# OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning

arXiv: https://arxiv.org/abs/2410.04775 · HTML: https://mlsanity.com/paper/2410.04775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 42 references that are in the map.
- First posted on arXiv 7 Oct 2024.

## Builds on (most cited first, 7 of 7)

- [Evaluation of PPG Biometrics for Authentication in different states](https://mlsanity.com/paper/1712.08583/) (2017)
- [Semantic Hearing: Programming Acoustic Scenes with Binaural Hearables](https://mlsanity.com/paper/2311.00320/) (2023)
- [SensiX++: Bringing MLOPs and Multi-tenant Model Serving to Sensory Edge Devices](https://mlsanity.com/paper/2109.03947/) (2021)
- [OESense: Employing Occlusion Effect for In-ear Human Sensing](https://mlsanity.com/paper/2106.08607/) (2021)
- [EarGate: Gait-based User Identification with In-ear Microphones](https://mlsanity.com/paper/2108.12305/) (2021)
- [hEARt: Motion-resilient Heart Rate Monitoring with In-ear Microphones](https://mlsanity.com/paper/2108.09393/) (2021)
- [Wireless earbuds for low-cost hearing screening](https://mlsanity.com/paper/2212.05435/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fine-grained Soundscape Control for Augmented Hearing](https://mlsanity.com/paper/2603.00395/) (2026)

## Abstract (arXiv)

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.