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Smart Life: A Lifesaving Wearable System For Senior Citizen

Author

Listed:
  • DANIAL JAMIL

    (Department of Computer Sciencce & IT, Ghazi University, Dera Ghazi Khan, Pakistan)

  • MUHAMMAD TALHA TAHIR BAJWA

    (Department of Computer Science, University of Agriculture Faisalabad, Pakistan)

  • TAHIR KHALIL

    (Department of Computer Science, University of Agriculture Faisalabad, Pakistan)

  • HUMERA OMER FAROOQ

    (College of Art & Design, University of the Punjab, Lahore, Pakistan)

  • IRAM NAEEM

    (M.Phil. (ELM), Punjab School Education Department, Lahore)

  • KHADIJA SHAHZAD

    (MS Leadership & Management, Islamia University Bahawalpur, Pakistan)

Abstract

Deterioration in an aged person's mobility, self-determination, and quality of lifestyle. This paper proposes a special Internet of Things-based system for recognizing indoor falls among the elderly by combining lightweight devices mobile sensing connections, big data, cloud computing, and smart appliances. For this, we use a wrist-worn sixLowPAN device equipped with an accelerometer with three axes to monitor the exact location and motion of senior citizens in real-time. A powerful IoT network analyzes the sensor signals is applying machine learning algorithms, which helps resulting in resulting in improved recognition of falls outcomes. For systems, we employ an incremental model with a long-memory framework. for the classification of falls, and economical Portable detecting gadget from Apache Flink and MbientLab, with a free software encoder. Using the initial data set, that is freely accessible "MobiAct," we evaluate the most effective Nyquist rate, sensor location, and multi-transmitting data modification. Our system for edge computing uses analytics on information streams in real-time to identify falls with a 95.87% efficiency ratio.

Suggested Citation

  • Danial Jamil & Muhammad Talha Tahir Bajwa & Tahir Khalil & Humera Omer Farooq & Iram Naeem & Khadija Shahzad, 2023. "Smart Life: A Lifesaving Wearable System For Senior Citizen," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 12(2), pages 260-268.
    • Handle: RePEc:rfh:bbejor:v:12:y:2023:i:2:p:260-268
    DOI: https://zenodo.org/records/8371177
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    References listed on IDEAS

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    1. Wei Bao & Jun Yue & Yulei Rao, 2017. "A deep learning framework for financial time series using stacked autoencoders and long-short term memory," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-24, July.
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