Posture Detection Framework Using the Internet of Wearable Things

dc.contributor.authorBasmaji, Tasnim
dc.contributor.authorYaghi, Maha
dc.contributor.authorZia, Huma
dc.contributor.authorQamhieh, Lana
dc.contributor.authorAbueida, Doaa
dc.contributor.authorAbueida, Maha
dc.contributor.authorGhazal, Mohammed
dc.date.accessioned2024-06-02T22:00:52Z
dc.date.available2024-06-02T22:00:52Z
dc.date.issued2022-07
dc.description.abstractNeck and back pains are the most common health problems nowadays that can last from days to years, depending on the cause. Slouching for long periods while working or using smartphones, tablets, and computers would worsen the pain. Many medical studies show monitoring and adjusting seating posture can prevent spinal pain. This paper proposes a real-time posture detection tool based on an IoT belt and an HD camera. The IoT belt integrates a microcontroller unit and an Inertial Measurement Unit (IMU) sensor to collect posture data, including the thoracic and thoracolumbar angles. The collected sensor data and the captured videos are transmitted to a developed mobile application through a cloud server. The cross-platform mobile application allows users to view and track the seating posture over time. Our results show that the proposed tool is low-cost, user-friendly, and reliable and can be used to collect posture data to train machine learning models for different health-related applications. © 2022 IEEE. Keywords Back pain, Inertial sensors, Posture detection, Real-time system, Seating posture
dc.identifier.citationBasmaji, T., Yaghi, M., Zia, H., Qamhieh, L., Abueida, D., Abueida, M., & Ghazal, M. (2022, July). Posture detection framework using the internet of wearable things. In 2022 2nd International Conference on Computing and Machine Intelligence (ICMI) (pp. 1-5). IEEE.
dc.identifier.doihttps://doi.org/10.1109/ICMI55296.2022.9873656
dc.identifier.urihttps://dspace.adu.ac.ae/handle/1/5552
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.titlePosture Detection Framework Using the Internet of Wearable Things
dc.typeConference Paper

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