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This study generally is purposed to implementation and usability evaluation of augmentative and alternative communication system (hereinafter referred to as an “AAC system”). Also the device is aimed as an AAC system using symbols for HCI, allowing a handicapped person to make a general communication with others in a free and convenient manner. This study specifically presents a method of predicting predictions, which contributes to reducing the size of the AAC system. This method includes selecting vocabulary and classifying it by domains so as to meet the characteristics of the AAC communication, using a noun thesaurus for semantic analysis, and building a sub-category dictionary. Accordingly, this study has verified the system by applying it to linguistically handicapped persons with different kinds of handicaps two times.

To be functional in a practical sense for real-time control of assistive devices, a myoprocessor must successfully integrate both detection and estimation systems. This paper describes an approach for classifying electromyographic (EMG) signals using a multilayer perceptrons (MLPs) and hidden Markov models (HMMs) hybrid classifier and force estimation. Instead of using MLPs as probability generators for HMMs the authors propose to use MLPs as the second classifiers to increase discrimination rates of myoelectric patterns. This strategy is proposed to overcome weak discrimination and to consider dynamic properties of EMG signals. Two discrimination strategies (HMM, and HMM with three subnet MLPs) for discriminating signals representative of 6 primitive class of motions are described and compared. The proposed strategy increase the discrimination results considerably. Results are presented to support this approach.

Remote monitoring for medical diagnosis is widespread from personal computer to wireless communication nowadays. In this paper we propose an ECG (electrocardiogram) monitoring system which is based on the nonpersonal computer of a subject. Using the network device of small size, the detected biomedical signals are transmitted from a subject to a medical doctor. The designed system are consisted of three parts which are biomedical signals acquisition part, network device having only one IP address and the intermediate AVR-microcontroller unit, which is placed between two parts. In programming, some observer can monitor the cardiogram of a subject, if they know the IP address of a subject, which is displayed by the applet of the Java program. Consequently, with this small size and freedom from the restriction of the place can enable a subject's biomedical signals to be monitored easily from the remote area through the Ethernet.