A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier
Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to contr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/11/23/3915 |
| _version_ | 1797463403458985984 |
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| author | Vassilis Alimisis Vassilis Mouzakis Georgios Gennis Errikos Tsouvalas Christos Dimas Paul P. Sotiriadis |
| author_facet | Vassilis Alimisis Vassilis Mouzakis Georgios Gennis Errikos Tsouvalas Christos Dimas Paul P. Sotiriadis |
| author_sort | Vassilis Alimisis |
| collection | DOAJ |
| description | Electromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling prosthetics in a more natural way. To this end, a novel analog integrated voting classifier is proposed as a hand gesture recognition system. The voting classifiers utilize 3 separate centroid-based classifiers, each one attached to a different electromyographic electrode and a voting circuit. The main building blocks of the architecture are bump and winner-take-all circuits. To confirm the proper operation of the proposed classifier, its post-layout classification results (91.2% accuracy) are compared to a software-based implementation (93.8% accuracy) of the same voting classifier. A TSMC 90 nm CMOS process in the Cadence IC Suite was used to design and simulate the following circuits and architectures. |
| first_indexed | 2024-03-09T17:50:11Z |
| format | Article |
| id | doaj.art-39ced436214f4546a710cd3ad9ca025d |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T17:50:11Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-39ced436214f4546a710cd3ad9ca025d2023-11-24T10:47:38ZengMDPI AGElectronics2079-92922022-11-011123391510.3390/electronics11233915A Hand Gesture Recognition Circuit Utilizing an Analog Voting ClassifierVassilis Alimisis0Vassilis Mouzakis1Georgios Gennis2Errikos Tsouvalas3Christos Dimas4Paul P. Sotiriadis5Department of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, GreeceDepartment of Electrical and Computer Engineering, National Technical University of Athens, 15772 Athens, GreeceElectromyography is a diagnostic medical procedure used to assess the state of a muscle and its related nerves. Electromyography signals are monitored to detect neuromuscular abnormalities and diseases but can also prove useful in decoding movement-related signals. This information is vital to controlling prosthetics in a more natural way. To this end, a novel analog integrated voting classifier is proposed as a hand gesture recognition system. The voting classifiers utilize 3 separate centroid-based classifiers, each one attached to a different electromyographic electrode and a voting circuit. The main building blocks of the architecture are bump and winner-take-all circuits. To confirm the proper operation of the proposed classifier, its post-layout classification results (91.2% accuracy) are compared to a software-based implementation (93.8% accuracy) of the same voting classifier. A TSMC 90 nm CMOS process in the Cadence IC Suite was used to design and simulate the following circuits and architectures.https://www.mdpi.com/2079-9292/11/23/3915analog VLSI implementationcentroid-based classifierhand gesture recognitionlow-power designvoting classifier |
| spellingShingle | Vassilis Alimisis Vassilis Mouzakis Georgios Gennis Errikos Tsouvalas Christos Dimas Paul P. Sotiriadis A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier Electronics analog VLSI implementation centroid-based classifier hand gesture recognition low-power design voting classifier |
| title | A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier |
| title_full | A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier |
| title_fullStr | A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier |
| title_full_unstemmed | A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier |
| title_short | A Hand Gesture Recognition Circuit Utilizing an Analog Voting Classifier |
| title_sort | hand gesture recognition circuit utilizing an analog voting classifier |
| topic | analog VLSI implementation centroid-based classifier hand gesture recognition low-power design voting classifier |
| url | https://www.mdpi.com/2079-9292/11/23/3915 |
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