data and three data-driven models, specifically Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Artificial Neural Network (ANN), were compared in three different input conditions i.e. sEMG features, hip angle, and their fusion. Gait phase estimation results averaged from 40 healthy participants during normal walking with 10 strides per each demonstrated that the proposed fusion approach has consistently outperformed (p<0.0001) the other two conditions achieving a maximum accuracy of 85.48% with SVM. The findings suggest promising applications in prosthetic motion control and rehabilitative exoskeletons, highlighting the potential for improved user-driven strategies in lower limb prostheses.

pdf (الإنجليزية)

المراجع

H. Pernot, L. De Witte, E. Lindeman, and J. Cluitmans, “Daily functioning of the lower extremity amputee: an overview of the literature,” Clinical rehabilitation, vol. 11, no. 2, pp. 93–106, 1997.

C. L. McDonald, S. Westcott-McCoy, M. R. Weaver, J. Haagsma, and D. Kartin, “Global prevalence of traumatic non-fatal limb amputation,” Prosthetics and orthotics international, p. 0309364620972258, 2021.

B. Ahkami, K. Ahmed, A. Thesleff, L. Hargrove, and M. Ortiz-Catalan, “Electromyography-based control of lower limb prostheses: a systematic review,” IEEE Transactions on Medical Robotics and Bionics, 2023.

B.-Y. Su, J. Wang, S.-Q. Liu, M. Sheng, J. Jiang, and K. Xiang, “A cnn- based method for intent recognition using inertial measurement units and intelligent lower limb prosthesis,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 5, pp. 1032–1042, 2019.

T. Schmalz, S. Blumentritt, and B. Marx, “Biomechanical analysis of stair ambulation in lower limb amputees,” Gait & posture, vol. 25, no. 2, pp. 267–278, 2007.

T. Afzal, K. Iqbal, G. White, and A. B. Wright, “A method for locomotion mode identification using muscle synergies,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 25, no. 6, pp.608–617, 2016.

M. R. Tucker, J. Olivier, A. Pagel, H. Bleuler, M. Bouri, O. Lambercy, J. d. R. Mill ́an, R. Riener, H. Vallery, and R. Gassert, “Control strategies for active lower extremity prosthetics and orthotics: a review,” Journal of neuroengineering and rehabilitation, vol. 12, pp. 1–30, 2015.

S. P. Sitole and F. C. Sup IV, “Continuous prediction of human joint mechanics using emg signals: A review of model-based and model-free approaches,” IEEE Transactions on Medical Robotics and Bionics, 2023.

H. Huang, F. Zhang, L. J. Hargrove, Z. Dou, D. R. Rogers, and K. B. Englehart, “Continuous locomotion-mode identification for prosthetic legs based on neuromuscular–mechanical fusion,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 10, pp. 2867–2875, 2011.

S. Thongpanja, A. Phinyomark, F. Quaine, Y. Laurillau, C. Limsakul, and P. Phukpattaranont, “Probability density functions of stationary surface emg signals in noisy environments,” IEEE Transactions on Instrumentation and Measurement, vol. 65, no. 7, pp. 1547–1557, 2016.

R. Gupta, I. S. Dhindsa, and R. Agarwal, “Continuous angular position estimation of human ankle during unconstrained locomotion,” Biomedical Signal Processing and Control, vol. 60, p. 101968, 2020.

L. Zhang, Z. Li, Y. Hu, C. Smith, E. M. G. Farewik, and R. Wang, “Ankle joint torque estimation using an emg-driven neuromusculoskeletal model and an artificial neural network model,” IEEE Transactions on Automation Science and Engineering, vol. 18, no. 2, pp. 564–573, 2020.

D. Xu, Y. Feng, J. Mai, and Q. Wang, “Real-time on-board recognition of continuous locomotion modes for amputees with robotic transtibial prostheses,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 10, pp. 2015–2025, 2018.

