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Towards Real-Time Vision-Based Sign Language Recognition on Edge Devices
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).
Mid Sweden University, Faculty of Science, Technology and Media, Department of Computer and Electrical Engineering (2023-).ORCID iD: 0000-0002-8382-0359
2024 (English)In: 2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings, IEEE conference proceedings, 2024Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a comparative study focused on the classification of American Sign Language (ASL) gestures and the challenges involved in interpreting the signs for effective communication. Using transfer learning this study evaluates three variants of MobileNet, a machine-learning model optimized for low-resource environments, on a vision-based dataset. The models are deployed on an STM32F746G microcontroller with a Cortex-M7 core. Two frameworks are compared, namely TensorFlow Lite for Microcontrollers and STM32Cube.AI. An ArduCam Mini camera with a maximum image resolution of 5 megapixels is utilized to capture the hand gestures. The study concludes that STM32Cube.AI is the preferred implementation due to its lower model ROM and RAM requirements. Among the three tested models, MobileNetV1 is the most suitable for the task, achieving the highest F1-score of 0.865, the smallest memory footprint of 290.96 kB of ROM and 85.59 kB of RAM, and the shortest inference time of 103 ms. Despite these promising results, the models encountered some difficulties distinguishing between similar signs, highlighting the challenges involved in real-time sign language recognition and the need for further research. 

Place, publisher, year, edition, pages
IEEE conference proceedings, 2024.
Keywords [en]
American sign language, computer vision, embedded machine learning, embedded systems, TinyML, transfer learning
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:miun:diva-52587DOI: 10.1109/SAS60918.2024.10636604ISI: 001304520300093Scopus ID: 2-s2.0-85203713331ISBN: 9798350369250 (print)OAI: oai:DiVA.org:miun-52587DiVA, id: diva2:1900668
Conference
2024 IEEE Sensors Applications Symposium, SAS 2024 - Proceedings
Available from: 2024-09-24 Created: 2024-09-24 Last updated: 2024-11-25Bibliographically approved

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Bader, Sebastian

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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More languages
Output format
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