Mobarak Rami, Mengarelli Alessandro, Khushaba Rami N, Al-Timemy Ali H, Prinsen Erik C, Verdini Federica, Leijendekkers Ruud A, Fioretti Sandro, Burattini Laura, Tigrini Andrea
Department of Information Engineering, Universitá Politecnica delle Marche, Via Brecce Bianche 12, 60131 Ancona, Italy.
Transport for NSW, Alexandria, NSW, Australia.
J Neural Eng. 2025 Aug 27;22(4). doi: 10.1088/1741-2552/adfc9a.
. Reliable control of lower limb prostheses during gait using surface electromyography requires robust decoding of myoelectric signals to ensure safety and efficiency. Conventional myoelectric pattern recognition (PR) methods, which classify features extracted from each window, often yield inaccurate and unstable output, limiting their practical use.. To deal with these issues, two novel temporal myoelectric-based gait phase recognition frameworks are presented. Temporal activation profile (TAP) considers a sequence of features extracted from consecutive windows, and dual activation shots (DAS) using features extracted from the current and a specific preceding window. These methods were tested on (1) publicly available SIAT-LLMD dataset of 40 healthy subjects under different locomotion conditions, and (2) two subjects with transfemoral amputation during normal walking.. TAP and DAS significantly outperformed conventional PR methods, achieving accuracies of 88.50% and 87.97%, respectively, in healthy subjects during normal walking. TAP achieved optimal performance using features extracted from consecutive windows spanning 240 ms in the past, whereas DAS performed best when leveraging features from the current window combined with those from a window 160 ms prior. No significant differences were observed between TAP and DAS under optimal conditions. Both approaches effectively enhanced gait phase recognition performance when applied to transfemoral amputee gait data. The TAP framework achieved the highest performance, surpassing 87.80% accuracy with extended temporal context requirement, and outperforming the DAS approach (82.32%) under pathological conditions.. Both TAP and DAS are robust solutions for gait phase recognition as they stabilize the decision output and reduce classification errors. DAS is more practically feasible due to lower temporal and computational demands, while TAP is more effective in the case of altered neuromuscular activation patterns. The findings of this paper highlight the potential of integrating these methods into real-time prosthetic controllers, ensuring safe and reliable use for patients.
在步态过程中，利用表面肌电图可靠地控制下肢假肢需要对肌电信号进行强大的解码，以确保安全性和效率。传统的肌电模式识别（PR）方法通过对从每个窗口提取的特征进行分类，往往会产生不准确和不稳定的输出，限制了它们的实际应用。为了解决这些问题，提出了两种新颖的基于时间肌电的步态相位识别框架。时间激活剖面（TAP）考虑从连续窗口提取的一系列特征，而双激活快照（DAS）使用从当前窗口和特定的前一个窗口提取的特征。这些方法在以下两个数据集上进行了测试：（1）40名健康受试者在不同运动条件下的公开可用SIAT-LLMD数据集，以及（2）两名经大腿截肢者在正常行走过程中的数据集。TAP和DAS显著优于传统PR方法，在健康受试者正常行走过程中，准确率分别达到88.50%和87.97%。TAP使用过去240毫秒内连续窗口提取的特征实现了最佳性能，而DAS在利用当前窗口与160毫秒前窗口的特征相结合时表现最佳。在最佳条件下，TAP和DAS之间未观察到显著差异。当应用于经大腿截肢者的步态数据时，两种方法都有效地提高了步态相位识别性能。TAP框架实现了最高性能，在扩展时间上下文要求下准确率超过87.80%，在病理条件下优于DAS方法（82.32%）。TAP和DAS都是步态相位识别的强大解决方案，因为它们稳定了决策输出并减少了分类错误。由于较低的时间和计算需求，DAS在实际应用中更可行，而在神经肌肉激活模式改变的情况下，TAP更有效。本文的研究结果突出了将这些方法集成到实时假肢控制器中的潜力，确保患者安全可靠地使用。
