A Study on the Classification Effect of sEMG Signals in Different Vibration Environments Based on the LDA Algorithm

Myoelectric prosthesis has become an important aid to disabled people. Although it can help people to recover to a nearly normal life, whether they can adapt to severe working conditions is a subject that is yet to be studied. Generally speaking, the working environment is dominated by vibration. This paper takes the gripping action as its research object, and focuses on the identification of grasping intentions under different vibration frequencies in different working conditions. In this way, the possibility of the disabled people who wear myoelectric prosthesis to work in various vibration environment is studied. In this paper, an experimental test platform capable of simulating 0–50 Hz vibration was established, and the Surface Electromyography (sEMG) signals of the human arm in the open and grasping states were obtained through the MP160 physiological record analysis system. Considering the reliability of human intention recognition and the rapidity of algorithm processing, six different time-domain features and the Linear Discriminant Analysis (LDA) classifier were selected as the sEMG signal feature extraction and recognition algorithms in this paper. When two kinds of features, Zero Crossing (ZC) and Root Mean Square (RMS), were used as input, the accuracy of LDA algorithm can reach 96.9%. When three features, RMS, Minimum Value (MIN), and Variance (VAR), were used as inputs, the accuracy of the LDA algorithm can reach 98.0%. When the six features were used as inputs, the accuracy of the LDA algorithm reached 98.4%. In the analysis of different vibration frequencies, it was found that when the vibration frequency reached 20 Hz, the average accuracy of the LDA algorithm in recognizing actions was low, while at 0 Hz, 40 Hz and 50 Hz, the average accuracy was relatively high. This is of great significance in guiding disabled people to work in a vibration environment in the future.

Radiocapitellar joint pressures following transradial amputation increase during elbow motion

This study aimed to compare the contact area, mean pressure, and peak pressure of the radiocapitellar joint (RCJ) in the upper limb after transradial amputation with those of the normal upper limb during elbow flexion and forearm rotation. Testing was performed using ten fresh-frozen upper limbs, and the transradial amputation was performed 5 cm proximal to the radial styloid process. The specimens were connected to a custom-designed apparatus for testing. A pressure sensor was inserted into the RCJ. The biomechanical indices of the RCJ were measured during elbow flexion and forearm rotation in all specimens. There was no significant difference in the contact area between the normal and transradial amputated upper limbs. However, in the upper limbs after transradial amputation, the mean pressure was higher than that in the normal upper limbs at all positions of elbow flexion and forearm rotation. The peak pressure was significantly higher in the upper limbs after transradial amputation than in the normal upper limbs, and was especially increased during pronation at 45° of elbow flexion. In conclusion, these results could cause cartilage erosion in the RCJ of transradial amputees. Thus, methods to reduce the pressure of the RCJ should be considered when a myoelectric prosthesis is developed.

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.