OmniBuds用耳戴设备实现本地实时生理监测与机器学习。
OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning
- 双耳对称设计集成多种传感器，支持多模态生理信号采集。
- 在设备端实时运行复杂模型，延迟更低且数据不外传。
- 适合健康监测、人机交互等需要隐私保护的场景。
感官耳戴设备已从基础音频增强演进为临床级健康监测与福祉管理平台。本文提出OmniBuds，一个集成多生物传感器与机载计算能力的先进耳戴平台，基于实时操作系统（RTOS）运行，配备机器学习加速器。其双耳对称设计融合精确定位的运动、声学、光学和热传感器，实现高精度、实时的生理评估。与依赖外部处理的传统耳戴设备不同，OmniBuds通过本地实时计算显著提升系统效率，降低延迟，并通过本地数据处理保障隐私。该平台可直接在设备上执行复杂机器学习模型，具备多功能应用潜力，实现生理参数的精准可靠追踪及高级人机交互。
原文摘要 · Abstract (English)
Sensory earables have evolved from basic audio enhancement devices into sophisticated platforms for clinical-grade health monitoring and wellbeing management. This paper introduces OmniBuds, an advanced sensory earable platform integrating multiple biosensors and onboard computation powered by a machine learning accelerator, all within a real-time operating system (RTOS). The platform's dual-ear symmetric design, equipped with precisely positioned kinetic, acoustic, optical, and thermal sensors, enables highly accurate and real-time physiological assessments. Unlike conventional earables that rely on external data processing, OmniBuds leverage real-time onboard computation to significantly enhance system efficiency, reduce latency, and safeguard privacy by processing data locally. This capability includes executing complex machine learning models directly on the device. We provide a comprehensive analysis of OmniBuds' design, hardware and software architecture demonstrating its capacity for multi-functional applications, accurate and robust tracking of physiological parameters, and advanced human-computer interaction.
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