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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Veröffentlicht in:Frontiers in neurorobotics Jg. 13; S. 114
Hauptverfasser: Esposito, Daniele, Andreozzi, Emilio, Gargiulo, Gaetano D., Fratini, Antonio, D’Addio, Giovanni, Naik, Ganesh R., Bifulco, Paolo
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland Frontiers Research Foundation 17.01.2020
Frontiers Media S.A
Schlagworte:
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
exergaming
human–machine interface
piezoresistive sensor
support vector machine
muscle sensors array
hand gesture recognition
ISSN:1662-5218, 1662-5218
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Zusammenfassung: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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Reviewed by: Hassan Elahi, Sapienza University of Rome, Italy; Amad Zafar, University of Wah, Pakistan
Edited by: Keum-Shik Hong, Pusan National University, South Korea
ISSN:1662-5218
1662-5218
DOI:10.3389/fnbot.2019.00114