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Hand Sign Recognition based on Myographic Methods and Random K-Tournament Grasshopper Extreme Learner
CHF 28.25
Auf Lager
SKU
31OSUH1TA1O
Geliefert zwischen Mi., 24.12.2025 und Do., 25.12.2025
Details
Surface electromyography (sEMG), force myography (FMG) and surface electrical impedance myography (sEIM) are investigated for perspective wearable embedded systems. A database has been collected from more than 100 healthy subject performing American sign language (ASL). Classification methods have been proposed based on Extreme Learning Machine (ELM) supported by a grasshopper optimization algorithm (GOA) as a core weight pruning process. To ensure the GOA population diversity a K-tournament selection strategy is included. The K-Tournament Grasshopper Optimization Algorithm (KTGOA) has been improved for discrete optimization problems and implemented to select the ELM weights as a K-Tournament Grasshopper Extreme Learner (KTGEL). To improve the balance of exploration and exploitation, the balancing coefficients of the KTGEL are subjected to uniform randomization. The resulting Random K-Tournament Grasshopper Extreme Learner (RKTGEL) is a novel classifier with a simultaneously automated feature selection. The number of sensors and their positions have been investigated: For FMG, 8 sensors, for sEMG, 2 sensors and for sEIM, 4 equidistant electrodes for measurements in the frequencies from 1 kHz to 4 kHz, are suitable. Combinations of myographic methods reach an accuracy of 100% for small and medium ambiguous datasets. For high ambiguity, a targeted reduction of ambiguity by excluding signs with a high similarity results the RKTGEL to reach an overall accuracy of 97%.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783961001477
- Anzahl Seiten 242
- Lesemotiv Verstehen
- Genre Earth Science
- Herausgeber Universitätsverlag Chemnitz
- Gewicht 339g
- Größe H210mm x B148mm x T14mm
- EAN 9783961001477
- Titel Hand Sign Recognition based on Myographic Methods and Random K-Tournament Grasshopper Extreme Learner
- Autor Rim Barioul
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