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<button class="button is-small is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.04775">arXiv:2410.04775</a> <span> [<a href="https://arxiv.org/pdf/2410.04775">pdf</a>, <a href="https://arxiv.org/format/2410.04775">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Emerging Technologies">cs.ET</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> OmniBuds: A Sensory Earable Platform for Advanced Bio-Sensing and On-Device Machine Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Montanari%2C+A">Alessandro Montanari</a>, <a href="/search/cs?searchtype=author&query=Thangarajan%2C+A">Ashok Thangarajan</a>, <a href="/search/cs?searchtype=author&query=Al-Naimi%2C+K">Khaldoon Al-Naimi</a>, <a href="/search/cs?searchtype=author&query=Ferlini%2C+A">Andrea Ferlini</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Y">Yang Liu</a>, <a href="/search/cs?searchtype=author&query=Balaji%2C+A+N">Ananta Narayanan Balaji</a>, <a href="/search/cs?searchtype=author&query=Kawsar%2C+F">Fahim Kawsar</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.04775v1-abstract-short" style="display: inline;"> 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 s… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04775v1-abstract-full').style.display = 'inline'; document.getElementById('2410.04775v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.04775v1-abstract-full" style="display: none;"> 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. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.04775v1-abstract-full').style.display = 'none'; document.getElementById('2410.04775v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.14332">arXiv:2109.14332</a> <span> [<a href="https://arxiv.org/pdf/2109.14332">pdf</a>, <a href="https://arxiv.org/format/2109.14332">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3460418.3479326">10.1145/3460418.3479326 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> PilotEar: Enabling In-ear Inertial Navigation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ahuja%2C+A">Ashwin Ahuja</a>, <a href="/search/cs?searchtype=author&query=Ferlini%2C+A">Andrea Ferlini</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2109.14332v1-abstract-short" style="display: inline;"> Navigation systems are used daily. While different types of navigation systems exist, inertial navigation systems (INS) have favorable properties for some wearables which, for battery and form factors may not be able to use GPS. Earables (aka ear-worn wearables) are living a momentum both as leisure devices, and sensing and computing platforms. The inherent high signal to noise ratio (SNR) of ear-… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.14332v1-abstract-full').style.display = 'inline'; document.getElementById('2109.14332v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.14332v1-abstract-full" style="display: none;"> Navigation systems are used daily. While different types of navigation systems exist, inertial navigation systems (INS) have favorable properties for some wearables which, for battery and form factors may not be able to use GPS. Earables (aka ear-worn wearables) are living a momentum both as leisure devices, and sensing and computing platforms. The inherent high signal to noise ratio (SNR) of ear-collected inertial data, due to the vibration dumping of the musculoskeletal system; combined with the fact that people typically wear a pair of earables (one per ear) could offer significant accuracy when tracking head movements, leading to potential improvements for inertial navigation. Hence, in this work, we investigate and propose PilotEar, the first end-to-end earable-based inertial navigation system, achieving an average tracking drift of 0.15 m/s for one earable and 0.11 m/s for two earables. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.14332v1-abstract-full').style.display = 'none'; document.getElementById('2109.14332v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.12305">arXiv:2108.12305</a> <span> [<a href="https://arxiv.org/pdf/2108.12305">pdf</a>, <a href="https://arxiv.org/format/2108.12305">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1145/3447993.3483240">10.1145/3447993.3483240 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> EarGate: Gait-based User Identification with In-ear Microphones </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ferlini%2C+A">Andrea Ferlini</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+D">Dong Ma</a>, <a href="/search/cs?searchtype=author&query=Harle%2C+R">Robert Harle</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.12305v1-abstract-short" style="display: inline;"> 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… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.12305v1-abstract-full').style.display = 'inline'; document.getElementById('2108.12305v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.12305v1-abstract-full" style="display: none;"> 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. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.12305v1-abstract-full').style.display = 'none'; document.getElementById('2108.12305v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">MobiCom 2021</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2108.09393">arXiv:2108.09393</a> <span> [<a href="https://arxiv.org/pdf/2108.09393">pdf</a>, <a href="https://arxiv.org/format/2108.09393">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1109/PERCOM56429.2023.10099317">10.1109/PERCOM56429.2023.10099317 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> hEARt: Motion-resilient Heart Rate Monitoring with In-ear Microphones </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Butkow%2C+K">Kayla-Jade Butkow</a>, <a href="/search/cs?searchtype=author&query=Dang%2C+T">Ting Dang</a>, <a href="/search/cs?searchtype=author&query=Ferlini%2C+A">Andrea Ferlini</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+D">Dong Ma</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2108.09393v4-abstract-short" style="display: inline;"> 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… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09393v4-abstract-full').style.display = 'inline'; document.getElementById('2108.09393v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2108.09393v4-abstract-full" style="display: none;"> 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 \textit{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 $\pm$ 2.97~BPM, 8.12 $\pm$ 6.74~BPM, 11.23 $\pm$ 9.20~BPM and 9.39 $\pm$ 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. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2108.09393v4-abstract-full').style.display = 'none'; document.getElementById('2108.09393v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 January, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> 2023 IEEE International Conference on Pervasive Computing and Communications (PerCom) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2106.08607">arXiv:2106.08607</a> <span> [<a href="https://arxiv.org/pdf/2106.08607">pdf</a>, <a href="https://arxiv.org/format/2106.08607">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> OESense: Employing Occlusion Effect for In-ear Human Sensing </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ma%2C+D">Dong Ma</a>, <a href="/search/cs?searchtype=author&query=Ferlini%2C+A">Andrea Ferlini</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2106.08607v1-abstract-short" style="display: inline;"> 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 i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.08607v1-abstract-full').style.display = 'inline'; document.getElementById('2106.08607v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2106.08607v1-abstract-full" style="display: none;"> 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. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2106.08607v1-abstract-full').style.display = 'none'; document.getElementById('2106.08607v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 June, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Published at MobiSys 2021 </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" role="navigation" 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