*4.1.2. EMG based neuro-fuzzy control method*

A considerable numbers of literature of EMG based control methods show the usage of different methods for EMG processing. This may give a number of options to researchers to conduct experiments for EMG processing in different angles. Most of EMG based assistive robots use surface EMG electrodes for EMG signal detection and few robots use needle electrodes [8, 13]. It was found that all methods of EMG processing belong to one of three main categories: time domain, frequency domain and time-frequency domain [5]. Another impor‐ tant aspect of an EMG based control method is signal classification. Generally, accuracy of EMG based control method highly depends on method of classification and which helps to identify muscles to generate the required output from the EMG based control method [18]. Different robots use different techniques for signal classification and many of them are based on neuro-fuzzy, fuzzy logic and neural network. All assistive robots considered for this review used an EMG signal as its main input signal and the architecture of the controller varies from one type to other. Some of them are based on proportional control and others use advanced control methods such as PID control. In another way, controllers can be classified based on its model as dynamic model [25], muscle model or other method. EMG based control methods of upper-limb exoskeleton robots and prostheses are respectively presented and reviewed in the next subsections. The authors make every possible effort to include all recent EMG based control methods of assistive robots in the next section. The logic for selecting particular control

Different approaches have been proposed by various researchers in the past in order to estimate the muscle torque starting from EMG activation. These methods include neural networks, neuro-fuzzy classifier, hill model *etc* [27]. In the next subsections, several EMG based

The hand exoskeleton robot with EMG control has been developed by researchers from the University of Berlin, Germany [26]. This mainly focuses to use by patients who have limited hand mobility. Figure 8 shows the EMG control method for a hand exoskeleton with blind source separation. The construction of the design allows controlling the motion of finger joints. Researchers have presented the difficulties in the application of EMG algorithms. One such drawback is the identification of the correct muscle responsible for a particular motion. In other words, only a subset of muscles responsible for hand movement is sampled by the surface electrodes [26]. Another difficulty is the EMG signal separation for relevant motion. This needs a suitable process to recover the underlying original signals. Armin *et. al.,* have proposed a blind source separation method to recover the underlying original signals developed by a particular motion of muscle [26]. Initially signals are filtered by a weighted low pass differ‐ ential filter. Then an inverse demixing matrix which, results from an iterative algorithm [4] approximates the separation about 1.5dB for close proximity sensors. Subsequent to the separation, the signals are used for control purposes. However, integration of additional sensors and additional DoF complicates the separation and further, the position of electrodes increases the complexity. Therefore, blind source separation has practical limitations in

method is its key features and novelty.

246 Electrodiagnosis in New Frontiers of Clinical Research

*4.1.1. EMG based control of hand exoskeleton*

working with EMG sensors with force sensors.

**4.1. EMG based control methods of upper-limb exoskeleton robots**

control methods of upper-limb exoskeleton robots are reviewed.

Kiguchi *et al*. [18] have developed an exoskeleton robot and it is controlled based on EMG signals. The robot is used to assist the motion of physically weak persons such as elderly, disabled and injured [37-39]. Although EMG signals directly reflect the human motion intention, it is difficult to control the robotic exoskeleton since the strength of EMG varies with factors like physical and physiological conditions, placement of electrodes, shift of electrodes and high nonlinearity of muscle activity for a certain motion. Therefore, Kiguchi *et al* proposed a neuro-fuzzy controller with EMG signals which provides flexible and adaptive nonlinear control for the exoskeleton robot in real time. The architecture of the controller is shown in the Fig. 9. Mean absolute value (MAV) is used to extract the features of the EMG signal due to its

**Figure 9.** Architecture of neuro-fuzzy controller [18]

effectiveness in real time control compared with other methods such as mean absolute value slope, zero crossing, slope sign changes or wave form length [16, 18]. The hierarchical controller consists mainly of three stages: input signal selection stage, posture selection region stage and neuro-fuzzy control stage. The first stage consists of EMG based control according to the muscle activity levels of the robot and the second stage consists of neuro-fuzzy control according to the motion of the human and finally the controller determines desired torque command for each joint via neuro-fuzzy controller. This helps to realize the effective motion assistance for the robot user. In the first stage, input information to the controller is selected in accordance with the user's muscle activity levels. The control is carried out based on EMG, however, when muscle activity is low, the control signal is generated by force sensors. The second stage of the controller basically works in accordance with the user's arm posture. Different postures may cause different control rules and this leads to complexity of controller. In this situation, multiple neuro-fuzzy controllers have been proposed. The desired torque commands are finally generated by the neuro-fuzzy controller in their last stage.

