Literature DB >> 30441555

Wireless Smartphone Control using Electromyography and Automated Gesture Recognition.

Jacob Dawes, Makenzie Brian, Hayden Bialek, Matthew L Johnston.   

Abstract

In this paper, a wearable, wireless system is demonstrated using electromyography (EMG) signals for realtime control of a smartphone device. The system allows gesturebased control of a smartphone or tablet computer without physical contact, direct line of sight, or significant movement. Additionally, automated gesture detection is shifted to the smartphone, eliminating the need for robust computing hardware. The electronic system and gesture prediction algorithm are described, and measured results are presented and for multiple users. The system demonstrates a maximum true positive detection rate of 92% for a trained user, using three distinct hand gestures. The EMG-based detection system serves as a proof-of-concept for providing wireless, gesture-based control of computer interfaces using low-cost consumer hardware.

Mesh:

Year:  2018        PMID: 30441555     DOI: 10.1109/EMBC.2018.8513640

Source DB:  PubMed          Journal:  Annu Int Conf IEEE Eng Med Biol Soc        ISSN: 2375-7477


  1 in total

1.  Multi-Channel Biopotential Acquisition System Using Frequency-Division Multiplexing With Cable Motion Artifact Suppression.

Authors:  Jinyong Kim; Hyunkyu Ouh; Matthew L Johnston
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2022-02-17       Impact factor: 3.833

  1 in total

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