Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer

Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture. To the best of our knowledge, we are …

accuracy ann anns artificial artificial neural networks challenges computing computing resources cs.cv deployment devices measuring mobile mobile devices networks neural networks rate resources transformer videos