Wearable RF Sensing System with Edge AI Inference for In-vivo Cognitive Load Classification

Cognitive load (CL) refers to the mental effort required to process information. Monitoring increased CL is important as it can indicate the onset of cognitive decline and neurodegeneration, allowing for early intervention and management. Traditional techniques using bio-physiological and audio-visual sensors are privacy-invasive and computationally complex. The synchronization, data alignment, and accessibility problems with these techniques can lead to increased noise and errors, reducing the accuracy of CL estimates. This paper presents a first-of-its-kind Radio Frequency (RF) based sensing system that effectively monitors and classifies cognitive load states by detecting cerebral blood flow variations through backscattered RF signal strength. The RF sensors are designed and miniaturized using microwave computational software and fabricated sensors are integrated with glasses to estimate in-vivo CL variations with on-edge processing and classification. The system is validated through user-oriented audio-only (AO) and audio-visual (AV) trials. Participants are tested on their ability to comprehend target speech with different levels of background noise, assessing the impact on CL. The statistical features from the RF reflection data are processed using machine learning (ML) and deep learning (DL) algorithms implemented on a Raspberry Pi. The participants’ CL is classified into high, medium and low categories separately for AO and AV trials. The Multilayer Perceptron (MLP) achieves an overall accuracy of 85% for AO trials and 66.2% for AV trials, with average training and testing times of 6 seconds and 0.001 seconds, respectively. The promising results suggest that the system is an effective, portable, and low-cost alternative for on-edge CL estimation.