  Stress recognition through physiological signals such as Electrocardiogram
(ECG) signals has garnered significant attention. Traditionally, research in
this field predominantly focused on utilizing handcrafted features or raw
signals as inputs for learning algorithms. However, there is now a burgeoning
interest within the community in leveraging large-scale vision foundation
models (VFMs) like ResNet50, VGG19, and others. These VFMs are increasingly
preferred due to their ability to capture complex features, enhancing the
accuracy and effectiveness of stress recognition systems. However, no
particular focus has been given on combining these VFMs. The combination of
VFMs offers promising benefits by harnessing their collective knowledge to
extract richer representations for improved stress recognition. So, to mitigate
this research gap, we focus on combining different VFMs for stress recognition
from ECG and propose SONIC, a novel framework that combines VFMs through their
logits and training a fully connected network on the combined logits. Through
extensive experimentation, SONIC showed the top performance against individual
VFMs performance on the WESAD benchmark. With SONIC, we report state-of-the-art
(SOTA) performance in WESAD with 99.36% and 99.24% (stress vs non-stress) and
97.66% and 97.10% (amusement vs stress vs baseline) in accuracy and F1
respectively.
