2025 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD 2025)

This paper designs an online monitoring system for fatigue, anesthesia and sleep states based on a multi-parameter biological information perception network. It realizes the real-time collection and wireless transmission of multi-modal physiological signals such as electroencephalogram (EEG), electrocardiogram (ECG), electromyogram (EMG), respiratory rate and limb movement through wearable and low-cost sensor nodes. The system uses Wi-Fi networking and combines LabVIEW platforms for data processing and fusion analysis. It uses the ratio method or BP neural network to achieve accurate assessment of human states. Experiments show that the system can still work stably under the synchronous sampling of 16 nodes, with high monitoring accuracy and good practicality and promotion value.

Wearable sensor, Multi-modal physiological signal acquisition, Physiological state monitoring system