Myoelectric signal is known as an alternative human-machine interface (HMI) for people with motor disability in dealing with assisting robots and rehabilitation devices. This chapter examines a myoelectric HMI in real-time application and compares its performance with traditional tools. It also studies the manifestation of fatigue in long-term muscular activities and its impact on ultimate performance. The core of applied HMI is built on the support vector machine as a classifier. The experiments confirm that the myoelectric HMI is a reliable alternative to traditional HMI. Meanwhile, they show a significant decline in the dominant frequency of myoelectric signals during long-term applications.
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Since most disabled people have problems manipulating current assistive robots and rehabilitation devices that employ traditional user interfaces, such as a joystick and/or keyboard, they need more advanced hands-free human-machine interfaces (HMIs). Surface myoelectric signal (MES) is a biological signal collected non-invasively from surface of the skin covering the muscles, and contains rich information to identify neuromuscular activities. A myoelectric HMI uses MES as an input signal to recognize various patterns of muscular activities and employs them to produce the effect of user’s intention on its output. It eases the interaction with electric devices, assisting robots, or rehabilitating devices to the minimum level of movements for the disabled.
A pattern recognition-based myoelectric HMI recognizes input patterns by classifying the signal features, and outputs their corresponding commands, hence, the selection of effective features and a classifier that provide accurate as well as fast reactions are crucial issue (Asghari-Oskoei & Hu, 2007). The application of pattern recognition to myoelectric HMI was first introduced in the 1960-70s (Englehart et al., 2001b); however, due to limited acquisition instruments and computing capacity at that time, real time control was not feasible. (Hudgins et al., 1993) were pioneers in developing a real time pattern-recognition-based myoelectric HMI. Using time domain (TD) features and a multilayer perceptron (MLP) neural network, they succeeded in classifying four types of upper limb motion, with an accuracy of approximately 90%. This work was continued over the last fifteen years, by employing various classifiers, such as LDA, MLP/RBF neural networks, time-delayed ANN, Fuzzy, NEURO-Fuzzy, Fuzzy ARTMAP networks, Fuzzy-MINMAX networks, Gaussian mixture models (GMM), and hidden Markov models (HMM). (Vuskovic & Du, 2002) introduced a modified version of a fuzzy ARTMAP network to classify prehensile myoelectric signals. (Englehart et al., 2001a) showed that linear discriminant analysis (LDA) outperforms MLP on time-scale features which are dimensionally reduced by PCA.
In addition, significant results were achieved using probabilistic approaches. (Chan & Englehart, 2005) applied a hidden Markov model (HMM) to discriminate six classes of limb movement based on a four-channel MES. It resulted in an average accuracy of 94.63%, which exceeded an MLP based classifier used in (Englehart et al., 2003) (93.27%). Furthermore, (Huang et al., 2005) and (Fukuda et al., 2003) developed a Gaussian mixture model (GMM) as a classifier; the former showed an accuracy of approximately 97%. (Englehart et al., 2003) introduced a continuous classification scheme that provided more robust results for a shortened segment length of signal, and high speed controllers.