Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications
Online Finger Control Using High-Density EMG and Minimal Training Data for Robotic Applications
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DOI：
10.1109/lra.2018.2885753
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发表时间：
2019-04-01
影响因子：
5.2
通讯作者：
Farina, Dario
中科院分区：
文献类型：
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作者：
Barsotti, Michele;Dupan, Sigrid;Farina, Dario
手部损伤会对生活质量产生深远的影响。这推动了灵巧假肢和矫形装置的发展。然而，它们对神经肌肉接口的控制仍然具有挑战性。此外，现有的肌肉控制接口通常需要广泛的校准。我们提出了一种最低限度监督的在线肌肉控制系统，用于基于脊回归的比例和同步手指力估计，仅使用单个手指任务进行训练。我们使用从高密度肌电图 (EMG) 记录中提取的两个特征集来比较该系统的性能：EMG 线性包络 (ENV) 和非线性 EMG 到肌肉激活映射 (ACT)。八名肢体完整的参与者接受了在线目标达成任务的测试。平均而言，受试者分别击中 85%+/- 9% 和 91%+/- 11% 具有 ENV 和 ACT 特征的单指目标。组合手指目标的命中率下降至 29%+/- 16% (ENV) 和 53%+/- 23% (ACT)。因此，非线性变换（ACT）提高了性能，导致更高的完成率和更稳定的控制，特别是对于未经训练的运动类别（更好的泛化）。这些结果证明了通过使用最小的单手指任务训练集对非线性 EMG 特征进行回归，在完整受试者中进行比例多手指控制的可行性。
A hand impairment can have a profound impact on the quality of life. This has motivated the development of dexterous prosthetic and orthotic devices. However, their control with neuromuscular interfacing remains challenging. Moreover, existing myocontrol interfaces typically require an extensive calibration. We propose a minimally supervised, online myocontrol system for proportional and simultaneous finger force estimation based on ridge regression using only individual finger tasks for training. We compare the performance of this system when using two feature sets extracted from high-density electromyography (EMG) recordings: EMG linear envelope (ENV) and non-linear EMG to muscle activation mapping (ACT). Eight intact-limb participants were tested using online target reaching tasks. On average, the subjects hit 85%+/- 9% and 91%+/- 11% of single finger targets with ENV and ACT features, respectively. The hit rate for combined finger targets decreased to 29%+/- 16% (ENV) and 53%+/- 23% (ACT). The nonlinear transformation (ACT) therefore improved the performance, leading to higher completion rate and more stable control, especially for the non-trainedmovement classes (better generalization). These results demonstrate the feasibility of proportional multiple finger control in intact subjects by regression on non-linear EMG features with a minimal training set of single finger tasks.