Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition
Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user’s body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have vari...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Published: |
2023
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| Online Access: | http://hdl.handle.net/20.500.11937/92441 |
| _version_ | 1848765640960114688 |
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| author | Khaksar, Siavash Checker, Luke Borazjani, Bita Murray, Iain |
| author_facet | Khaksar, Siavash Checker, Luke Borazjani, Bita Murray, Iain |
| author_sort | Khaksar, Siavash |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user’s body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment. |
| first_indexed | 2025-11-14T11:38:28Z |
| format | Journal Article |
| id | curtin-20.500.11937-92441 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:38:28Z |
| publishDate | 2023 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-924412023-06-29T07:04:12Z Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition Khaksar, Siavash Checker, Luke Borazjani, Bita Murray, Iain Gesture recognition is a mechanism by which a system recognizes an expressive and purposeful action made by a user’s body. Hand-gesture recognition (HGR) is a staple piece of gesture-recognition literature and has been keenly researched over the past 40 years. Over this time, HGR solutions have varied in medium, method, and application. Modern developments in the areas of machine perception have seen the rise of single-camera, skeletal model, hand-gesture identification algorithms, such as media pipe hands (MPH). This paper evaluates the applicability of these modern HGR algorithms within the context of alternative control. Specifically, this is achieved through the development of an HGR-based alternative-control system capable of controlling of a quad-rotor drone. The technical importance of this paper stems from the results produced during the novel and clinically sound evaluation of MPH, alongside the investigatory framework used to develop the final HGR algorithm. The evaluation of MPH highlighted the Z-axis instability of its modelling system which reduced the landmark accuracy of its output from 86.7% to 41.5%. The selection of an appropriate classifier complimented the computationally lightweight nature of MPH whilst compensating for its instability, achieving a classification accuracy of 96.25% for eight single-hand static gestures. The success of the developed HGR algorithm ensured that the proposed alternative-control system could facilitate intuitive, computationally inexpensive, and repeatable drone control without requiring specialised equipment. 2023 Journal Article http://hdl.handle.net/20.500.11937/92441 10.3390/s23125462 http://creativecommons.org/licenses/by/4.0/ fulltext |
| spellingShingle | Khaksar, Siavash Checker, Luke Borazjani, Bita Murray, Iain Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition |
| title | Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition |
| title_full | Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition |
| title_fullStr | Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition |
| title_full_unstemmed | Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition |
| title_short | Design and Evaluation of an Alternative Control for a Quad-Rotor Drone Using Hand-Gesture Recognition |
| title_sort | design and evaluation of an alternative control for a quad-rotor drone using hand-gesture recognition |
| url | http://hdl.handle.net/20.500.11937/92441 |