*4.1.3. EMG controlled orthotic exoskeleton hand*

**Figure 10.** Orthotic exoskeleton hand control by EMG [19]

determine the effectiveness of the natural reaching algorithm.

reaching algorithm [19].

Researchers from Carnegie Mellon University, USA have developed a light weight, low profile orthotic exoskeleton (see Fig. 10) which is controlled by using EMG signals [19]. Matsuoka *et al*. further extended their research on discovering the best control methodology for EMG based control of exoskeleton robots. Their observations are presented through conducting experi‐ ments under three control scenarios [19]. According to [19], they have conducted EMG based control through Binary control algorithm [40], Variable control algorithm [19] and Natural

Recent Trends in EMG-Based Control Methods for Assistive Robots

http://dx.doi.org/10.5772/56174

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The binary control algorithm provides either 'ON' or 'OFF' states to the outputs or actuators based on EMG activity. This is a more primitive type of control method and it does not cover any intermediate state of function. This problem is overcome when adopting a variable control algorithm which defines the intermediate state and guarantees the smooth function of robotic actuators via an EMG signal. The natural reaching algorithm is suitable for subjects who are paralyzed completely in one of the arms. Authors concluded that the suitable control algorithm is one of the important aspects for better control of the exoskeleton robot with an EMG signal. This determines the type of object being carried by user and the type of interaction needed for it. Therefore, identification of the control algorithm enhances the effective use of the EMG signal for exoskeleton robot control. However, the experiments have not been carried out to

This EMG based control method employed controller adaptation to realize the desired motion assistance for any subject. Further, the controller is capable of adapt itself to physical and physiological condition of any user of robot. However, this adaptation of the controller imposes training prior to use the assistive robot and it may take a considerable time resulting in a lack of motivation.

**Figure 10.** Orthotic exoskeleton hand control by EMG [19]

#### *4.1.3. EMG controlled orthotic exoskeleton hand*

effectiveness in real time control compared with other methods such as mean absolute value slope, zero crossing, slope sign changes or wave form length [16, 18]. The hierarchical controller consists mainly of three stages: input signal selection stage, posture selection region stage and neuro-fuzzy control stage. The first stage consists of EMG based control according to the muscle activity levels of the robot and the second stage consists of neuro-fuzzy control according to the motion of the human and finally the controller determines desired torque command for each joint via neuro-fuzzy controller. This helps to realize the effective motion assistance for the robot user. In the first stage, input information to the controller is selected in accordance with the user's muscle activity levels. The control is carried out based on EMG, however, when muscle activity is low, the control signal is generated by force sensors. The second stage of the controller basically works in accordance with the user's arm posture. Different postures may cause different control rules and this leads to complexity of controller. In this situation, multiple neuro-fuzzy controllers have been proposed. The desired torque

commands are finally generated by the neuro-fuzzy controller in their last stage.

in a lack of motivation.

**Figure 9.** Architecture of neuro-fuzzy controller [18]

248 Electrodiagnosis in New Frontiers of Clinical Research

This EMG based control method employed controller adaptation to realize the desired motion assistance for any subject. Further, the controller is capable of adapt itself to physical and physiological condition of any user of robot. However, this adaptation of the controller imposes training prior to use the assistive robot and it may take a considerable time resulting

Researchers from Carnegie Mellon University, USA have developed a light weight, low profile orthotic exoskeleton (see Fig. 10) which is controlled by using EMG signals [19]. Matsuoka *et al*. further extended their research on discovering the best control methodology for EMG based control of exoskeleton robots. Their observations are presented through conducting experi‐ ments under three control scenarios [19]. According to [19], they have conducted EMG based control through Binary control algorithm [40], Variable control algorithm [19] and Natural reaching algorithm [19].

The binary control algorithm provides either 'ON' or 'OFF' states to the outputs or actuators based on EMG activity. This is a more primitive type of control method and it does not cover any intermediate state of function. This problem is overcome when adopting a variable control algorithm which defines the intermediate state and guarantees the smooth function of robotic actuators via an EMG signal. The natural reaching algorithm is suitable for subjects who are paralyzed completely in one of the arms. Authors concluded that the suitable control algorithm is one of the important aspects for better control of the exoskeleton robot with an EMG signal. This determines the type of object being carried by user and the type of interaction needed for it. Therefore, identification of the control algorithm enhances the effective use of the EMG signal for exoskeleton robot control. However, the experiments have not been carried out to determine the effectiveness of the natural reaching algorithm.

**Figure 11.** Hand exoskeleton [24]
