Abstract：
A pattern-recognition (PR)-based myoelectric control system is the trend of future prostheses development. Compared with conventional prosthetic control systems, PR-based control systems provide high dexterity, with many studies achieving >95% accuracy in the last two decades. However, most research studies have been conducted in the laboratory. There is limited research investigating how EMG signals are acquired when users operate PR-based systems in their home and community environments. This study compares the statistical properties of surface electromyography (sEMG) signals used to calibrate prostheses and quantifies the quality of calibration sEMG data through separability indices, repeatability indices, and correlation coefficients in home and laboratory settings. The results demonstrate no significant differences in classification performance between home and laboratory environments in within-calibration classification error (home: 6.33 ± 2.13%, laboratory: 7.57 ± 3.44%). However, between-calibration classification errors (home: 40.61 ± 9.19%, laboratory: 44.98 ± 12.15%) were statistically different. Furthermore, the difference in all statistical properties of sEMG signals is significant (p < 0.05). Separability indices reveal that motion classes are more diverse in the home setting. In summary, differences in sEMG signals generated between home and laboratory only affect between-calibration performance.
摘要：
基于模式识别（PR）的肌电控制系统是未来假肢发展的趋势。与传统的假肢控制系统相比，基于PR的控制系统提供高灵活性，在过去的二十年里，许多研究达到了95%的准确率。然而,大多数研究都是在实验室进行的。关于当用户在其家庭和社区环境中操作基于PR的系统时如何获取EMG信号的研究有限。这项研究比较了用于校准假体的表面肌电图（sEMG）信号的统计特性，并通过可分性指数量化了校准sEMG数据的质量，重复性指标,以及家庭和实验室环境中的相关系数。结果表明，在校准内分类误差方面，家庭和实验室环境之间的分类性能没有显着差异（家庭：6.33±2.13％，实验室：7.57±3.44%）。然而,校准间分类误差(家庭：40.61±9.19%，实验室：44.98±12.15%）有统计学差异。此外，sEMG信号的所有统计特性差异均有统计学意义(p<0.05)。可分性指数表明，在家庭环境中，运动类别更加多样化。总之,家庭和实验室之间产生的sEMG信号的差异仅影响校准之间的性能。
