Research on Power-Assisted Strategy and Device Based on Muscle Synergy

Hu, Lintao; Luo, Xiuying; Tang, Shangjie; Wu, Xiaoying; Chen, Lin; Zheng, Xiaolin; Hou, Wensheng

doi:10.1109/civemsa45640.2019.9071628

Cited by 2 publications

(1 citation statement)

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“…In this study, the process of analyzing synergies based on human experimental data is illustrated in Figure 3. The raw sEMG signal during walking (Figure 3A) underwent filtering (Figure 3B), and after gait segmentation and normalization, the preprocessed sEMG signal was obtained (Figure 3C), NMF was employed for extracting muscle synergies, ensuring the non-negative attributes of the decomposition matrix [29,30], thereby granting it greater physical interpretability compared with alternative decomposition methodologies. The expression is as follows: The sEMG signals and lower-limb kinematic data were transmitted to the upperlevel computer via WIFI and Bluetooth, respectively, where all data processing tasks were conducted using MATLAB (MatlabR2020b, MathWorks Inc., Natick, MA, USA).…”

Section: Evaluation Index Based On Muscle Synergymentioning

confidence: 99%

Optimizing Exoskeleton Assistance: Muscle Synergy-Based Actuation for Personalized Hip Exoskeleton Control

Ma,

Liu,

Yan

et al. 2024

Actuators

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Exoskeleton robots hold promising prospects for rehabilitation training in individuals with weakened muscular conditions. However, achieving improved human–machine interaction and delivering customized assistance remains a challenging task. This paper introduces a muscle synergy-based human-in-the-loop (HIL) optimization framework for hip exoskeletons to offer more personalized torque assistance. Initially, we propose a muscle synergy similarity index to quantify the similarity of synergy while walking with and without the assistance of an exoskeleton. By integrating surface electromyography (sEMG) signals to calculate metrics evaluating muscle synergy and iteratively optimizing assistance parameters in real time, a muscle synergy-based HIL optimized torque configuration is presented and tested on a portable hip exoskeleton. Iterative optimization explores the optimal and suboptimal assistance torque profiles for six healthy volunteers, simultaneously testing zero torque and predefined assistance configurations, and verified the corresponding muscle synergy similarity indices through experimental testing. In our validation experiments, the assistance parameters generated through HIL optimization significantly enhance muscle synergy similarity during walking with exoskeletal assistance, with an optimal average of 0.80 ± 0.04 (mean ± std), marking a 6.3% improvement over prior assistive studies and achieving 96.4% similarity compared with free walking. This demonstrates that the proposed muscle synergy-based HIL optimization can ensure robotic exoskeleton-assisted walking as “natural” as possible.

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Section: Evaluation Index Based On Muscle Synergymentioning

confidence: 99%