DeepRespNet: a hybrid attention-recurrent framework for non-contact respiratory rate estimation

The noncontact measurement of respiratory rate (RR) has gained considerable attention recently due to its relevance to remote healthcare and continuous physiological monitoring. However, existing camera-based approaches often exhibit reduced accuracy in subjects having darker skin tones, primarily owing to melanin absorption and variations in illumination that degrade remote photoplethysmographic (rPPG) signal quality. This study aims to develop a robust deep learning framework that ensures reliable RR estimation across diverse skin tones.

A hybrid deep learning framework, referred to as DeepRespNet, is proposed that jointly analyzes rPPG and motion signals extracted from facial and thoracic regions in RGB video sequences. The core feature encoder, termed RespFormer, integrates spatiotemporal convolution with multi-head self-attention to capture both local and long-range respiratory patterns. The learned representations are further processed by a separate bidirectional long short-term memory (BiLSTM) network to model temporal coherence and generate physiologically stable respiratory waveforms. Optical-flow-based motion features and rPPG color variations are combined to form a multi-channel respiratory representation.

The proposed framework was evaluated on a multi-subject dataset with synchronized reference respiration signals. Experimental results achieve mean absolute errors of 0.45 breaths per minute (BPM) for light-skinned subjects and 0.80 BPM for dark-skinned subjects. Bland-Altman and cross dataset analyses further confirm strong agreement and consistent performance across skin tone groups.

The proposed framework enables reliable and skin-tone-aware noncontact respiratory rate estimation. Initial findings indicate its potential suitability for camera-based respiratory monitoring in remote healthcare and telemedicine applications, though further validation on larger populations is required.