Author:
Sandoval-Rodriguez C L,Pita-Mejia A C,Villamizar-Mejia R,Tarazona-Romero B E,Lengerke-Perez Omar
Abstract
Abstract
Various previous works have sought to achieve hand prostheses with natural movements. It has been employed tools to recognize patterns of surface electromyographic signals associated with each move. Although many successful studies classify some types of hand movements with high performance, the results show that the speed and strength must be analyzed so that the resulting moves are like those of a natural hand. This study evaluates 23 healthy subjects at two different speeds and six types of movements (pronation, supination, ulnar deviation, radial deviation, flexion, and extension -276 records of SEMG and Velocity). The objective was to obtain a model (transfer function) that would allow the relation of the velocity profiles with the tone of the forearm SEMG signals. The results show models with an average RMSE of 18.55% for slow movements using low-order systems (2). The parameters of the models between subjects are very different, with high coefficients of variation and standard deviations, which implies that the fitting must be for each subject.
Subject
General Physics and Astronomy
Reference19 articles.
1. Development of the hand motion recognition system based on surface EMG using suitable measurement channels for pattern recognition;Nagata,2007
2. The evaluation of the discriminant ability of multiclass SVM in a study of hand motion recognition by using SEMG;Futamata;Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS,2012
3. Comparison of surface and intramuscular EMG pattern recognition for simultaneous wrist/hand motion classification;Smith;Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. EMBS,2013
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献