Unit of Advanced Robotics and Human-Centred Technologies, Università Campus Bio-Medico di Roma, Rome, Italy.
INAIL Prosthetic Center, Vigorso di Budrio, Italy.
J Neuroeng Rehabil. 2022 Jan 28;19(1):10. doi: 10.1186/s12984-022-00982-z.
In the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes.
The parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the "Elbow classifier", the "Wrist classifier", and the "Hand classifier" provided the simultaneous control of the elbow, hand, and wrist joints, respectively.
Both the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist).
In this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.
在肌电控制系统领域，模式识别（PR）算法已成为预测涉及超过 2 个自由度（DoFs）运动的复杂肌电模式的首选方法。用于假肢控制的大多数分类策略都是基于单一、分层和并行线性判别分析（LDA）分类器，这些分类器能够区分多达 19 个腕/手运动（在 3DoFs 情况下），同时考虑组合和离散运动。然而，这些策略仅用于同时分类 2DoFs，其使用受到缺乏在线性能指标的限制。本研究引入了一种基于逻辑回归（LR）算法和正则化参数的新分类策略，以提供 3DoFs 运动类别的同时分类。
通过使用仅六个表面肌电传感器，在 15 名健康受试者上测试了基于并行 PR 的策略。通过将肌电图（EMG）电极的数量降至最低，并将分类错误率控制在 10%以下，对 27 个离散和复杂的肘部、手部和腕部运动进行了分类。为此，通过使用三个分类器（每个 DoF 一个）实现了并行分类策略：“肘部分类器”、“手腕分类器”和“手部分类器”，分别提供肘部、手部和腕部关节的同步控制。
评估了离线和实时性能指标，并与 LDA 并行分类结果进行了比较。与 LDA 分类器相比，LR 分类器在所有运动类别（肘部、手部和腕部）的实时识别结果均有统计学上的提高。
本文提出了一种新的基于并行 PR 的策略，用于对多达 3DoFs 进行分类：同时使用三个关节分类器对涉及肘部、手腕和手部的 27 个运动类别进行分类，取得了有前景的结果。
