**1. Introduction**

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Any person would like to spend his or her entire life time as an individual without becoming a dependant person by any means. Nonetheless, there are several instances where a human being would fail to achieve this due to physical problems which preventing him/her from acting as an individual. In most cases, after a stroke, brain or orthopedic trauma, brain damage due to an accident or a cognitive disease the victim will definitely have to undergo physical or cognitive rehabilitation in order to get him used to changed body conditions. In modern society, a considerable percentage of population is physically weak due to aging, congenital diseases, physical diseases and occupational hazards [1, 2]. Such people need a dexterous assistive methodology to regain the normal activities of daily living (ADL). Not only they, those with a missing limb (e.g. due to an amputation), should also be furnished with necessary aids which would enable them to regain the individuality. The development of proper devices for the purpose of rehabilitation, human power assistance and as replacements to body parts has a long history [3, 4] which has reached a high point due to recent developments in technology, such as robotics, biomedical signal processing, soft computing and advances in sensors and actuators over the past few decades. With many advances, capabilities and potential, still biological signal based control has a long way to go before reaching the realm of professional and commercial applications [5].

Most of the studies [5-7] that considered the integration of biomedical signal processing with robotics have achieved tremendous development in the area of assistive robotics. Assistive robots can mainly be classified into three areas: orthoses, prostheses and other types. An orthosis is worn as an external device to the existing body part, while a prosthesis is used as

© 2013 Gopura et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Gopura et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

a replacement for the lost body part [5, 8]. The meal assistive robot [9], assistive wheel chair [10] and assistive humanoid robot [10] are some examples of the other types of assistive robots. Both orthoses and prostheses directly interact physically and cognitively with the wearer. Therefore, they are expected to provide physiological and mental comfort to the user, without letting the user feel a difference while functioning/assisting correctly to perform the required motion. However, the controlling of the robot according to human motion intention is not an easy task [11, 12]. Therefore, in the integration of the human with a robot, the selection of a proper control input signal to reflect the correct motion intention would be very important. So far research is being carried out considering different biological signals such as Electromyog‐ raphy (EMG), Mechnanomyogram (MMG), Electroencephalography (EEG) Electrooculogra‐ phy (EOG) and Electrocorticogram (EcoG) [13, 14, 15] as the main input signal to the robot controller. Among them, the EMG signal, which is the measurement of the electrical activity of muscles at rest and during contraction, has obtained promising results in the case of controlling robotic prostheses and orthoses by correctly interpreting the human motion intention [16, 17]. Basically, EMG based control is a sophisticated technique concerned with detection, processing, classification and application of EMG to control assistive robots. EMG can be applied to control assistive robots in various manners by considering data acquisition, feature extraction and classification [18]. Some of the available exoskeleton robots controlled based on EMG are the orthotic exoskeleton hand [19], exoskeleton robot for tremor suppression [20], SUEFUL-7 [6] etc. In addition, DEKA Arm [21], Saga Prosthetic Arm [22], and Manus Hand [23] are some of the available prosthetic devices with EMG based controlling. Many researchers from different institutions have been working on EMG based control methods over several decades and their contribution has greatly influenced a new era of EMG based control of assistive robots [5, 24, 25].

**2. Procedure of EMG detection**

**Figure 1.** EMG signal extraction process

Detection of EMG signals can be done mainly in two ways, namely non-invasive and invasive [5]. Surface EMG (sEMG) electrodes are used for the former, while intramuscular EMG (iEMG) electrodes are used for the latter. Placement of surface EMG electrodes is comparatively easier than intramuscular EMG electrodes. However noise and other disturbances are inherent in surface EMG detection [5]. Intramuscular EMG electrodes are placed close to the Motor Unit Action Potentials (MUAP), and as a result the influence of other disturbances is not dominant. It provides a better accuracy and repeatability of the EMG signal [25]. As shown in Fig. 1, the EMG extraction process includes several steps. The initial step is the selection of the most significant muscle of the human body relevant to the required motion. After the muscle is selected, the next important step is the placement of electrodes. In the case of sEMG, the electrodes should be placed in the belly area of the muscle for maximum signal extraction.

Recent Trends in EMG-Based Control Methods for Assistive Robots

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The electrode should be placed onto the relevant muscle after cleaning the skin surface. There are a few types of surface electrodes, some of which need a gel [31] to be applied between the skin and the electrode and some [32] which instead use an adhesive tape to ensure proper contact between the muscle and the electrode. Signals from several electrodes are then fed into the input box and subsequently passed to the amplifier. The output of the amplifier can be fed into a computer via data cards or any other data communication interface and can be recorded or manipulated in the required way. In most EMG based control methods a raw EMG signal is processed to extract the features of the signal. Several feature extraction methods are available for this purpose [5]: mean absolute value, mean absolute value slope, waveform length, zero crossings, root mean square value, *etc*. The features of the raw EMG signal have to be extracted in real time to use EMG as input signals to the controller of the assistive robots. Most robots use Root Mean Square (RMS) as the feature extraction method of raw EMG mainly

There are a number of commercially developed EMG acquisition systems available [31-34] (see Fig. 2). They could be used for both medical and research purposes. Some of the leading EMG acquisition system manufacturers are Nihon Kohden Co. [31], Delsys [32], BioSemi [33], and Cambridge Electronic Design [34]. The Delsys EMG system shown in Fig. 2(a) is widely used in research, whilst the Nihon Kohden EMG system shown in Fig. 2(b) is widely used for medical applications. Nevertheless, there are other models in Nihon Kohden which also support for research [31]. The BioSemi EMG system shown in Fig. 2(c) could also be used in research applications. The next section will explain the classification of EMG based control methods

due to ease of analyzing real time information of EMG signal.

This chapter presents a comprehensive review of EMG based control methods of recent upperlimb orthoses and prostheses. Initially, it explains the detection and processing of EMG and available EMG extraction systems. Next, ways of categorization of EMG based control methods are explained. Here EMG based control methods are categorized in detail, based on the structure of the controller algorithm, as pattern recognition based controls and non-pattern recognition based controls. Most of available control methodologies used with assistive robots are pattern recognition based controls [18, 19, 24, 26, 27]. The control methods belonging to non-pattern recognition based controls [28-30] are rare. Further, comparison of EMG based control methods of upper-limb exoskeleton robots and upper-limb prostheses are presented considering the features of the control method. In addition, the conclusion and future direc‐ tions of EMG based control methods of assistive robots are described.

The next section focuses on the procedure of EMG detection and processing. Section 3 describes the categorization of EMG based control methods. The review of EMG based control methods of upper-limb exoskeleton robots and upper-limb prostheses together with a comparison of control methods are presented in section 4. The final section briefly outlines the conclusion and future directions.
