UPPER EXTREMITY LIMB FUNCTION DISCRIMINATION USING EMG SIGNAL ANALYSIS
UPPER EXTREMITY LIMB FUNCTION DISCRIMINATION USING EMG SIGNAL ANALYSIS
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DOI：
10.1109/tbme.1983.325162
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发表时间：
1983-01-01
影响因子：
4.6
通讯作者：
WILLSKY, AS
中科院分区：
文献类型：
--
作者：
DOERSCHUK, PC;GUSTAFSON, DE;WILLSKY, AS
提出了一种利用表面肌电信号识别前臂和腕关节功能的信号分析方法。数据从放置在前臂近端的四个电极获得。分析的功能包括腕关节屈曲/伸展、腕关节外展/内收和前臂旋前/旋后。多变量自回归模型推导出每个功能，歧视进行了多模型假设检测技术。这种方法通过包括空间相关性并通过使用基于所有肢体功能概率的时间历史分析的更广义的检测原理来扩展Graffic和Cline [1]的工作。如果肌电信号是平稳的高斯-马尔可夫过程，这些概率是问题的充分统计量。正常受试者的实验结果表明，使用的信号的空间和时间相关性的优势。这种技术应该是有用的，在产生控制信号的假肢装置。
A signal analysis technique is developed for discriminating a set of lower arm and wrist functions using surface EMG signals. Data wete obtained from four electrodes placed around the proximal forearm. The functions analyzed included wrist flexion/extension, wrist abduction/adduction, and forearm pronation/supination. Multivariate autoregression models were derived for each function; discrimination was performed using a multiple-model hypothesis detection technique. This approach extends the work of Graupe and Cline [1] by including spatial correlations and by using a more generalized detection philosophy, based on analysis of the time history of all limb function probabilities. These probabilities are the sufficient statistics for the problem if the EMG data are stationary Gauss-Markov processes. Experimental results on-normal subjects are presented which demonstrate the advantages of using the spatial and time correlation of the signals. This technique should be useful in generating control signals for prosthetic devices.