AI-driven framework for text neck syndrome detection using non-contact software-defined RF sensing and sequential deep learning

Daniyal Yousaf (), Muhammad Bilal Khan (), Hazrat Bilal (), Abdul Basit Khattak (), Hamna Baig (), Shujaat Ali Khan Tanoli (), Muhammad Shamrooz Aslam (), Inam Ullah (), Shakila Basheer () and Ali Kashif Bashir ()
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Daniyal Yousaf: COMSATS University Islamabad, Attock Campus
Muhammad Bilal Khan: University of Brighton
Hazrat Bilal: University of Science and Technology of China
Abdul Basit Khattak: University of Oulu
Hamna Baig: COMSATS University Islamabad, Attock Campus
Shujaat Ali Khan Tanoli: COMSATS University Islamabad, Attock Campus
Muhammad Shamrooz Aslam: China University of Mining and Technology
Inam Ullah: Gachon University
Shakila Basheer: Princess Nourah bint Abdulrahman University
Ali Kashif Bashir: Manchester Metropolitan University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 3, No 19, 19 pages

Abstract: Abstract Text neck syndrome is a rapidly growing health concern in today’s society, largely caused by the excessive use of mobile devices. Text neck syndrome has a significant impact on the musculoskeletal health of the broader population, particularly among frequent users of mobile devices. These types of health issues require treatment at an early stage, as they tend to worsen over time and become more difficult to manage. To address this issue, this study presents an innovative non-contact posture monitoring system using software-defined radio (SDR) technology to detect and analyse postural patterns associated with text neck syndrome for early interventions. The non-contact software-defined radio sensing system is developed using Universal Software Radio Peripheral (USRP) devices equipped with antennas. The experiments are conducted in a controlled lab environment to collect a dataset of distinct neck tilt angles ( $$0^o$$ 0 o , $$15^o$$ 15 o , $$30^o$$ 30 o , $$45^o$$ 45 o , and $$60^o$$ 60 o ). The collected dataset is processed using advanced signal processing techniques to clean and smooth the postural patterns. The machine learning (ML) and deep learning (DL) algorithms are used to categorise postural patterns and identify deviations indicative of text neck syndrome. The performance of these models was subsequently evaluated. The results demonstrate the ML and DL model’s ability to detect healthy and unhealthy postures with a maximum accuracy of 99.97% by using the random forest ML model and 99.89% by using the Bidirectional Long-Term Memory (Bi-LSTM) DL model. This system represents an accessible, contactless, and portable solution with the potential to revolutionise the early detection of text neck syndrome.

Keywords: Artificial intelligence (AI); Channel state information (CSI); Radio frequency (RF); Software-defined radio (SDR); Text neck syndrome (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11235-025-01330-x

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