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Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning

التفاصيل البيبلوغرافية
العنوان: Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
المؤلفون: Julian Webber, Abolfazl Mehbodniya, Rui Teng, Ahmed Arafa, Ahmed Alwakeel
المصدر: Applied Sciences, Vol 11, Iss 24, p 11582 (2021)
بيانات النشر: MDPI AG, 2021.
سنة النشر: 2021
المجموعة: LCC:Technology
LCC:Engineering (General). Civil engineering (General)
LCC:Biology (General)
LCC:Physics
LCC:Chemistry
مصطلحات موضوعية: visible light communications (VLC), gesture recognition (GR), human-computer interaction (HCI), human activity recognition (HAR), machine learning (ML), neural network, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
الوصف: Gesture recognition (GR) has many applications for human-computer interaction (HCI) in the healthcare, home, and business arenas. However, the common techniques to realize gesture recognition using video processing are computationally intensive and expensive. In this work, we propose to task existing visible light communications (VLC) systems with gesture recognition. Different finger movements are identified by training on the light transitions between fingers using the long short-term memory (LSTM) neural network. This paper describes the design and implementation of the gesture recognition technique for a practical VLC system operating over a distance of 48 cm. The platform uses a single low-cost light-emitting diode (LED) and photo-diode sensor at the receiver side. The system recognizes gestures from interruptions in the direct light transmission, and is therefore suitable for high-speed communication. Gesture recognition accuracies were conducted for five gestures, and results demonstrate that the proposed system is able to accurately identify the gestures in up to 88% of cases.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2076-3417
Relation: https://www.mdpi.com/2076-3417/11/24/11582; https://doaj.org/toc/2076-3417
DOI: 10.3390/app112411582
URL الوصول: https://doaj.org/article/bf0c9923886447a89c67a626a40acbd5
رقم الأكسشن: edsdoj.bf0c9923886447a89c67a626a40acbd5
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20763417
DOI:10.3390/app112411582