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dc.rights.licenseAll rights reserved
dc.contributor.advisorJordán, Zayira
dc.contributor.authorRamos González, Héctor A.
dc.contributor.authorVega, Anthony
dc.date.accessioned2020-06-29T14:03:20Z
dc.date.available2020-06-29T14:03:20Z
dc.date.issued2018
dc.identifier.citationRamos González, H. A. & Vega, A. (2018). American Sign Language recognition using electromyographic signals via Myo Armband [Research Poster]. Undergraduate Research Program For Honor Students HSI STEM Grant, Polytechnic University of Puerto Rico.
dc.identifier.urihttp://hdl.handle.net/20.500.12475/228
dc.descriptionFinal Research Poster for the Undergraduate Research Program for Honor Students HSI STEM Grant.en_US
dc.description.abstractHuman and computer interaction has become an important tool in the 21 st century One of the most promising technologies for human/computer interaction is speech recognition ( which is a system composed of microphones, soundcards and speech engine software in which isolate words, spoken by an emitter is recognized through calculation of a minimum prediction residual (Johnson et al 2014 Nowadays there has been many studies around this form of technology but the hearing impaired population cannot benefit from these advancements This study proposes to address this problem by applying the Myo Armband, a wearable commercial band that captures Electromyographic signals ( and tests its potential to correctly classify American Sign Language ( gestures by applying a brain computer interface methodology in which consist of three parts data acquisition, signal processing and classification This methodology was applied to three types of hand gestures with the objective of extracting a mean power value for each sensor and compare it to experimental signals to determine if a correct classification was obtained In the end, the brain computer interface methodology was accomplished successfully, as it was possible to measure the power of every sensor After finalizing the research, it is possible to say that the approach taken to satisfy the primary goal, which was recognizing the three gestures, was not accurate, as it did not reach a satisfying accuracy percentage There can be many factors of why this happened One can be the fact that the algorithm created and the approach of measuring the power of every sensor of the Myo Armband was not the indicated or the best to recognize EMG signals or that the sample experimental size was very small One potential candidate to enhance the obtained results is to increase the number of experimental samples or to develop a new approach to process the obtained signals and enhance the classificationen_US
dc.description.sponsorshipThis research project was supported by the Title V STEM Grant “Bridges to STEM Success” P 0031 C 160141en_US
dc.language.isoen_USen_US
dc.publisherPolytechnic University of Puerto Ricoen_US
dc.relation.ispartofComputer Engineering Program
dc.relation.ispartofseriesUndergraduate Research Program For Honor Students HSI STEM Grant 2017-2018
dc.relation.haspartSan Juan Campus
dc.subject.lcshAmerican Sign Language
dc.subject.lcshHuman-computer interaction
dc.subject.lcshSignal processing
dc.subject.lcshBrain-computer interfaces
dc.subject.lcshPolytechnic University of Puerto Rico--Undergraduates--Posters
dc.subject.lcshPolytechnic University of Puerto Rico--Electrical & Computer Engineering and Computer Science Department--Undergraduates--Research
dc.titleAmerican Sign Language Recognition using Electromyographic Signals via Myo Armbanden_US
dc.typePosteren_US
dc.rights.holderPolytechnic University of Puerto Rico, Undergraduate Research Program for Honor and Outstanding Students HSI STEM Grant


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