Bio-Inspired Adaptive Multimodal Decision Fusion for Intelligent Safety Monitoring in Confined Spaces

To improve operational safety in confined spaces, this study proposes an intelligent safety monitoring framework that utilizes multimodal data from wearable devices. The framework comprises two core components: a human activity recognition (HAR) module and a bio-inspired adaptive multimodal decision fusion (BA-MDF) module. The HAR module processes accelerometer and gyroscope data through an enhanced FFT-LSTM architecture that integrates time- and frequency-domain features for real-time activity classification. The BA-MDF module, inspired by biological multisensory integration mechanisms-particularly the inverse effectiveness principle observed in the superior colliculus-evaluates contextual risk by adaptively fusing HAR outputs, heart rate variability, and geospatial constraints without additional computational overhead. Experimental testing demonstrated 92.4% overall HAR accuracy and 94.3% identification accuracy for emergency scenarios under a simulated sensor degradation environment. These results validate the framework's effectiveness in mitigating risks from anomalous events in visually constrained environments.