Feature and Channel Selection Using Correlation Based Method for Hand Posture Classification in Multiple Arm Positions

In this work we propose a method based on correlation-based feature selection (CFS) to select features and channels for pattern recognition control of upper-limb prostheses. This method was applied on features extracted from myoelectric signals acquired from two able-bodied subjects and one individual with transradial amputation while contracting the muscles as to perform five functional hand postures in nine arm positions. The classification accuracy increased by using CFS for the able-bodied, while no statistical improvements has been highlighted for the amputee subject. The channels selected by this approach were mainly placed on the posterior side of the forearm which might reflect importance role of the extensor muscles over the flexor muscle when performing these hand postures. Further analysis with bigger dataset will be conducted to validate these preliminary findings

Comparative study of segmentation and feature extraction method on finger movement

Myoelectric control prostheses fingers are a popular developing clinical option that offers an amputee person to control their artificial fingers by recognizing the contacting muscle residual informs of electromyography (EMG) signal. Lower performance of recognition system always has been the main problem in producing the efficient prostheses finger. This is due to the inefficiency of segmentation and feature extraction in EMG recognition system. This paper aims to compare the most used overlapping segmentation scheme and time domain feature extraction method in recognition system respectively. A literature review found that a combination of Hudgins and Root Mean Square (RMS) methods is a possible way of improving feature extraction. To proof this hypothesis, an experiment was conducted by using a dataset of ten finger movements that has been pre-processed. The performance measurement considered in this study is the classification accuracy. Based on the classification accuracy results for the three common overlapping segmentation schemes, the smaller the window size with larger increment windows produce better accuracy but it will degrade the computational time. For feature extraction, the proposed Hudgins with RMS feature showed an improvement of average accuracy for ten finger movements by 0.74 and 3 per cent compared to Hudgins and RMS alone. Future study should incorporate more advance classification accuracy to improve the study

Overcoming the information overload problem in a multiform feedback-based virtual reality system for hand motion rehabilitation: healthy subject case study

The use of composite multiple feedback in a newly proposed virtual reality system enables the patient to perceive similar real-world performance in the virtual world. However, it might cause information overload, which makes the patient feel confused and distracted during training. The aim of this study is to investigate the effectiveness of having separate function-specific feedback pre-training prior to the final multiform feedback task. During the evaluating tests with thirty healthy subjects, it has been found that effective pre-training set could overcome the problem in the main task. Minor modifications on the pre-training set could overcome or aggravate the problem, which indicates the importance of choosing the correct pre-training parameters

Characterization of surface EMG signals using improved approximate entropy

An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal dimension as well as the standard ApEn, the improved ApEn can extract information underlying sEMG signals more efficiently and accurately. The method introduced here can also be applied to other medium-sized and noisy physiological signals