An EMG Keyboard for Forearm Amputees

A high-efficiency, easy-to-use input device is not only important for data entry but also for human-computer interaction. To date, there has been little research on input devices with many degrees of freedom (DOF) that can be used by the handicapped. This paper presents the development of an electromyography (EMG)-based input device for forearm amputees. To overcome the difficulties in analysing EMG and realising high DOF from biosignals, the following were integrated: (1) an online learning method to cope with nonlinearity and the individual difference of EMG signals; (2) a smoothing algorithm to deal with noisy recognition results and transition states; and (3) a modified Huffman coding algorithm to generate the optimal code, taking expected error and input efficiency into consideration. Experiments showed the validity of the system and the possibility for development of a quiet, free-posture (no postural restriction) input device with many DOF for users, including forearm amputees

Lamarckian GA with Genetic Supervision

The evolutionary theory advocated by Lamarck [3], focuses on the inheritance of characteristics acquired for self-adaptation to environment. In the domain of the purpose of acquiring adaptive strategies, it is important to make use of the information of experiences through adaptation. Therefore, the Lamarckian mechanism is an effective approach and is expected to augment the power of many kinds of evolving or learning algorithms. In this paper, we propose the Lamarckian Lookup-Table type Genetic Algorithm (LLT-GA). In general, the effectiveness of the characteristics useful for adaptation depends on a class or rather a landscape of problems to be applied. In order to demolish this barrier, the proposed LLT-GA is armed with a control mechanism for acquired characteristics based on a concept of Genetic Supervision. In this paper we discuss first Lamarckian effect and demonstrate that it is dependent on a landscape of a problem. Then, we develop an adaptive evaluation module for Lamarckia..

Artificial Ecological System For Evolving Computational Procedures

: This paper explores a model where computational procedures themselves are evolved for the development of the next generation of emergent computing. The proposed model is an artificial ecosystem consisting of Turing machines which are a mathematical model of computing or algorithm. These Turing machines interact with each other by reading the other machines' descriptions as an input tape. As the simulation proceeds, a series of effective computational procedures emerges from an initial set. This evolutionary process does not require any mutation or static fitness function. This paper demonstrates the self-organizational evolution of these computational procedures through computer simulations. INTRODUCTION In recent years, new methodologies of emergent computing paradigms have been studied in order to enhance our understanding of complex systems and to further the development of emergent design theory for artifacts. In fact, evolutionary computations (Goldberg, 1989 and Koza, 1992) ..