Hand Sign Recognition based on Myographic Methods and Random K-Tournament Grasshopper Extreme Learner

CHF 28.25
Auf Lager
SKU
31OSUH1TA1O
Stock 1 Verfügbar
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

Bewertungen

Schreiben Sie eine Bewertung
Nur registrierte Benutzer können Bewertungen schreiben. Bitte loggen Sie sich ein oder erstellen Sie ein Konto.
Made with ♥ in Switzerland | ©2025 Avento by Gametime AG
Gametime AG | Hohlstrasse 216 | 8004 Zürich | Schweiz | UID: CHE-112.967.470