Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment
Gesture recognition has a crucial role in Human–Computer Interaction (HCI) and in assisting the elderly to perform automatically their everyday activities. In this paper, three methods for gesture recognition and computer vision were implemented and tested in order to investigate the most suitable o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | AI |
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| Online Access: | https://www.mdpi.com/2673-2688/6/1/17 |
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| author | Christos Panagiotou Evanthia Faliagka Christos P. Antonopoulos Nikolaos Voros |
| author_facet | Christos Panagiotou Evanthia Faliagka Christos P. Antonopoulos Nikolaos Voros |
| author_sort | Christos Panagiotou |
| collection | DOAJ |
| description | Gesture recognition has a crucial role in Human–Computer Interaction (HCI) and in assisting the elderly to perform automatically their everyday activities. In this paper, three methods for gesture recognition and computer vision were implemented and tested in order to investigate the most suitable one. All methods, machine learning using IMU, machine learning on device, and were combined with certain activities that were determined during a needs analysis research. The same volunteers took part in the pilot testing of the proposed methods. The results highlight the strengths and weaknesses of each approach, revealing that while some methods excel in specific scenarios, the integrated solution of MoveNet and CNN provides a robust framework for real-time gesture recognition. |
| format | Article |
| id | doaj-art-cc3bfeae1af54653a7de3cbf23f4f196 |
| institution | Kabale University |
| issn | 2673-2688 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | AI |
| spelling | doaj-art-cc3bfeae1af54653a7de3cbf23f4f1962025-01-24T13:17:24ZengMDPI AGAI2673-26882025-01-01611710.3390/ai6010017Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home EnvironmentChristos Panagiotou0Evanthia Faliagka1Christos P. Antonopoulos2Nikolaos Voros3Electrical & Computer Engineering Department, University of Peloponnese, M. Alexandrou 1, 22100 Patras, GreeceElectrical & Computer Engineering Department, University of Peloponnese, M. Alexandrou 1, 22100 Patras, GreeceElectrical & Computer Engineering Department, University of Peloponnese, M. Alexandrou 1, 22100 Patras, GreeceElectrical & Computer Engineering Department, University of Peloponnese, M. Alexandrou 1, 22100 Patras, GreeceGesture recognition has a crucial role in Human–Computer Interaction (HCI) and in assisting the elderly to perform automatically their everyday activities. In this paper, three methods for gesture recognition and computer vision were implemented and tested in order to investigate the most suitable one. All methods, machine learning using IMU, machine learning on device, and were combined with certain activities that were determined during a needs analysis research. The same volunteers took part in the pilot testing of the proposed methods. The results highlight the strengths and weaknesses of each approach, revealing that while some methods excel in specific scenarios, the integrated solution of MoveNet and CNN provides a robust framework for real-time gesture recognition.https://www.mdpi.com/2673-2688/6/1/17gesture detectionedge computingInternet of Things system |
| spellingShingle | Christos Panagiotou Evanthia Faliagka Christos P. Antonopoulos Nikolaos Voros Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment AI gesture detection edge computing Internet of Things system |
| title | Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment |
| title_full | Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment |
| title_fullStr | Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment |
| title_full_unstemmed | Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment |
| title_short | Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment |
| title_sort | multidisciplinary ml techniques on gesture recognition for people with disabilities in a smart home environment |
| topic | gesture detection edge computing Internet of Things system |
| url | https://www.mdpi.com/2673-2688/6/1/17 |
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