Novel Physics-Informed Bayesian Fusion Post-Processor for Enhanced Gait Phase Recognition Using Surface Electromyography

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Rami Mobarak;Alessandro Mengarelli;Rami N. Khushaba;Ali H. Al-Timemy;Federica Verdini;Sandro Fioretti;Laura Burattini;Andrea Tigrini
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Abstract

Myoelectric pattern recognition systems serve as a promising predictive control approach for the lower limbs prostheses and exoskeletons. However, their actual deployment is challenged by the signal stochastic nature that could contaminate the decision stream with physiologically implausible transitions, posing safety and metabolic cost concerns on the potential user. Therefore, this study proposes a novel Physics-Informed Bayesian Fusion (PI-BF) post-processor that embeds biomechanical sequentiality constraints into the posterior probabilistic output of the classifiers to suppress unstable transitions and promote natural gait progression. Time-Domain (TD) and Time-Dependent Power Spectrum Descriptors (TD-PSD) features were extracted from the lower limbs muscles surface electromyography (sEMG) signals and classified using Support vector machines (SVM), Artificial neural networks (ANN), K-Nearest Neighbour (KNN), and a CNN-LSTM hybrid deep learning model to predict five phases of gait cycle. The output of these classifiers was followed by the proposed PI-BF postprocessor and it was compared against Bayesian Fusion (BF) Majority voting (MV) as well as the performance without post-processing (WPP) using different numbers of votes from the previous windows. Results shows that PI-BF can increase the classification accuracy by up to 5.5% reaching up to 85% in SIAT-LLMD dataset (40 subjects) using SVM with 3 previous decision windows. It also reduced Transition Detection Difference (TDD) to 0.1 ± 59.8 ms and improved output stability by 5%, as measured by the Instability (INS) index. The proposedPI-BF exhibited consistent improvements in real-time gait phase recognition experiments, achieving classification accuracies of around 90%. These results demonstrate that PI-BF offers a practical, low-complexity solution for enhancing the safety, reliability, and real-time performance of myoelectric control in assistive lower-limb devices.
基于表面肌电图增强步态相位识别的新型物理贝叶斯融合后处理器。
肌电模式识别系统是一种很有前途的下肢假肢和外骨骼预测控制方法。然而,它们的实际部署受到信号随机特性的挑战,这可能会污染决策流,导致生理上不合理的转变,给潜在用户带来安全和代谢成本问题。因此,本研究提出了一种新的物理信息贝叶斯融合(PI-BF)后置处理器,该处理器将生物力学顺序约束嵌入分类器的后验概率输出中,以抑制不稳定的过渡并促进自然的步态进展。利用支持向量机(SVM)、人工神经网络(ANN)、k近邻(KNN)和CNN-LSTM混合深度学习模型对下肢肌肉表面肌电信号进行分类,预测步态周期的5个阶段。这些分类器的输出之后是所提出的PI-BF后处理器,并将其与贝叶斯融合(BF)多数投票(MV)以及使用来自前一个窗口的不同票数的未经后处理(WPP)的性能进行比较。结果表明,在SIAT-LLMD数据集(40个受试者)上,PI-BF使用支持向量机在3个前决策窗口下的分类准确率可提高5.5%,达到85%。根据不稳定性(INS)指数,它还将过渡检测差(TDD)降低到0.1±59.8 ms,并将输出稳定性提高了5%。所提出的PI-BF在实时步态相位识别实验中表现出一致的改进,分类准确率达到90%左右。这些结果表明,PI-BF为增强辅助下肢装置肌电控制的安全性、可靠性和实时性提供了一种实用、低复杂度的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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