Szczegóły publikacji
Opis bibliograficzny
Hand body language gesture recognition based on signals from specialized glove and machine learning algorithms / Paweł PŁAWIAK, Tomasz Sośnicki, Michał Niedźwiecki, Zbisław Tabor, Krzysztof Rzecki // IEEE Transactions on Industrial Informatics ; ISSN 1551-3203 . — 2016 — vol. 12 no. 3, s. 1104–1113. — Bibliogr. s. 1112, Abstr. — Publikacja dostępna online od: 2016-04-05. — P. Pławiak - dod. afiliacja: Cracow University of Technology. — T. Sośnicki - afiliacja: Cracow University of Technology
Autorzy (5)
- AGHPławiak Paweł
- Sośnicki Tomasz
- Niedźwiecki Michał
- Tabor Zbisław
- Rzecki Krzysztof
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 98671 |
|---|---|
| Data dodania do BaDAP | 2016-08-04 |
| Tekst źródłowy | URL |
| DOI | 10.1109/TII.2016.2550528 |
| Rok publikacji | 2016 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | IEEE Transactions on Industrial Informatics |
Abstract
The man–machine interface (MMI) is one of the most exciting areas of contemporary research. To make the MMI as convenient for a human as possible, it is desirable that efficient algorithms for recognizing body language are developed. This paper presents a system for quick and effective recognition of gestures of hand body language, based on data from a specialized glove equipped with ten sensors. In the experiment, 10 people performed 22 hand body language gestures. Each of the 22 gestures was executed 10 times. Collected data were preprocessed in multiple ways and three machine learning algorithms were designed based on classifiers (probabilistic neural network, support vector machine, and k-nearest neighbors algorithm) trained and tested by a tenfold cross-validation technique. The best designed classifiers gained effectiveness of gesture recognition at \kappa = 98.24% with a very short time of testing, below 1 ms. The experiments confirm that efficient and quick recognition of hand body language is possible.
