Design of an Effective Prosthetic Hand System for Adaptive Grasping with the Control of Myoelectric Pattern Recognition Approach

1. Introduction

As an important part for individuals with upper-limb differences, a prosthetic bionic hand is indispensable to keep the structural integrity of the human body. For the study of prosthetic bionic hands, many studies contribute greatly. For example, Dr Paul Chappell from the University of Southampton in the UK developed the Southampton Hand [ 1 ] series based on the observation of human hands and fingers when grasping. In this scheme, each finger has three joints except the thumb, and the motion of two joints at the end are correlatively coupled. However, the hand only has three fingers, which is too small for people to act like ordinary people. Dalley et al. [ 2 ] developed a hand with 16 joints driven by five independent actuators, which can provide eight hand postures. However, the specific control process is too complicated, which is not practical for individuals with upper-limb difference. Another study is the i-Limb hand, which has five independently controlled fingers that is controlled by action signals from two electrodes [ 3 ]. There are also some other classic prosthetic bionic hands, including Okada hand [ 4 ], Stanford/JPL [ 5 ], Shadow hand [ 6 ], Utah/MIThand [ 7 9 ], Hitachi hand [ 10 ], etc. In all, the research on intelligent prosthetic hands is relatively too complicated, resulting in various inconveniences for the individuals with upper-limb difference in practical application. However, due to seeking more freedom, the motor driving system of these prosthetic hands are relatively too complicated in hardware structure and control system, leading to the weak strengthen of hand structure and poor real-time performance. Therefore, improving prosthesis control is a key requirement for enhancing prosthesis satisfaction. By improving intuitive control prosthesis, users may experience decrease cognitive effort and decrease dependency on vision to guide and monitor actions.

Among them, the Ottobock hand [ 11 ] produced by Otto Bock Company in Germany is one of the most successful and widely used prosthetic hands in the world. Although this kind of hand can achieve good accuracy, its control movement is fixed, which could not realize the adaptive grasp of objects of different shapes. In order to realize the adaptive grasping function of the prosthetic bionic hand, many studies begin to concentrate on the feature extraction method and advanced intelligent algorithms.

The surface electromechanical signal (sEMG) has a peak value of 0–6 mv and a frequency range of 0–500 hz is one-dimensional time-series signal recorded by electrodes on the surface of the muscle during neuromuscular system activity [ 12 13 ] have the properties of non-stationary, non-linear, complexity, and large variation. At the same time, when the handicapped wear the prosthetic bionic hand, there will also be sweating of the skin, muscle fatigue [ 14 17 ], ECG noise, device noise [ 18 19 ] and other environmental conditions. These characteristics make it so difficult to analyze surface electromechanical signals under multiple action modes well. Generally, the recognition of human movement intention through electromyographic signals is mainly divided into three steps [ 20 ]: signal preprocessing, feature extraction and feature classification [ 21 23 ]. The signal preprocessing process generally includes signal amplification, filtering and notch. This process is a significant step, which directly affects the reliability of signals [ 24 ]. Feature extraction is used for extracting relevant information from sEMG signal, and rejecting noise and other unimportant components. Feature extraction of sEMG signals mainly includes time domain analysis, frequency domain analysis and time-frequency domain analysis. Time domain analysis includes Integrated EMG, Mean Absolute Value (MAV), Root Mean Square (RMS), Zero Crossing, etc. [ 25 ]. Frequency domain analysis includes Autoregressive coefficient (AR), Modified Median Frequency (MMDF), etc. [ 26 ], and time-frequency domain analysis includes Wavelet Transform (WT), Wigner–Ville distribution (WVD), etc. [ 27 ].

Among the feature extraction methods, time domain analysis is computational simplicity and effective, which is widely used in many studies. For example, Hudgins et al. recognized four forearm motions by using five time domain features including MAV, Mean Absolute Value Slope (MAVS), Zero crossing (ZC), Slope Sign Changes (SSC) and Waveform Length (WL), and gained an average accuracy rate of 91% [ 28 ]. Kim et al. successfully classified four wrist movements by using Integrated Absolute Value (IAV) and Root Mean Square (RMS) [ 29 ].

For sEMG-based control of prosthetic hands, there are many classification algorithms, such as linear discriminant analysis (LDA) [ 30 ], fuzzy logic, artificial neural networks (ANN), and support vector machine (SVM) [ 31 ]. For example, S.M. Mane [ 32 ] used the ANN algorithm for hand gesture recognition; H. Zhang [ 33 ] adopted LDA for sEMG pattern recognition; K. Xing [ 34 ] applied the support vector machine (SVM) into the real-time sEMG pattern recognition system for the control of the prosthetic hand. However, for the control of prosthetic hands, we need to consider two indicators, running speed and recognition accuracy.

To select a suitable algorithm for the control of prosthetic hand, Sumit A [ 35 ] identify different hand movements sEMG signals of prosthesis hand with LDA, SVM and ANN algorithm for comparison, and proved that the LDA algorithm can achieve a high accuracy, as well as faster running behavior than other algorithms. Besides, D. Zhang [ 36 ] also claimed that LDA can achieve better control performance in EMG classification for a prosthetic hand. The same work can also be seen in the work from C. Antuvan [ 37 ]. Therefore, we can conclude that the LDA algorithm can perform better in the classification work in sEMG signals for the control of prosthesis hand, whether in recognition accuracy or in running speed. So, we choose LDA algorithms for intension recognition in our work.

Based on the analysis above, this paper designed an adaptive prosthetic bionic hand with an LDA algorithm. We present the mechanical structure design of our prosthetic bionic hand and also analyze the movement track of the finger. Then the control strategy is designed based on the sEMG signal. In the control process, we firstly collect the sEMG data and segment it with a sliding window for feature extraction. Then the LDA algorithm is applied to recognize the motion intension through these features. Once the algorithm recognizes a certain movement, the system will send the command to the motor of BIT hand C so as to realize the grasping and opening action.

The contribution of this study can be summarized as four aspects: (1) The bionic hand adopts the connecting rod drive design so that the bionic hand can better adapt to the grasping action of objects of different shapes and sizes. This kind of design is more anthropomorphic than the Ottobock bionic hand on the market. (2) In the grasp control strategy of the bionic hand, we adopt two closed-loop control strategies of the current loop and position loop, which makes the control of the bionic hand safer and more reliable. (3) In order to make the hand light, cheap and durable as much as possible, we design it with aluminum alloy, nylon and rubber, which is only 332 g. This kind of design can help people grasp objects more freely than other designs. (4) We adopt LDA linear classifier for the signal detection from the people, which makes the control process faster and more accurate.