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 time, 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 maximum accuracy of 99.9% with fine kth-nearest neighbors (KNN). The developed testbed has a considerable clinical impact on improving human health.