Celadon Nicolò, Došen Strahinja, Binder Iris, Ariano Paolo, Farina Dario
Center for Sustainable Futures@PoliTo, Fondazione Istituto Italiano di Tecnologia, Torino, Italy.
Institute for Neurorehabilitation Systems, University Medical Center Göttingen, Göttingen, Germany.
J Neuroeng Rehabil. 2016 Aug 4;13(1):73. doi: 10.1186/s12984-016-0172-3.
The importance to restore the hand function following an injury/disease of the nervous system led to the development of novel rehabilitation interventions. Surface electromyography can be used to create a user-driven control of a rehabilitation robot, in which the subject needs to engage actively, by using spared voluntary activation to trigger the assistance of the robot.
The study investigated methods for the selective estimation of individual finger movements from high-density surface electromyographic signals (HD-sEMG) with minimal interference between movements of other fingers. Regression was evaluated in online and offline control tests with nine healthy subjects (per test) using a linear discriminant analysis classifier (LDA), a common spatial patterns proportional estimator (CSP-PE), and a thresholding (THR) algorithm. In all tests, the subjects performed an isometric force tracking task guided by a moving visual marker indicating the contraction type (flexion/extension), desired activation level and the finger that should be moved. The outcome measures were mean square error (nMSE) between the reference and generated trajectories normalized to the peak-to-peak value of the reference, the classification accuracy (CA), the mean amplitude of the false activations (MAFA) and, in the offline tests only, the Pearson correlation coefficient (PCORR).
The offline tests demonstrated that, for the reduced number of electrodes (≤24), the CSP-PE outperformed the LDA with higher precision of proportional estimation and less crosstalk between the movement classes (e.g., 8 electrodes, median MAFA ~ 0.6 vs. 1.1 %, median nMSE ~ 4.3 vs. 5.5 %). The LDA and the CSP-PE performed similarly in the online tests (median nMSE < 3.6 %, median MAFA < 0.7 %), but the CSP-PE provided a more stable performance across the tested conditions (less improvement between different sessions). Furthermore, THR, exploiting topographical information about the single finger activity from HD-sEMG, provided in many cases a regression accuracy similar to that of the pattern recognition techniques, but the performance was not consistent across subjects and fingers.
The CSP-PE is a method of choice for selective individual finger control with the limited number of electrodes (<24), whereas for the higher resolution of the recording, either method (CPS-PA or LDA) can be used with a similar performance. Despite the abundance of detection points, the simple THR showed to be significantly worse compared to both pattern recognition/regression methods. Nevertheless, THR is a simple method to apply (no training), and it could still give satisfactory performance in some subjects and/or simpler scenarios (e.g., control of selected fingers). These conclusions are important for guiding future developments towards the clinical application of the methods for individual finger control in rehabilitation robotics.
恢复神经系统损伤/疾病后手功能的重要性促使了新型康复干预措施的发展。表面肌电图可用于创建由用户驱动的康复机器人控制，其中受试者需要通过利用剩余的自主激活来触发机器人的辅助，从而积极参与。
本研究调查了从高密度表面肌电信号（HD-sEMG）中选择性估计单个手指运动的方法，同时尽量减少其他手指运动之间的干扰。使用线性判别分析分类器（LDA）、通用空间模式比例估计器（CSP-PE）和阈值化（THR）算法，对九名健康受试者（每次测试）进行在线和离线控制测试，评估回归情况。在所有测试中，受试者执行等长力跟踪任务，由移动视觉标记引导，指示收缩类型（屈曲/伸展）、期望的激活水平以及应移动的手指。结果测量指标包括参考轨迹与生成轨迹之间的均方误差（nMSE），以参考轨迹的峰峰值进行归一化、分类准确率（CA）、误激活的平均幅度（MAFA），仅在离线测试中包括皮尔逊相关系数（PCORR）。
离线测试表明，对于电极数量减少（≤24个）的情况，CSP-PE在比例估计精度更高且运动类别之间串扰更少方面优于LDA（例如，8个电极时，MAFA中位数约为0.6%对1.1%，nMSE中位数约为4.3%对5.5%）。LDA和CSP-PE在在线测试中的表现相似（nMSE中位数<3.6%，MAFA中位数<0.7%），但CSP-PE在所有测试条件下提供了更稳定的性能（不同测试之间的改善较小）。此外，THR利用HD-sEMG中关于单个手指活动的地形信息，在许多情况下提供了与模式识别技术相似的回归精度，但性能在不同受试者和手指之间不一致。
对于电极数量有限（<24个）的选择性单个手指控制，CSP-PE是首选方法，而对于更高分辨率的记录，两种方法（CPS-PA或LDA）均可使用且性能相似。尽管检测点丰富，但简单的THR与两种模式识别/回归方法相比明显较差。然而，THR是一种易于应用的方法（无需训练），并且在某些受试者和/或更简单的场景（例如，选定手指的控制）中仍可给出令人满意的性能。这些结论对于指导康复机器人中单个手指控制方法的临床应用的未来发展具有重要意义。
