One of the main problems in developing active prosthesis is how to control them in a natural way. In order to increase the effectiveness of hand prostheses there is a need in better exploiting electromyography (EMG) signals. After an analysis of the movements necessary for grasping, we individuated five movements for the wrist-hand mobility. Then we designed the basic electronics and software for the acquisition and the analysis of the EMG signals. We built a small size electronic device capable of registering them that can be integrated into a hand prosthesis. Among all the numerous muscles that move the fingers, we have chosen the ones in the forearm and positioned only two electrodes. To recognize the operation, we developed a classification system, using a novel integration of Artificial Neural Networks (ANN) and wavelet features.
In this contribution, a new concept for an activity recorder and transceiver (ART) is presented. Among the many purposes ART can be used for, this contribution focuses on the development of personal assistant devices for an aging society. Instead of probing a patient's health only when illness occurs, ART offers the possibility to monitor a person ubiquitous in every day life in order e.g. to detect abnormal changes in behavior, mainly based on the recording of discrete events rather than continuous sensor data streams. ARTs are key elements to develop personal assistant devices that are able to support people especially with cognitive impairments to remain living independent and yet secure their home environments. In this document vital demands on such systems will be defined and systematically analyzed. Furthermore the system architecture and possible applications for ARTs will be presented.
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