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Appropriate Feature Set and Window Parameters Selection for Efficient Motion Intent Characterization towards Intelligently Smart EMG-PR System
Symmetry ( IF 2.940 ) Pub Date : 2020-10-16 , DOI: 10.3390/sym12101710
Mojisola Grace Asogbon , Oluwarotimi Williams Samuel , Yanbing Jiang , Lin Wang , Yanjuan Geng , Arun Kumar Sangaiah , Shixiong Chen , Peng Fang , Guanglin Li

The constantly rising number of limb stroke survivors and amputees has motivated the development of intelligent prosthetic/rehabilitation devices for their arm function restoration. The device often integrates a pattern recognition (PR) algorithm that decodes amputees’ limb movement intent from electromyogram (EMG) signals, characterized by neural information and symmetric distribution. However, the control performance of the prostheses mostly rely on the interrelations among multiple dynamic factors of feature set, windowing parameters, and signal conditioning that have rarely been jointly investigated to date. This study systematically investigated the interaction effects of these dynamic factors on the performance of EMG-PR system towards constructing optimal parameters for accurately robust movement intent decoding in the context of prosthetic control. In this regard, the interaction effects of various features across window lengths (50 ms~300 ms), increments (50 ms~125 ms), robustness to external interferences and sensor channels (2 ch~6 ch), were examined using EMG signals obtained from twelve subjects through a symmetrical movement elicitation protocol. Compared to single features, multiple features consistently achieved minimum decoding error below 10% across optimal windowing parameters of 250 ms/100 ms. Also, the multiple features showed high robustness to additive noise with obvious trade-offs between accuracy and computation time. Consequently, our findings may provide proper insight for appropriate parameter selection in the context of robust PR-based control strategy for intelligent rehabilitation device.

中文翻译:

适合智能 ​​EMG-PR 系统的有效运动意图表征的适当特征集和窗口参数选择

肢体中风幸存者和截肢者人数的不断增加推动了智能假肢/康复设备的开发,以恢复其手臂功能。该设备通常集成了模式识别 (PR) 算法,该算法从肌电图 (EMG) 信号中解码截肢者的肢体运动意图,其特征是神经信息和对称分布。然而,假肢的控制性能主要依赖于特征集、窗口参数和信号调节等多个动态因素之间的相互关系,这些因素迄今为止很少被联合研究。本研究系统地研究了这些动态因素对 EMG-PR 系统性能的交互影响,以构建最佳参数以在假肢控制的背景下准确地进行稳健的运动意图解码。在这方面,使用 EMG 信号检查了跨窗口长度(50 ms~300 ms)、增量(50 ms~125 ms)、对外部干扰和传感器通道(2 ch~6 ch)的鲁棒性的各种特征的交互作用通过对称运动诱发协议从十二个受试者中获得。与单个特征相比,多个特征在 250 ms/100 ms 的最佳窗口参数中始终实现低于 10% 的最小解码错误。此外,多个特征显示出对加性噪声的高度鲁棒性,在准确性和计算时间之间具有明显的权衡。
更新日期:2020-10-16
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