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<p>Vestibular dysfunction is a common cause of dizziness and a leading cause of medical visits. Yet, current assessment methods of dizziness remain largely subjective, relying on self-reports and intermittent clinical evaluations that lack real-time monitoring, quantitative precision, and preventive capability. This paper introduces EquilibriSense, a bio-inspired, head-worn system for quantifying motion-induced dizziness under a controlled head-rotation paradigm. The system integrates multiple physiological sensing modalities with an AI-driven pipeline and a neurocomputational labeling framework to model dizziness progression and classify symptom severity on a five-level scale (0-4). In a pilot study involving a small cohort of 10 healthy participants in a controlled laboratory setting, the system achieved 86.8% accuracy in multi-level motion-induced dizziness classification and enabled early detection of dizziness onset with an AUC of 0.99 and over 98% accuracy, supported by high precision and recall. These results demonstrate the feasibility of using multimodal physiological sensing to characterize motion-induced dizziness and establish EquilibriSense as a proof-of-concept platform for objective dizziness quantification, providing a foundation for future validation in real-world and clinical populations.</p>

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Objective quantification of motion-induced dizziness using a proof-of-concept multimodal wearable platform

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Nhan Cao,
  • Brian Loyd,
  • Andy Kittelson,
  • Anh Nguyen

摘要

Vestibular dysfunction is a common cause of dizziness and a leading cause of medical visits. Yet, current assessment methods of dizziness remain largely subjective, relying on self-reports and intermittent clinical evaluations that lack real-time monitoring, quantitative precision, and preventive capability. This paper introduces EquilibriSense, a bio-inspired, head-worn system for quantifying motion-induced dizziness under a controlled head-rotation paradigm. The system integrates multiple physiological sensing modalities with an AI-driven pipeline and a neurocomputational labeling framework to model dizziness progression and classify symptom severity on a five-level scale (0-4). In a pilot study involving a small cohort of 10 healthy participants in a controlled laboratory setting, the system achieved 86.8% accuracy in multi-level motion-induced dizziness classification and enabled early detection of dizziness onset with an AUC of 0.99 and over 98% accuracy, supported by high precision and recall. These results demonstrate the feasibility of using multimodal physiological sensing to characterize motion-induced dizziness and establish EquilibriSense as a proof-of-concept platform for objective dizziness quantification, providing a foundation for future validation in real-world and clinical populations.