ArticleFrontiers in neurorobotics2019
A Piezoresistive Array Armband With Reduced Number of Sensors for Hand Gesture Recognition.
Article in Frontiers in neurorobotics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 66 citations in OpenAlex.
- A Simplified Print-in-Place Redesign of the Open-Source Federica Prosthetic Hand.Bioengineering (Basel, Switzerland) · 2026Article
- Sensor Arrays: A Comprehensive Systematic Review.Sensors (Basel, Switzerland) · 2025Review
- Hand gesture recognition using sEMG signals with a multi-stream time-varying feature enhancement approach.Scientific reports · 2024Article
- A smart approach to EMG envelope extraction and powerful denoising for human-machine interfaces.Scientific reports · 2023Article
- Anatomically Designed Triboelectric Wristbands with Adaptive Accelerated Learning for Human-Machine Interfaces.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Changes in Forcecardiography Heartbeat Morphology Induced by Cardio-Respiratory Interactions.Sensors (Basel, Switzerland) · 2022Article
- Study on Flexible sEMG Acquisition System and Its Application in Muscle Strength Evaluation and Hand Rehabilitation.Micromachines · 2022Article
- Multimodal Finger Pulse Wave Sensing: Comparison of Forcecardiography and Photoplethysmography Sensors.Sensors (Basel, Switzerland) · 2022Article
- Respiratory-Induced Amplitude Modulation of Forcecardiography Signals.Bioengineering (Basel, Switzerland) · 2022Article
- Noncontact Human-Machine Interface Using Complementary Information Fusion Based on MEMS and Triboelectric Sensors.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram.Bioengineering (Basel, Switzerland) · 2022Article
- Detection of Aortic Valve Opening and Estimation of Pre-Ejection Period in Forcecardiography Recordings.Bioengineering (Basel, Switzerland) · 2022Article
- Myoelectric Pattern Recognition Performance Enhancement Using Nonlinear Features.Computational intelligence and neuroscience · 2022Article
- A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces.Sensors (Basel, Switzerland) · 2021Article
- Wearable Bluetooth Triage Healthcare Monitoring System.Sensors (Basel, Switzerland) · 2021Article
- Biosignal-Based Human-Machine Interfaces for Assistance and Rehabilitation: A Survey.Sensors (Basel, Switzerland) · 2021Review
- The "Federica" Hand.Bioengineering (Basel, Switzerland) · 2021Article
- AI enabled sign language recognition and VR space bidirectional communication using triboelectric smart glove.Nature communications · 2021Article
- Respiration Monitoring via Forcecardiography Sensors.Sensors (Basel, Switzerland) · 2021Article
- A Novel Broadband Forcecardiography Sensor for Simultaneous Monitoring of Respiration, Infrasonic Cardiac Vibrations and Heart Sounds.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.