Microcontroller Unit-Based Gesture Recognition System
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| Publicado en: | Machines vol. 13, no. 2 (2025), p. 90 |
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| Autor principal: | |
| Otros Autores: | |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 003 | UK-CbPIL | ||
| 022 | |a 2075-1702 | ||
| 024 | 7 | |a 10.3390/machines13020090 |2 doi | |
| 035 | |a 3171134538 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231531 |2 nlm | ||
| 100 | 1 | |a Grabarczyk, Jakub |u Faculty of Electrical Engineering, Gdynia Maritime University, 81-225 Gdynia, Poland | |
| 245 | 1 | |a Microcontroller Unit-Based Gesture Recognition System | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a This article describes the design, construction, and programming of a microcontroller-based system, which uses hand gestures with machine learning algorithms to control an unmanned aerial vehicle (UAV). A neural network is used as a model, and an IMU sensor detects the gestures. The developed gesture recognition system, besides the IMU sensor, is composed of a Raspberry Pi Pico and radio communication module. The benefits and drawbacks of deploying machine learning models on microcontrollers, as opposed to units superior in terms of clocking are also discussed. | |
| 610 | 4 | |a Raspberry Pi Ltd | |
| 653 | |a Radio communications | ||
| 653 | |a Microcontrollers | ||
| 653 | |a Microelectromechanical systems | ||
| 653 | |a Machine learning | ||
| 653 | |a Embedded systems | ||
| 653 | |a Neural networks | ||
| 653 | |a Computer vision | ||
| 653 | |a Electromagnetism | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a People with disabilities | ||
| 653 | |a Magnetic fields | ||
| 653 | |a Sensors | ||
| 653 | |a Gesture recognition | ||
| 653 | |a Algorithms | ||
| 653 | |a Accelerometers | ||
| 653 | |a Vehicles | ||
| 700 | 1 | |a Lazarowska, Agnieszka |u Department of Autonomous Systems, Faculty of Computer Science, Gdynia Maritime University, 81-225 Gdynia, Poland | |
| 773 | 0 | |t Machines |g vol. 13, no. 2 (2025), p. 90 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3171134538/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3171134538/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3171134538/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |