Key result
An RF-based breathing monitoring system utilizing optimal feature scoring achieved maximum classification accuracies of 93.8% and 91.7% for single-person and multi-person scenarios, respectively.
Why the study?
Increasing data dimensionality challenges machine learning-based RF breathing monitoring systems, making optimal feature scoring a potential solution to improve performance.
Does an RF-based breathing monitoring system with optimal feature scoring improve the accuracy of classifying breathing abnormalities in single and multiple persons?
Does an RF-based breathing monitoring system with optimal feature scoring improve the accuracy of classifying breathing abnormalities in single and multiple persons?
An RF-based non-contact breathing monitoring system using optimal feature scoring and machine learning can accurately classify breathing abnormalities in single and multi-person scenarios.
Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system's performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system's performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.
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Rehman et al. (2023) studied Breathing abnormalities. RF-based breathing monitoring system with optimal feature scoring vs. Wearable sensor was evaluated on Accuracy of classifying breathing abnormalities. An RF-based breathing monitoring system utilizing optimal feature scoring achieved maximum classification accuracies of 93.8% and 91.7% for single-person and multi-person scenarios, respectively.
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