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AuthorQidwai, Uvais
AuthorZahid, Aejaz
Available date2024-05-07T05:39:58Z
Publication Date2014
Publication NameIECBES 2014, Conference Proceedings - 2014 IEEE Conference on Biomedical Engineering and Sciences: "Miri, Where Engineering in Medicine and Biology and Humanity Meet"
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/IECBES.2014.7047500
URIhttp://hdl.handle.net/10576/54685
AbstractSpinal Muscular Atrophy (SMA) is a progressive neuromuscular disorder. Usually, this condition is considered genetically induced with no known cure to date. Children are born with the condition and develop muscular weakness progressively as they grow. The weakness ultimately encompasses the whole muscular function rendering the limbs dysfunctional or paralyzed. Many children with SMA, if they do not have the weakness from the beginning, will start having the disease manifesting itself on the legs first and then the arms and, in due time, they will become quadriplegic and even more disabilities can follow including speech impairment. Assistive Technology support for people with such disabilities often requires identification of the best residual muscular function so that this can be utilized as a means of voluntary control. Electromyography (EMG) is a popular clinical procedure to monitor muscular function in a large number of healthcare and other clinical measurements. It translates muscular activity into proportional voltage signals which can then be used for analysis and other applications. Most of the existing assistive applications are based on amplitude thresholding of the EMG signals, which can drift over time due to fatigue on part of the patient and partly due to changes at the electrode interface over the period of use. This requires that a care-giver must re-calibrate the signal threshold making the process both impractical and prone to errors. In this paper, a new approach has been presented that alleviates the need for re-calibration of thresholds for such applications development. Fuzzy classifier has been used on pattern-related features from the signal samples and based on that appropriate computer signals can be generated to be adapted in an Assistive application such as playing a computer game, using serial keyboard interface, controlling the wheelchair/other-hardware, or even being able to generate text.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectElectromyography
Fuzzy sets
Neuromuscular rehabilitation
Amplitude thresholding
Applications development
Assistive applications
Assistive technology
Clinical measurements
Electrode interface
Neuromuscular disorders
Spinal muscular atrophy
Computer games
TitleFuzzy-EMG-based Assistive interface for children with Spinal-Muscular-Atrophy
TypeConference Paper
Pagination27-31
dc.accessType Abstract Only


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