Abstract
In the tragic situation when a person loses his or her hand, they are usually faced with only one option if they wish to regain a good level of mobility; learn to control an artificial hand. It has been suggested that our brain stores a "body map" of the different parts in our body. Thus, if a person loses a hand, their "body map" remains intact and produces phantom sensations that permit the person to feel like they still have their hand. Some discomfort is felt during these sensations; nevertheless, there is a positive side to them as they enable patients to control prosthetic replacements. Sensations experienced can be measured using a method known as Electromyography (EMG) and can be acquired and processed to control an artificial hand. This research involved the acquisition, analysis and classification of EMG signals through construction of a recording device and the development of classification models based on heuristic approaches and Artificial Intelligence classifiers based on Neural Networks to control artificial hands.
| Original language | English |
|---|---|
| Title of host publication | Personalised Health Management Systems: The Integration of Innovative Sensing, Textile, Information and Communication Technologies |
| Publisher | IOS Press |
| Pages | 229-234 |
| Volume | 117 |
| ISBN (Print) | 978-1-58603-565-5 |
| Publication status | Published (in print/issue) - 1 Mar 2005 |
Keywords
- Electromyography
- Artificial Hand
- Signal Processing
- Neural Networks
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