**Abstract**

The main goal of this research is to develop a control strategy for an underactuated robotic hand, based on surface electromyography (sEMG) signal obtained from a wireless Myo gesture armband, to distinguish six, several hand movements. The pattern recognition system is employed to analyze these gestures and consists of three main parts: segmentation, feature extraction, and classification. A series of 150 trials is carried out for each movement and it is established which was most suitable for electromyography signals that can be later used in recognition systems. A backpropagation neural network was used as a classifier. The architecture has a hidden network and six output layers. The number of neurons of the hidden network (20) was determined based on the performance in training progress. The proposed system is tested on datasets extracted from five healthy subjects. A great accuracy (94.94% correct assessment). between the experimentally values and those predicted by the artificial neural network (ANN) was achieved. In addition, kinematic analysis of the proposed underactuated hand has been carried out to verify the motion range of the joints. Simulations and experiments are carried out to verify the effectiveness of the proposed fingers mechanism and the hand prosthesis to generate grasp or postures.

**Keywords:** underactuated prosthesis, electromyography signal, Myo armband gestures, four-bar mechanism

### **1. Introduction**

Due to its use in majority of activities of daily living (ADL), the human hand is one of the most essential body parts, which enables humans to perform basic daily activities ranging from hand gestures to object manipulation [1]. Amputation results from accidents at work, armed conflicts, diseases, and malformations [2],

and it is a severe mental and physical trauma with the loss of both motor and sensory perceptions [3].

High-performance prosthetic hands significantly improve the quality of life for upper limb amputees. Passive prosthetic hands are lightweight, robust, and quiet but can only perform a limited subset of activities. Therefore, researchers have investigated externally powered prosthetic hands for upper limb amputees for more than a century. Regarding the prostheses development, the main contributions are the robotic hand designs such as Vincent hand [4], iLimb hand [5], iLimb Pulse [6], Bebionic hand [7], Bebionic hand v2 [8], Michelangelo hand [9], Metamorphic hand [10], etc. (**Figure 1**). These devices, which emulate with human hand, are characterized by complex systems based on microprocessor technology.

All these myoelectric prostheses harness the EMG signals of residual limb muscles to trigger the function of a robotic prosthetic arm or hand. While these devices have been greatly improved over the past decade, they are still limited due to their number of controllable degrees of freedom, intuitiveness, and reliability. Additional restrictions include weight, limited battery life, cost (there are very expensive due to the amount of actuators, sensors, and the electronics involved), and the user's inability to control multiple degrees of freedom simultaneously and consistently [11]. Therefore, despite numerous advances in the field, rates of user abandonment for upper limb prosthetic systems remain high.

Underactuated robotic prosthesis (URP) is an emerging research direction in the field of robotic medical devices. The control input of the underactuated prostheses is less than the degree of freedom of the system. It has the advantages of

**Figure 1.**

*(a) Vincent hand (Vincent Systems), (b) iLimb hand (touch bionics), (c) iLimb pulse (touch bionics), (d) Bebionic hand (RSL steeper), (e) Bebionic hand v2 (RSL steeper), and (f) Michelangelo hand (Otto bock).*

*Control Strategy for Underactuated Multi-Fingered Robot Hand Movement Using… DOI: http://dx.doi.org/10.5772/intechopen.93767*

lightweight, low energy consumption, and excellent performance. Compared with full-drive prostheses, URP has fewer drives while maintaining the same DOF, thus reducing mass, volume, and energy consumption. It is ideal for applications where quality, volume, low power consumption, and low cost are desirable.

Natural muscles provide mobility in response to nerve impulses. Electromyography (EMG) measures the electrical activity of muscles in response to a nerve's stimulation. Surface electromyography (SEMG) is a noninvasive method of measurement of the bioelectrical activity of muscles. EMG signals have been used extensively in the identification of user intention to potentially control assistive devices such as smart wheelchairs, exoskeletons, and prosthetic devices [4, 7, 8]. Feature extraction and signals' classification are essential subsystems of this approach.

Pattern recognition-based myoelectric control consists of feature extraction and feature classification of segmented data in signal processing to command to the motor controller. Some signal processing may include feature reduction or feature selection between extraction and classification, depending on the number of features. In general, various features are extracted in time, frequency, and time-frequency to identify the information content of EMG signals [12]. Surface electromyography (sEMG) signals are information bearers that correspond to the hand posture intention. Finite gestures could be identified [13], if considering that the characteristics extracted from each sEMG signal are directly related to the gesture and the number of gestures (including their positions and speeds).
