StressNet: Neuromorphic multimodal stress recognition from wearable physiological signals

Abstract Continuous, real-time monitoring of psychological stress is becoming increasingly important in digital health and remote patient-monitoring systems. However, deploying always-on stress-monitoring models on consumer wearable devices remains challenging because of their limited computational resources and microjoule-scale energy budgets. This paper presents StressNet (Multimodal Spiking Stress Network), a spike-driven neuromorphic framework for real-time, subject-independent stress monitoring from multimodal physiological signals acquired using a consumer wrist-worn device. StressNet is designed for efficient on-device inference on edge-class wearable hardware. Each physiological modality is first converted into spike trains using a learnable adaptive-threshold leaky-integrate-and-fire encoder. Reliability-gated sparse spike-feature cross-modal attention is then used to selectively integrate complementary information across modalities, while a recurrent neuromorphic memory captures temporal dependencies across time. The network is trained end-to-end using surrogate-gradient backpropagation through time and evaluated under leave-one-subject-out (LOSO) cross-validation on the three-class WESAD benchmark using only the six wrist-based physiological channels. Across five random seeds, StressNet achieves a mean accuracy of 97.50% with a standard deviation of 0.88 percentage points and a macro-F1 score of 0.974. These results were obtained using 60-second windows with a 7-second stride. A mean expected calibration error (ECE) of 0.023 indicates good calibration under the evaluated protocol. StressNet requires an estimated 32.5, $$\\mu$$ J of energy and 7.95,ms of model-side inference time per window. With preprocessing included, these estimates increase to approximately 33.5, $$\\mu$$ J and 8.21,ms. These values are analytical estimates rather than direct hardware measurements. Overall, StressNet demonstrates promising subject-independent stress-recognition performance and estimated computational efficiency for wearable deployment, while broader calibration studies and real-world hardware validation remain important directions for future work.