Version 1
: Received: 5 November 2024 / Approved: 6 November 2024 / Online: 7 November 2024 (10:13:47 CET)
How to cite:
Panagiotou, C.; Faliagka, E.; Antonopoulos, C.; Voros, N. Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment. Preprints2024, 2024110452. https://doi.org/10.20944/preprints202411.0452.v1
Panagiotou, C.; Faliagka, E.; Antonopoulos, C.; Voros, N. Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment. Preprints 2024, 2024110452. https://doi.org/10.20944/preprints202411.0452.v1
Panagiotou, C.; Faliagka, E.; Antonopoulos, C.; Voros, N. Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment. Preprints2024, 2024110452. https://doi.org/10.20944/preprints202411.0452.v1
APA Style
Panagiotou, C., Faliagka, E., Antonopoulos, C., & Voros, N. (2024). Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment. Preprints. https://doi.org/10.20944/preprints202411.0452.v1
Chicago/Turabian Style
Panagiotou, C., Christos Antonopoulos and Nikolaos Voros. 2024 "Multidisciplinary ML Techniques on Gesture Recognition for People with Disabilities in a Smart Home Environment" Preprints. https://doi.org/10.20944/preprints202411.0452.v1
Abstract
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 to the pilot testing of the proposed methods. The results highlight the strengths and weaknesses of each approach, revealing that 60 while some methods excel in specific scenarios, the integrated solution of MoveNet and 61 CNN provides a robust framework for real-time gesture recognition.
Keywords
gesture detection; edge computing; Internet of Things system)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
