A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition
Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only t...
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| Vydáno v: | Frontiers in neurorobotics Ročník 13; s. 114 |
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| Jazyk: | angličtina |
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Switzerland
Frontiers Research Foundation
17.01.2020
Frontiers Media S.A |
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| ISSN: | 1662-5218, 1662-5218 |
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| Abstract | Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control. |
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| AbstractList | Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control. Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control.Human machine interfaces (HMIs) are employed in a broad range of applications, spanning from assistive devices for disability to remote manipulation and gaming controllers. In this study, a new piezoresistive sensors array armband is proposed for hand gesture recognition. The armband encloses only three sensors targeting specific forearm muscles, with the aim to discriminate eight hand movements. Each sensor is made by a force-sensitive resistor (FSR) with a dedicated mechanical coupler and is designed to sense muscle swelling during contraction. The armband is designed to be easily wearable and adjustable for any user and was tested on 10 volunteers. Hand gestures are classified by means of different machine learning algorithms, and classification performances are assessed applying both, the 10-fold and leave-one-out cross-validations. A linear support vector machine provided 96% mean accuracy across all participants. Ultimately, this classifier was implemented on an Arduino platform and allowed successful control for videogames in real-time. The low power consumption together with the high level of accuracy suggests the potential of this device for exergames commonly employed for neuromotor rehabilitation. The reduced number of sensors makes this HMI also suitable for hand-prosthesis control. |
| Author | Andreozzi, Emilio Esposito, Daniele Fratini, Antonio Gargiulo, Gaetano D. D’Addio, Giovanni Naik, Ganesh R. Bifulco, Paolo |
| AuthorAffiliation | 2 Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri , Pavia , Italy 4 School of Life and Health Sciences, Aston University , Birmingham , United Kingdom 1 Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II , Naples , Italy 5 MARCS Institute for Brain, Behaviour and Development, Western Sydney University , Penrith, NSW , Australia 3 School of Computing, Engineering and Mathematics, Western Sydney University , Penrith, NSW , Australia |
| AuthorAffiliation_xml | – name: 2 Department of Neurorehabilitation, IRCCS Istituti Clinici Scientifici Maugeri , Pavia , Italy – name: 3 School of Computing, Engineering and Mathematics, Western Sydney University , Penrith, NSW , Australia – name: 1 Department of Electrical Engineering and Information Technologies, Polytechnic and Basic Sciences School, University of Naples Federico II , Naples , Italy – name: 4 School of Life and Health Sciences, Aston University , Birmingham , United Kingdom – name: 5 MARCS Institute for Brain, Behaviour and Development, Western Sydney University , Penrith, NSW , Australia |
| Author_xml | – sequence: 1 givenname: Daniele surname: Esposito fullname: Esposito, Daniele – sequence: 2 givenname: Emilio surname: Andreozzi fullname: Andreozzi, Emilio – sequence: 3 givenname: Gaetano D. surname: Gargiulo fullname: Gargiulo, Gaetano D. – sequence: 4 givenname: Antonio surname: Fratini fullname: Fratini, Antonio – sequence: 5 givenname: Giovanni surname: D’Addio fullname: D’Addio, Giovanni – sequence: 6 givenname: Ganesh R. surname: Naik fullname: Naik, Ganesh R. – sequence: 7 givenname: Paolo surname: Bifulco fullname: Bifulco, Paolo |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32009926$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2020 Esposito, Andreozzi, Gargiulo, Fratini, D’Addio, Naik and Bifulco. 2020. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2020 Esposito, Andreozzi, Gargiulo, Fratini, D’Addio, Naik and Bifulco. 2020 Esposito, Andreozzi, Gargiulo, Fratini, D’Addio, Naik and Bifulco |
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| Keywords | exergaming human–machine interface piezoresistive sensor support vector machine muscle sensors array hand gesture recognition |
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| References | Frank (B21) 2016 Ordnung (B36) 2017; 11 Bifulco (B6) 2017 Nymoen (B35) 2015 (B40) 2019 Polisiero (B39) 2013; 6 Elahi (B17) 2018; 11 Jung (B30) 2015; 11 Parajuli (B37) 2019; 19 Drake (B15) 2014 Bisi (B7) 2018; 7 Jiang (B29) 2017; 41 Boy (B9) 2017 D’Ausilio (B14) 2012; 44 Esposito (B20) Shukla (B44) 2018; 12 McIntosh (B32) 2016 Arapi (B2) 2018; 12 Polfreman (B38) 2018 Cho (B12) 2016; 4 (B28) 2019 Sathiyanarayanan (B43) 2016 Booth (B8) 2018; 38 Esposito (B18) 2018; 18 Cho (B13) 2017; 15 McKirahan (B33) 2016 Radmand (B42) 2016; 53 Bifulco (B5) 2011 Beckerle (B4) 2018; 12 Witten (B46) 2016 (B41) 2019 Ma (B31) 2008 Hong (B26) 2018; 12 Chakraborty (B11) 2017; 12 Huang (B27) 2017; 22 Abraham (B1) 2018; 18 Du (B16) 2017; 17 Gargiulo (B22) 2010 Zhu (B47) 2018 Geng (B23) 2016; 6 Sreenivasan (B45) 2018; 2018 Ghafoor (B24) 2017; 11 Giovanelli (B25) 2016; 2016 (B34) 2019 Esposito (B19) Caramiaux (B10) 2015; 21 (B3) 2019 |
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| SubjectTerms | Accuracy Contraction Electromyography exergaming Forearm hand gesture recognition human–machine interface Interfaces Learning algorithms Muscle contraction muscle sensors array Muscles Neurorobotics Noise piezoresistive sensor Rehabilitation Sensors support vector machine Wrist |
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| Title | A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition |
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