An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves
The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/5/1766 |
| _version_ | 1797414683510046720 |
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| author | Taha Müezzinoğlu Mehmet Karaköse |
| author_facet | Taha Müezzinoğlu Mehmet Karaköse |
| author_sort | Taha Müezzinoğlu |
| collection | DOAJ |
| description | The interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy. |
| first_indexed | 2024-03-09T05:37:07Z |
| format | Article |
| id | doaj.art-c1e5f5cf33024f2dad92caa827978494 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:37:07Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c1e5f5cf33024f2dad92caa8279784942023-12-03T12:28:10ZengMDPI AGSensors1424-82202021-03-01215176610.3390/s21051766An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable GlovesTaha Müezzinoğlu0Mehmet Karaköse1Department of Computer Engineering, Firat University, 23200 Elazig, TurkeyDepartment of Computer Engineering, Firat University, 23200 Elazig, TurkeyThe interactions between humans and unmanned aerial vehicles (UAVs), whose applications are increasing in the civilian field rather than for military purposes, are a popular future research area. Human–UAV interactions are a challenging problem because UAVs move in a three-dimensional space. In this paper, we present an intelligent human–UAV interaction approach in real time based on machine learning using wearable gloves. The proposed approach offers scientific contributions such as a multi-mode command structure, machine-learning-based recognition, task scheduling algorithms, real-time usage, robust and effective use, and high accuracy rates. For this purpose, two wearable smart gloves working in real time were designed. The signal data obtained from the gloves were processed with machine-learning-based methods and classified multi-mode commands were included in the human–UAV interaction process via the interface according to the task scheduling algorithm to facilitate sequential and fast operation. The performance of the proposed approach was verified on a data set created using 25 different hand gestures from 20 different people. In a test using the proposed approach on 49,000 datapoints, process time performance of a few milliseconds was achieved with approximately 98 percent accuracy.https://www.mdpi.com/1424-8220/21/5/1766human–UAV interactionwearable technologiesInternet of Things (IoT)human–computer interactionsmart systems |
| spellingShingle | Taha Müezzinoğlu Mehmet Karaköse An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves Sensors human–UAV interaction wearable technologies Internet of Things (IoT) human–computer interaction smart systems |
| title | An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves |
| title_full | An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves |
| title_fullStr | An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves |
| title_full_unstemmed | An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves |
| title_short | An Intelligent Human–Unmanned Aerial Vehicle Interaction Approach in Real Time Based on Machine Learning Using Wearable Gloves |
| title_sort | intelligent human unmanned aerial vehicle interaction approach in real time based on machine learning using wearable gloves |
| topic | human–UAV interaction wearable technologies Internet of Things (IoT) human–computer interaction smart systems |
| url | https://www.mdpi.com/1424-8220/21/5/1766 |
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