BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Yin G, Zhang X, Chen D, Li H, Chen J, Chen C, Lemos S. Processing Surface EMG Signals for Exoskeleton Motion Control. Front Neurorobot 2020;14:40. [PMID: 32765250 DOI: 10.3389/fnbot.2020.00040] [Cited by in Crossref: 14] [Cited by in F6Publishing: 15] [Article Influence: 7.0] [Reference Citation Analysis]
Number Citing Articles
1 Karakish M, Fouz MA, Elsawaf A. Gait Trajectory Prediction on an Embedded Microcontroller Using Deep Learning. Sensors 2022;22:8441. [DOI: 10.3390/s22218441] [Reference Citation Analysis]
2 Fong J, Bernacki K, Pham D, Shah R, Tan Y, Oetomo D. Exploring the Utility of Crutch Force Sensors to Predict User Intent in Assistive Lower Limb Exoskeletons. 2022 International Conference on Rehabilitation Robotics (ICORR) 2022. [DOI: 10.1109/icorr55369.2022.9896511] [Reference Citation Analysis]
3 Chen B, Chen C, Hu J, Nguyen T, Qi J, Yang B, Chen D, Alshahrani Y, Zhou Y, Tsai A, Frush T, Goitz H. A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand. Front Neurorobot 2022;16:880073. [DOI: 10.3389/fnbot.2022.880073] [Reference Citation Analysis]
4 Fuentes-alvarez R, Hernandez JH, Matehuala-moran I, Alfaro-ponce M, Lopez-gutierrez R, Salazar S, Lozano R. Assistive robotic exoskeleton using recurrent neural networks for decision taking for the robust trajectory tracking. Expert Systems with Applications 2022;193:116482. [DOI: 10.1016/j.eswa.2021.116482] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
5 Wei C, Wang H, Hu F, Zhou B, Feng N, Lu Y, Tang H, Jia X. Single-channel surface electromyography signal classification with variational mode decomposition and entropy feature for lower limb movements recognition. Biomedical Signal Processing and Control 2022;74:103487. [DOI: 10.1016/j.bspc.2022.103487] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
6 Lian P, Ma Y, Zheng L, Xiao Y, Wu X. A Three-Step Hill Neuromusculoskeletal Model Parameter Identification Method Based on Exoskeleton Robot. J Intell Robot Syst 2022;104. [DOI: 10.1007/s10846-022-01585-5] [Reference Citation Analysis]
7 Tiboni M, Borboni A, Vérité F, Bregoli C, Amici C. Sensors and Actuation Technologies in Exoskeletons: A Review. Sensors (Basel) 2022;22:884. [PMID: 35161629 DOI: 10.3390/s22030884] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
8 Tang Z, Yu H, Yang H, Zhang L, Zhang L. Effect of velocity and acceleration in joint angle estimation for an EMG-Based upper-limb exoskeleton control. Comput Biol Med 2021;141:105156. [PMID: 34942392 DOI: 10.1016/j.compbiomed.2021.105156] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
9 Zhang Y, Cao G, Ling Z, Li W, Cheng H, He B, Cao S, Zhu A. A Multi-Information Fusion Method for Gait Phase Classification in Lower Limb Rehabilitation Exoskeleton. Front Neurorobot 2021;15:692539. [PMID: 34795571 DOI: 10.3389/fnbot.2021.692539] [Reference Citation Analysis]
10 Esposito D, Centracchio J, Andreozzi E, Gargiulo GD, Naik GR, Bifulco P. Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey. Sensors (Basel) 2021;21:6863. [PMID: 34696076 DOI: 10.3390/s21206863] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
11 Baud R, Manzoori AR, Ijspeert A, Bouri M. Review of control strategies for lower-limb exoskeletons to assist gait. J Neuroeng Rehabil 2021;18:119. [PMID: 34315499 DOI: 10.1186/s12984-021-00906-3] [Cited by in Crossref: 25] [Cited by in F6Publishing: 26] [Article Influence: 25.0] [Reference Citation Analysis]
12 Zhou Y, Chen C, Cheng M, Alshahrani Y, Franovic S, Lau E, Xu G, Ni G, Cavanaugh JM, Muh S, Lemos S. Comparison of machine learning methods in sEMG signal processing for shoulder motion recognition. Biomedical Signal Processing and Control 2021;68:102577. [DOI: 10.1016/j.bspc.2021.102577] [Cited by in Crossref: 9] [Cited by in F6Publishing: 8] [Article Influence: 9.0] [Reference Citation Analysis]
13 Lora-millan JS, Hidalgo AF, Rocon E. An IMUs-Based Extended Kalman Filter to Estimate Gait Lower Limb Sagittal Kinematics for the Control of Wearable Robotic Devices. IEEE Access 2021;9:144540-54. [DOI: 10.1109/access.2021.3122160] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
14 Jacob S, Alagirisamy M, Xi C, Balasubramanian V, Srinivasan R, R. P, Jhanjhi NZ, Islam SMN. AI and IoT-Enabled Smart Exoskeleton System for Rehabilitation of Paralyzed People in Connected Communities. IEEE Access 2021;9:80340-80350. [DOI: 10.1109/access.2021.3083093] [Cited by in Crossref: 6] [Cited by in F6Publishing: 6] [Article Influence: 6.0] [Reference Citation Analysis]
15 Li W, Cao G, Zhu A. Review on Control Strategies for Lower Limb Rehabilitation Exoskeletons. IEEE Access 2021;9:123040-60. [DOI: 10.1109/access.2021.3110595] [Cited by in Crossref: 6] [Cited by in F6Publishing: 8] [Article Influence: 6.0] [Reference Citation Analysis]