A Non-Invasive Smart Sensing of Text Neck Syndrome Using SDR Technology
Khattak, Abdul Basit; Tanoli, Shujaat Ali Khan; Khan, Muhammad Bilal; Mustafa, Ali; Ullah, Farman; Kwak, Daehan; López, Onel L. A. (2025-06-26)
IEEE
26.06.2025
A. B. Khattak et al., "A Non-Invasive Smart Sensing of Text Neck Syndrome Using SDR Technology," 2025 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Poznan, Poland, 2025, pp. 631-636, doi: 10.1109/EuCNC/6GSummit63408.2025.11036969
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© 2025 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202506275017
https://urn.fi/URN:NBN:fi:oulu-202506275017
Tiivistelmä
Abstract
Smartphones are extensively used for communication, business, study, entertainment, and other purposes in everyone's daily life. Unfortunately, using the smartphone for prolonged periods causes several problems. The development of a complicated cluster of clinical symptoms known as “text neck syndrome” may be linked to the improper usage of personal devices, especially mobile phones. In addition, typical postures while using mobile phone devices can cause musculoskeletal problems. Various technologies are being considered to keep track of health and identify problems unobtrusively. This paper employs software-defined radio (SDR) based RF sensing and machine learning (ML) algorithms to develop a testbed for detecting text neck syndrome and classifying healthy and unhealthy postures. Specifically, fine-grained orthogonal frequency division multiplex (OFDM) samples are leveraged for channel state information (CSI) acquisition for detecting neck tilt angles while using the mobile phone. For classification purposes, the ML algorithms are used, and their performance in terms of prediction speed, training time, and accuracy is assessed. The performance evaluation results of the testbed validated that this platform can faithfully detect and classify healthy and unhealthy postures with a maximum accuracy of 99.9 % with fine kth-nearest neighbors (KNN). The developed testbed can have a considerable clinical impact on improving human health.
Smartphones are extensively used for communication, business, study, entertainment, and other purposes in everyone's daily life. Unfortunately, using the smartphone for prolonged periods causes several problems. The development of a complicated cluster of clinical symptoms known as “text neck syndrome” may be linked to the improper usage of personal devices, especially mobile phones. In addition, typical postures while using mobile phone devices can cause musculoskeletal problems. Various technologies are being considered to keep track of health and identify problems unobtrusively. This paper employs software-defined radio (SDR) based RF sensing and machine learning (ML) algorithms to develop a testbed for detecting text neck syndrome and classifying healthy and unhealthy postures. Specifically, fine-grained orthogonal frequency division multiplex (OFDM) samples are leveraged for channel state information (CSI) acquisition for detecting neck tilt angles while using the mobile phone. For classification purposes, the ML algorithms are used, and their performance in terms of prediction speed, training time, and accuracy is assessed. The performance evaluation results of the testbed validated that this platform can faithfully detect and classify healthy and unhealthy postures with a maximum accuracy of 99.9 % with fine kth-nearest neighbors (KNN). The developed testbed can have a considerable clinical impact on improving human health.
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