Method and system for monitoring a human state on an arousal scale

The arousal estimation system addresses the challenge of accurately distinguishing psychophysiological states by generating a single arousal score, facilitating timely and effective interventions to maintain optimal driver arousal, enhancing safety and performance.

WO2026127960A1 Publication Date: 2026-06-18HARMAN INT IND INC
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Patent Information

Authority / Receiving Office
WO · WO
Patent Type
Applications
Current Assignee / Owner
HARMAN INT IND INC
Filing Date
2024-12-11
Publication Date
2026-06-18

AI Technical Summary

⚠Technical Problem

Current driver monitoring systems struggle to accurately distinguish between different psychophysiological states of drivers, such as drowsiness and stress, leading to conflicting assessments and inappropriate interventions due to misinterpretation of sensor data.

⚗Method used

An arousal estimation system that uses a model to generate a single arousal score based on a wide range of sensor data, including biosignals and camera images, to characterize the driver's psychophysiological state on an arousal scale, allowing for targeted interventions to maintain the driver within a desired range of arousal levels.

🎯Benefits of technology

The system provides more accurate assessments of driver arousal, enabling timely and appropriate interventions to enhance safety by adjusting vehicle settings or notifying the driver, thereby improving driving performance and reducing the risk of accidents.

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Abstract

An arousal estimation system is proposed herein that estimates a human psychophysiological state on an arousal scale, using an arousal detection model that outputs a single arousal score characterizing arousal, rather than a particular target state, such as drowsiness, stress, or cognitive load. Based on an estimated arousal score, the human state can be characterized as either within or outside a desired range of arousal levels associated with optimal performance of relevant tasks. The arousal detection model is trained on a wider set of sensor data than may be applicable to a specific mental state. By training the arousal detection model on the wider set of sensor data associated with different arousal levels, an arousal score generated by the arousal detection model may generate a more accurate estimation of the human state than the conventional single state detectors, even when combined.
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