Hypoglycemia Vehicle Detection System Using Non-Invasive Sensors Applying Both EEG And HRV Real Time Measures: Neuroergonomics Theoretical Design

Aljuaid, Awad · 2022 · Crossref

DOI: 10.54941/ahfe1001480

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Summary

This paper proposes a conceptual design for a non-invasive vehicle detection system aimed at identifying hypoglycemia in drivers to mitigate cognitive dysfunction and reduce accident risks. The research is motivated by the increased demand for ride-sharing and delivery services during the COVID-19 pandemic, which has heightened the exposure of diabetic drivers to hypoglycemic episodes. Hypoglycemia, defined as blood glucose levels below 70 mg/dL, is associated with significant cognitive impairment that can persist for up to 40 minutes after glucose normalization and has been linked to a 130% increase in car accidents. The study leverages neuroergonomics, integrating neuroscience and ergonomics, to monitor human-machine interactions and optimize safety through real-time biosignal analysis. The proposed system simultaneously measures two key biosignals: electroencephalography (EEG) and heart rate variability (HRV). EEG signals are acquired using a four-channel OPENBCI Ganglion device, while HRV is measured via photoplethysmogram (PPG) or electrocardiogram (ECG) sensors. The methodology involves preprocessing EEG signals with notch and band-pass filters (5–50 Hz) and converting them to the frequency domain using Fast Fourier Transform (FFT) to analyze delta, theta, alpha, beta, and gamma bands. HRV is analyzed in both time and frequency domains, calculating the root mean square of successive differences (RMSSD) and the low-frequency to high-frequency (LF:HF) ratio. The system’s detection logic relies on an Arduino Mega 2560 microcontroller that processes data streamed via Lab Streaming Layer (LSL). Hypoglycemia is flagged when specific EEG patterns persist for 10 seconds, specifically the activation of low-frequency delta and theta bands alongside the suppression of alpha and beta bands. This neural indicator is corroborated by abnormal HRV metrics, defined as an RMSSD value 20% below the short-term norm of 42 milliseconds or an LF:HF ratio 30% above the norm of 2.8 ms². Upon detection, the microcontroller communicates with the vehicle’s On-Board Diagnostics (OBD II) port to activate warning lights and potentially reduce speed via cruise control. Additionally, the system transmits the driver’s GPS location, vehicle speed, and biosignal data to external observers via Bluetooth and GSM modules. The significance of this work lies in its integration of real-time neural and cardiovascular monitoring into automotive safety systems. By detecting hypoglycemia before severe cognitive dysfunction occurs, the system aims to prevent accidents among diabetic drivers. The design demonstrates a practical application of neuroergonomic principles, utilizing non-invasive sensors to create a robust, multi-modal detection framework that addresses a critical health and safety issue in modern transportation logistics.

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• drowsiness detection algorithms

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• Empirical Findings: physiological data