Abstract-This paper proposes and evaluates the application of support vector machine (SVM) to classify upper limb motions using myoelectric signals. It explores the optimum configuration of SVMbased myoelectric control, by suggesting an advantageous data segmentation technique, feature set, model selection approach for SVM, and postprocessing methods. This work presents a method to adjust SVM parameters before classification, and examines overlapped segmentation and majority voting as two techniques to improve controller performance. A SVM, as the core of classification in myoelectric control, is compared with two commonly used classifiers: linear discriminant analysis (LDA) and multilayer perceptron (MLP) neural networks. It demonstrates exceptional accuracy, robust performance, and low computational load. The entropy of the output of the classifier is also examined as an online index to evaluate the correctness of classification; this can be used by online training for long-term myoelectric control operations.

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This paper evaluates supervised and unsupervised adaptive schemes applied to online support vector machine (SVM) that classifies BCI data. Online SVM processes fresh samples as they come and update existing support vectors without referring to pervious samples. It is shown that the performance of online SVM is similar to that of the standard SVM, and both supervised and unsupervised schemes improve the classification hit rate.

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This paper investigates manifestation of fatigue in myoelectric signals during dynamic contractions produced whilst playing PC games. The hand's myoelectric signals were collected in 26 independent sessions with 10 subjects. Two methods, spectral analysis and time-scale analysis, were applied to compute signal frequency and least-square linear regression was used to model the trend of frequency shift. Non-parametric statistical methods were employed to analyze experimental results, which indicates significant decline in signal frequency as a manifestation of fatigue in long-term muscle activities.

Abstract-The demographic trend is towards ageing in our society and the number of people with physical impairments and disabilities will increase dramatically in the future. It is necessary to deliver advanced healthcare and services to these people so that they can live independently and stay well at home throughout their lifespan. This paper presents a multi-robot architecture for ambient assisted living of the elderly and disabled, which is based on the robot operating system (ROS). A communication bridge is proposed for different means of human robot interaction, and ROS provides a framework for rapid system development with a reduced cost. Some experimental results are given in the paper to demonstrate the feasibility and performance of the proposed system.