Multimodal Respiration Signal Analysis for Wearable Cardiorespiratory Monitoring Using a Flexible Graphene-Based Sensor: A Comparative Study

The increasing prevalence of chronic cardiopulmonary diseases and the growing demand for remote healthcare solutions highlight the need for continuous and reliable physiological monitoring. This work proposes a flexible graphene-based multimodal sensor for simultaneous cardiorespiratory monitoring. Owing to the intrinsic properties of graphene-based nanocomposites, including high electrical conductivity, mechanical strength, and flexibility, the proposed sensor enables a direct multimodal approach using a reduced sensing architecture. Unlike approaches that require a dedicated respiratory sensor or additional respiratory sensing modality, this sensor acquires electrocardiogram (ECG) signals while extracting respiratory activity from variations at the electrode–skin interface from the same flexible electrode platform. This enables simultaneous monitoring of cardiac and respiratory activity while reducing sensing hardware complexity. Respiratory signals are validated against a reference force/pressure sensor, referred to as pressure-derived respiration (PDR). The proposed method is evaluated in terms of waveform similarity, frequency domain characteristics, temporal alignment, and respiratory rate (RR) agreement. The results demonstrate the accuracy and feasibility of the proposed approach in this proof-of-concept study, highlighting its potential for continuous and unobtrusive respiration monitoring in wearable healthcare applications.