F. Gao, G. Liu, F. Liang, and W.-H. Liao, “Imu-based locomotion mode identification for transtibial prostheses, orthoses, and exoskeletons,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 6, pp. 1334–1343, 2020.

A. D. Keles ̧ and C. A. Yucesoy, “Development of a neural network based control algorithm for powered ankle prosthesis,” Journal of Biomechanics, vol. 113, p. 110087, 2020.

R. Mobarak, A. Tigrini, F. Verdini, A. H. Al-Timemy, S. Fioretti, L. Burattini, and A. Mengarelli, “A minimal and multi-source recording setup for ankle joint kinematics estimation during walking using only proximal information from lower limb,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2024.

J. Zhang, Y. Zhao, F. Shone, Z. Li, A. F. Frangi, S. Q. Xie, and Z.-Q. Zhang, “Physics-informed deep learning for musculoskeletal modeling: Predicting muscle forces and joint kinematics from surface emg,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 484–493, 2022.

N. Sun, M. Cao, Y. Chen, Y. Chen, J. Wang, Q. Wang, X. Chen, and T. Liu, “Continuous estimation of human knee joint angles by fusing kinematic and myoelectric signals,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 30, pp. 2446–2455, 2022.

R. Luo, S. Sun, X. Zhang, Z. Tang, and W. Wang, “A low-cost end-to-end semg-based gait sub-phase recognition system,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 1, pp. 267–276, 2019.

L. Ren, R. K. Jones, and D. Howard, “Predictive modelling of human walking over a complete gait cycle,” Journal of biomechanics, vol. 40, no. 7, pp. 1567–1574, 2007.

I. Mileti, A. Serra, N. Wolf, V. Munoz-Martel, A. Ekizos, E. Palermo, A. Arampatzis, and A. Santuz, “Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion,” Frontiers in Bioengineering and Biotechnology, vol. 8, p. 1169, 2020.

M. Zhang, Q. Wang, D. Liu, B. Zhao, J. Tang, and J. Sun, “Real-time gait phase recognition based on time domain features of multi-mems inertial sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–12, 2021.

W. Wei, F. Tan, H. Zhang, H. Mao, M. Fu, O. W. Samuel, and G. Li, “Surface electromyogram, kinematic, and kinetic dataset of lower limb walking for movement intent recognition,” Scientific Data, vol. 10, no. 1, p. 358, 2023.

Z. Lu, X. Chen, X. Zhang, K.-Y. Tong, and P. Zhou, “Real-time control of an exoskeleton hand robot with myoelectric pattern recognition,” International journal of neural systems, vol. 27, no. 05, p. 1750009, 2017.

S. Micera, J. Carpaneto, and S. Raspopovic, “Control of hand prostheses using peripheral information,” IEEE reviews in biomedical engineering, vol. 3, pp. 48–68, 2010.

M. Jacquelin Perry, “Gait analysis: normal and pathological function,” New Jersey: SLACK, 2010.

J. Chen, X. Zhang, Y. Cheng, and N. Xi, “Surface emg based continuous estimation of human lower limb joint angles by using deep belief networks,” Biomedical Signal Processing and Control, vol. 40, pp. 335– 42, 2018.

X. Zhou, C. Wang, L. Zhang, J. Liu, G. Liang, and X. Wu, “Continuous estimation of lower limb joint angles from multi-stream signals based on knowledge tracing,” IEEE Robotics and Automation Letters, vol. 8, no. 2, pp. 951–957, 2023.

T. Liang, N. Sun, Q. Wang, J. Bu, L. Li, Y. Chen, M. Cao, J. Ma, and T. Liu, “semg-based end-to-end continues prediction of human knee joint angles using the tightly coupled convolutional transformer model,” IEEE Journal of Biomedical and Health Informatics, 2023.

Creative Commons License

هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.

الحقوق الفكرية (c) 2024 مجلة الخوارزمي الهندسية