Surface ElectroMyoGraphy (EMG) signals from the forearm used in prosthetic hand and finger control systems require precise anatomy data of finger muscles that are small and located deep within the forearm. The main problem of this method is that the signal quality depends on the placement of EMG sensor, which can significantly affects the accuracy and precision to estimate joint angles or forces. Moreover, in case of amputees, the location of finger muscles is unknown and needed to be identified manually for EMG recording. As a result, most modern prosthetic hands utilize limited number of muscles with pattern recognition to control finger according to pre-defined grip which is unable to mimic natural finger motion. To address such issue, we used array EMG sensors to obtain EMG signals from all possible positions on the forearm and applied regression method to produce natural finger motion. The signals were analyzed using independent component analysis (ICA) to find the best-fitted independent component (IC) that matches the anatomical data taken after the experiment. Next, from the IC and EMG signals, finger angles were estimated using linear regression model (LRM). Each finger was assigned EMG and IC component for flexion and extension muscles, to assess the possibility of controlling each finger angle separately. We compared the joint angles of each finger between calculated from IC and EMG by correlation coefficients (CC) for all fingers. The average CC values were higher than 0.7, demonstrating the strength of the linear relationship. The different between IC and EMG methods suggests that the IC method can reduce noise and increase the signal to noise ratio. The performance of ICA method showed higher CC value at around 0.2 ± 0.10. In order to confirm the performance of ICA method, we also tested mathematical musculoskeletal model (MSM). The result from this study showed that not only array EMG sensors with ICA significantly improve the quality of signal detected from forearm but also reduce problems of conventional EMG sensors and consequently improve the performance of regression method to imitate natural finger motion.

中文翻译：

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使用具有独立分量分析的线性回归模型，从阵列EMG系统估计手指角度。

假肢手和手指控制系统中使用的前臂的表面肌电图（EMG）信号需要细小且位于前臂深处的手指肌肉的精确解剖数据。这种方法的主要问题是信号质量取决于EMG传感器的位置，这会严重影响估计关节角度或力的准确性和精度。此外，在有截肢者的情况下，手指肌肉的位置未知，需要手动识别以记录EMG。结果，大多数现代假肢手利用有限数量的肌肉进行模式识别，以根据无法模仿自然手指运动的预定抓握力来控制手指。为了解决这个问题，我们使用阵列肌电图传感器从前臂上所有可能的位置获取肌电信号，并应用回归方法产生自然的手指运动。使用独立成分分析（ICA）对信号进行分析，以找到与实验后采集的解剖数据相匹配的最适合的独立成分（IC）。接下来，根据IC和EMG信号，使用线性回归模型（LRM）估算手指角度。为每个手指分配了屈肌和伸展肌的EMG和IC组件，以评估分别控制每个手指角度的可能性。我们通过所有手指的相关系数（CC）比较了从IC和EMG计算出的每个手指的关节角度。平均CC值高于0.7，表明线性关系的强度。IC和EMG方法之间的差异表明，IC方法可以降低噪声并提高信噪比。ICA方法的性能显示出较高的CC值，约为0.2±0.10。为了确认ICA方法的性能，我们还测试了数学肌肉骨骼模型（MSM）。这项研究的结果表明，不仅具有ICA的阵列EMG传感器可以显着提高从前臂检测到的信号的质量，而且还可以减少传统EMG传感器的问题，从而提高了模仿自然手指运动的回归方法的性能。我们还测试了数学肌肉骨骼模型（MSM）。这项研究的结果表明，不仅具有ICA的阵列EMG传感器可以显着提高从前臂检测到的信号的质量，而且还可以减少传统EMG传感器的问题，从而提高了模仿自然手指运动的回归方法的性能。我们还测试了数学肌肉骨骼模型（MSM）。这项研究的结果表明，不仅具有ICA的阵列EMG传感器可以显着提高从前臂检测到的信号的质量，而且还可以减少传统EMG传感器的问题，从而提高了模仿自然手指运动的回归方法的性能。