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Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping

Author

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  • Ahmed Ismail

    (Information System Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt)

  • Samir Abdlerazek

    (Information System Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt)

  • Ibrahim M. El-Henawy

    (Department of Computer Science, faculty of Computers and Information, El-Zagazig University, Zagazig, Sharqiyah 44519, Egypt)

Abstract

This paper presents an effective solution based on speech recognition to provide elderly people, patients and disabled people with an easy control system. The goal is to build a low-cost system based on speech recognition to easily access Internet of Things (IoT) devices installed in smart homes and hospitals without relying on a centralized supervisory system. The proposed system used a Raspberry Pi board to control home appliances through wireless with smartphones. The main purpose of this system is to facilitate interactions between the user and home appliances through IoT communications based on speech commands. The proposed framework contribution uses a hybrid Support Vector Machine (SVM) with a Dynamic Time Warping (DTW) algorithm to enhance the speech recognition process. The proposed solution is a machine learning-based system for controlling smart devices through speech commands with an accuracy of 97%. The results helped patients and elderly people to access and control IoT devices that are compatible with our system using speech recognition. The proposed speech recognition system is flexible with scalability and availability in adapting to existing smart IoT devices, and it provides privacy in managing patient devices. The research provides an effective method to integrate our systems among medical institutions to help elderly people and patients.

Suggested Citation

  • Ahmed Ismail & Samir Abdlerazek & Ibrahim M. El-Henawy, 2020. "Development of Smart Healthcare System Based on Speech Recognition Using Support Vector Machine and Dynamic Time Warping," Sustainability, MDPI, vol. 12(6), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2403-:d:334306
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    References listed on IDEAS

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    Cited by:

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    2. Jorge Morato & Sonia Sanchez-Cuadrado & Ana Iglesias & Adrián Campillo & Carmen Fernández-Panadero, 2021. "Sustainable Technologies for Older Adults," Sustainability, MDPI, vol. 13(15), pages 1-35, July.
    3. Hawon Chu & Jaeseong Kim & Seounghyeon Kim & Young-Kyoon Suh & Ryong Lee & Rae-Young Jang & Minwoo Park, 2020. "ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    4. Alfonso Marino & Paolo Pariso & Michele Picariello, 2023. "Energy use and End-use Technologies: Organizational and Energy Analysis in Italian Hospitals," International Journal of Energy Economics and Policy, Econjournals, vol. 13(3), pages 36-45, May.
    5. Arshiya Mariam & Hamed Javidi & Emily C Zabor & Ran Zhao & Tomas Radivoyevitch & Daniel M Rotroff, 2024. "Unsupervised clustering of longitudinal clinical measurements in electronic health records," PLOS Digital Health, Public Library of Science, vol. 3(10), pages 1-20, October.
    6. Guoquan Zhang & Guohao Li & Jing Peng, 2020. "Risk Assessment and Monitoring of Green Logistics for Fresh Produce Based on a Support Vector Machine," Sustainability, MDPI, vol. 12(18), pages 1-20, September.

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