Abstract
Wearable technologies are in pursuit of designing portable devices worn by people. In this paper, gesture recognition on a sleeve having a capacitive textile-based pressure sensor array has been developed. Machine learning models are generated to recognize a set of gestures by accumulating capacitance values read from a sensor array during the execution of gestures. The performance of the gesture recognition system is evaluated in the experiments conducted with human test subjects. Real-time gesture prediction is achieved using the trained model. The performed gestures and predicted data are collected and analyzed, resulting in accuracies up to 86'.
| Original language | English |
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| Title of host publication | EEITE 2024 - Proceedings of 2024 5th International Conference in Electronic Engineering, Information Technology and Education |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350372878 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 5th International Conference in Electronic Engineering, Information Technology and Education, EEITE 2024 - Chania, Greece Duration: 29 May 2024 → 31 May 2024 |
Publication series
| Name | EEITE 2024 - Proceedings of 2024 5th International Conference in Electronic Engineering, Information Technology and Education |
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Conference
| Conference | 5th International Conference in Electronic Engineering, Information Technology and Education, EEITE 2024 |
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| Country/Territory | Greece |
| City | Chania |
| Period | 29/05/24 → 31/05/24 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Capacitive pressure sensor
- etextile
- gesture recognition
- human-computer interaction
- sensor array