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Key-press gestures recognition and interaction based on SEMG signals

Published: 08 November 2010 Publication History

Abstract

This article conducted research on the pattern recognition of keypress finger gestures based on surface electromyographic (SEMG) signals and the feasibility of key -press gestures for interaction application. Two sort of recognition experiments were designed firstly to explore the feasibility and repeatability of the SEMG -based classification of 1 6 key-press finger gestures relating to right hand and 4 control gestures, and the key -press gestures were defined referring to the standard PC key board. Based on the experimental results, 10 quite well recognized key -press gestures were selected as numeric input keys of a simulated phone, and the 4 control gestures were mapped to 4 control keys. Then two types of use tests, namely volume setting and SMS sending were conducted to survey the gesture-base interaction performance and user's attitude to this technique, and the test results showed that users could accept this novel input strategy with fresh experience.

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  • (2022)Channel-distribution Hybrid Deep Learning for sEMG-based Gesture Recognition2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO55434.2022.10011951(278-284)Online publication date: 5-Dec-2022
  • (2022)Research on sEMG-Based Gesture Recognition by Dual-View Deep LearningIEEE Access10.1109/ACCESS.2022.315866710(32928-32937)Online publication date: 2022
  • (2021)RNN With Stacked Architecture for sEMG based Sequence-to-Sequence Hand Gesture Recognition2020 28th European Signal Processing Conference (EUSIPCO)10.23919/Eusipco47968.2020.9287828(1600-1604)Online publication date: 24-Jan-2021
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      cover image ACM Conferences
      ICMI-MLMI '10: International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
      November 2010
      311 pages
      ISBN:9781450304146
      DOI:10.1145/1891903
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      Published: 08 November 2010

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      Author Tags

      1. electromyographic
      2. human computer interaction
      3. key-press finger gesture
      4. virtual keyboard

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      ICMI-MLMI '10 Paper Acceptance Rate 41 of 100 submissions, 41%;
      Overall Acceptance Rate 453 of 1,080 submissions, 42%

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      View all
      • (2022)Channel-distribution Hybrid Deep Learning for sEMG-based Gesture Recognition2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)10.1109/ROBIO55434.2022.10011951(278-284)Online publication date: 5-Dec-2022
      • (2022)Research on sEMG-Based Gesture Recognition by Dual-View Deep LearningIEEE Access10.1109/ACCESS.2022.315866710(32928-32937)Online publication date: 2022
      • (2021)RNN With Stacked Architecture for sEMG based Sequence-to-Sequence Hand Gesture Recognition2020 28th European Signal Processing Conference (EUSIPCO)10.23919/Eusipco47968.2020.9287828(1600-1604)Online publication date: 24-Jan-2021
      • (2021)sEMG-Based Hand Movement Regression by Prediction of Joint Angles With Recurrent Neural Networks2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC46164.2021.9630042(6519-6523)Online publication date: 1-Nov-2021
      • (2020)Regression of Hand Movements from sEMG Data with Recurrent Neural Networks2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)10.1109/EMBC44109.2020.9176278(3783-3787)Online publication date: Jul-2020
      • (2019)Inhomogeneously Stacked RNN for Recognizing Hand Gestures from Magnetometer Data2019 27th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO.2019.8903132(1-5)Online publication date: Sep-2019
      • (2019)Forked Recurrent Neural Network for Hand Gesture Classification Using Inertial Measurement DataICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP.2019.8682986(2877-2881)Online publication date: May-2019
      • (2019)A Recurrent Neural Network for Hand Gesture Recognition based on Accelerometer Data2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)10.1109/EMBC.2019.8856844(5088-5091)Online publication date: Jul-2019
      • (2017)Daily behavior identification based on sEMG10.1063/1.4992824(020007)Online publication date: 2017
      • (2015)A Novel Phonology- and Radical-Coded Chinese Sign Language Recognition Framework Using Accelerometer and Surface Electromyography SensorsSensors10.3390/s15092330315:9(23303-23324)Online publication date: 15-Sep-2015

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