Wearable technology is advancing health monitoring by enabling real-time, privacy-preserving physiological analysis. However, traditional devices often rely on cloud processing, restricting access to raw sensor data and limiting the progress of health-related research. To overcome these limitations, we introduce OmniBuds, a programmable earable research platform that enables multimodal bio-sensing and on-device learning while providing direct access to raw physiological data, fostering advancements in health perception and wearable intelligence. It integrates PPG, temperature, IMUs, and multiple microphones, leveraging an embedded ML accelerator for efficient real-time processing. This paper presents its design, architecture, and applications, demonstrating its potential to shape the future of health-aware wearables.

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient heart rate monitoring. We first collected heart rate-induced sounds in the ear canal using an in-ear microphone under seven stationary activities and two full-body motion activities (i.e., walking, and running). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by a heart rate estimation algorithm to extract heart rate. With data collected from 15 subjects over nine activities, we demonstrate that hEARt, our end-to-end approach, achieves a mean absolute error (MAE) of 1.88 ± 2.89 BPM, 6.83 ± 5.05 BPM, and 13.19 ± 11.37 BPM for stationary, walking, and running, respectively, opening the door to a new non-invasive and affordable heart rate monitoring with usable performance for daily activities. Not only does hEARt outperform previous in-ear heart rate monitoring work, but it outperforms reported in-ear PPG performance.

With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate (HR) detection systems. HR is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable HR monitoring with wearable devices has therefore gained increasing attention in recent years. Existing HR detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time in-ear audio-based motion-resilient HR monitoring. We first collected HR-induced sounds in the ear canal leveraging an in-ear microphone under stationary and three different activities (i.e., walking, running, and speaking). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by an HR estimation algorithm to extract HR. With data collected from 20 subjects over four activities, we demonstrate that hEARt, our end-to-end approach, achieves a mean absolute error (MAE) of 3.02$\pm\ \boldsymbol{ 2.97}$BPM, 8.12$\pm\ \boldsymbol{6.74}$BPM, 11.23$\pm\ \boldsymbol{9.20}$BPM and 9.39$\pm\ \boldsymbol{6.97}$BPM for stationary, walking, running and speaking, respectively, opening the door to a new non-invasive and affordable HR monitoring with usable performance for daily activities. Not only does hEARt outperform previous in-ear HR monitoring work, but it outperforms reported in-ear PPG performance.

This paper revisits the opportunities of using WiFi offloading for Vehicle to Internet (V2I) communication, and how this has changed over the last decade. With the rollouts of provider-managed WiFi networks that are more structured and operate under authenticated regimes, WiFi offloading, or use of available (roadside) WiFi networks for V2I data communication, has different opportunities and challenges. To study the current landscape,we develop a system (X-Fi), which efficiently selects, associates to, authenticates with, and performs WiFi offloading for V2I communication with these networks, and a tool (X-Perf), which illustrates opportunities of WiFi offloading available today in these networks, with measurements and experiments across four metro areas across three continents over 22 months. Our results indicate the feasibility of achieving 1 GB/hour application goodput, an order of magnitude higher than the number provided by open WiFi networks in the past, which can take a significant load away from alternative communication paths for V2I systems. Moreover, we provide several implications on transport protocols and WiFi deployments to shed light on the use of such WiFi networks for V2I communication.

Human gait is a widely used biometric trait for user identification and recognition. Given the wide-spreading, steady diffusion of ear-worn wearables (Earables) as the new frontier of wearable devices, we investigate the feasibility of earable-based gait identification. Specifically, we look at gait-based identification from the sounds induced by walking and propagated through the musculoskeletal system in the body. Our system, EarGate, leverages an in-ear facing microphone which exploits the earable's occlusion effect to reliably detect the user's gait from inside the ear canal, without impairing the general usage of earphones. With data collected from 31 subjects, we show that EarGate achieves up to 97.26% Balanced Accuracy (BAC) with very low False Acceptance Rate (FAR) and False Rejection Rate (FRR) of 3.23% and 2.25%, respectively. Further, our measurement of power consumption and latency investigates how this gait identification model could live both as a stand-alone or cloud-coupled earable system.

Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an earbud and evaluate its performance on three representative applications, i.e., step counting, activity recognition, and hand-to-face gesture interaction. With data collected from 31 subjects, we show that OESense achieves 99.3% step counting recall, 98.3% recognition recall for 5 activities, and 97.0% recall for five tapping gestures on human face, respectively. We also demonstrate that OESense is compatible with earbuds' fundamental functionalities (e.g. music playback and phone calls). In terms of energy, OESense consumes 746 mW during data recording and recognition and it has a response latency of 40.85 ms for gesture recognition. Our analysis indicates such overhead is acceptable and OESense is potential to be integrated into future earbuds.

Earables (in-ear wearables) are a new frontier in wearables. Acting both as leisure devices, providing personal audio, as well as sensing platforms, earables could collect sensor data for the upper part of the body, subject to fewer vibrations and random movement variations than the lower parts of the body, due to inherent damping in the musculoskeletal system. These data may enable application domains such as augmented/virtual reality, medical rehabilitation, and health condition screening. Unfortunately, earables have inherent size, shape, and weight constraints limiting the type and position of the sensors on such platforms. For instance, lacking a magnetometer in all earables reference platforms, earables lack reference points. Thus, it becomes harder to work with absolute orientations. Embedding magnetometers in earables is challenging, as these rely heavily on radio (mostly Bluetooth) communication (RF) and contain magnets for magnetic-driven speakers and docking. We explore the feasibility of adding a built-in magnetometer in an earbud, presenting the first comprehensive study of the magnetic interference impacting the magnetometer when placed in an earable: both that caused by the speaker and by RF (music streaming and voice calls) are considered. We find that appropriate calibration of the magnetometer removes the offsets induced by the magnets, the speaker, and the variable interference due to BT. Further, we present an automatic, user-transparent adaptive calibration that obviates the need for alternative, expensive, and error-prone manual, or robotics, calibration procedures. Our evaluation shows how our calibration approach performs under different conditions, achieving convincing results with errors below 3° for the majority of the experiments.