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- W4293567548 abstract "The human hand is one of the most sophisticated machines ever seen by mankind which in itself is a result of the evolution of millions of years. It has enabled humans to perform tasks unimaginable by any other creature on the planet. It has given us the freedom to manipulate the world. The loss of a human hand due to any unfortunate series of events is fatal; breaking the connection between the amputee and the surrounding environment. Recent innovations in bionic hands controls have enabled us to bridge the gap of inequalities. Most widely used control scheme of bionic hands involve identification of patterns in muscle signals captured through electromyography (EMG). Electrodes are placed over target muscles of an amputee and recorded data is classified using machine learning. A number of signal features are computed for training of classifier and accurate identification of user intent. These control schemes have enabled high functionality in bionic prosthetic hands leading us to closely imitate functional capabilities of a natural human hand. The accuracy and robustness of these systems has increased to greater extents in past decades thus making the devices reliable to control and perform activities of daily living with effectiveness. This provides a user with restored capabilities and a sense of empowerment. In this chapter, authors have outlined the implementation of pattern recognition in control of bionic hands and various concepts as well as steps involved in the process." @default.
- W4293567548 created "2022-08-30" @default.
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- W4293567548 date "2022-08-30" @default.
- W4293567548 modified "2023-10-17" @default.
- W4293567548 title "Pattern Recognition in EMG Control of Bionic Hands" @default.
- W4293567548 doi "https://doi.org/10.1201/9781003245469-11" @default.
- W4293567548 hasPublicationYear "2022" @default.
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