arXiv subjects

Michael Beigl

Publications and source records attributed to Michael Beigl.

At least 19 recordsLinked to original sources

arXiv2026

Reassessing the Feasibility of PPG-Based Non-Invasive Blood Glucose Level Estimation

Non-invasive blood glucose level (BGL) estimation from photoplethysmography (PPG) holds great promise for wearable health monitoring, but results across studies are hard to compare due to inconsistent datasets, data leakage, and non-standardized evaluation metrics. We present the first reproducible, extensible evaluation pipeline and use it to reassess five representative PPG-based BGL methods on published datasets under three increasingly strict data-split protocols: random window-level, participant-aware, and leave-some-participants-out (LSPO). Models appeared competitive under random splitting but collapsed under participant-aware and LSPO evaluation, with nearly all yielding near-zero or negative R$^2$ values comparable to a mean-prediction baseline. Critically, across every model and split, over 90% of predictions fell within clinically acceptable zones (Clarke Error Grid A+B), including the baseline. This reveals a fundamental disconnect: clinical zone metrics systematically conceal model failure in this domain. Our findings demonstrate that random train-test splits substantially overestimate the generalization of PPG-based BGL models due to sample-level data leakage, and that robust ML evaluation must precede clinical validation to meaningfully assess real-world utility.

cs.HC↗

arXiv2026

Characterizing In-the-Wild Personal Listening Device Use to Inform Earable Application Design

Ear-worn devices are evolving from audio-playback tools into sensing platforms for health, interaction, and context-awareness. Yet, earable systems are typically designed and evaluated under strong assumptions about how long, how often, and in which situations people actually wear personal listening devices (PLDs). To ground these assumptions in-the-wild behavior, we combine a survey of 330 adults with multi-year, passively logged headphone audio-exposure records donated via Apple Health by 90 of them. We characterize where and when people use PLDs, how logged use has changed in recent years, and how psychological traits and social context associate with PLD usage. Our results show that logged mean daily use has increased from 37 minutes in 2020 to 64 minutes in 2024. Listening was intermittent: no listening was logged on 48% of participant-days in 2024, and sessions were fewer but longer on weekends. Sensation seeking, particularly disinhibition, showed small to medium positive associations with self-reported PLD use. Younger adults listened at higher volumes than the 25-34 group. High-volume exposure was uncommon, with only 4% of participant-weeks exceeding World Health Organization (WHO) safe-listening limits. Finally, most participants also reported avoiding PLD use in social situations. We translate these findings into implications for earable computing: realistic expectations of intermittent rather than continuous wear, contextual coverage that anticipates systematic gaps, targeted safe-listening interventions, and personalization grounded in psychosocial and demographic profiles rather than assumptions of uniform use.

cs.HC↗

arXiv2026

WHAR Arena: Benchmarking the State of the Art in Efficient Wearable Human Activity Recognition

Deep learning has become the dominant paradigm in Wearable Human Activity Recognition (WHAR), yet progress is obscured by a comparability crisis. Results are often reported using inconsistent datasets, custom data processing, and varying evaluation protocols, making state-of-the-art claims fragile. We address this with a large-scale, open-source benchmark that integrates 30 diverse datasets under standardized processing, unified model interfaces, and a shared cross-subject evaluation protocol. Evaluating 17 representative architectures across 4760 training runs, we jointly measure predictive performance alongside on-device latency, peak memory, and model size on an Android reference device. Our results reveal that the WHAR state of the art is distributed rather than dominated by a single architecture. While CNN-HAR achieves the highest mean macro-F1, top-performing models cluster tightly, indicating contemporary architectures have converged near a predictive performance ceiling. When accounting for deployment efficiency, compact neural models, such as TinierHAR, and classical Random Forests define the practically relevant Pareto frontier, whereas larger recurrent and hybrid models incur high hardware costs without corresponding performance gains. Consequently, while predictive performance has plateaued, substantial potential for future progress remains in optimizing deployment efficiency and improving adaptation to domain shifts. We release our full framework to support transparent reuse and extension.

cs.LG↗

arXiv2026

Uncertainty-Aware (Un)Supervised Few-Shot User Adaptation for On-Device Personalized Human Activity Recognition

Sensor-based Human Activity Recognition (HAR) models often degrade on unseen users due to domain shifts caused by individual movement patterns and sensor placement. Practical wearable HAR systems therefore require personalization methods that are lightweight, applicable whether calibration data is labeled, unlabeled, or unavailable, and robust under limited calibration. We present a gradient-free framework that repurposes pretrained HAR classifiers as Prototypical Networks using using prior prototypes, which preserve zero-shot performance and regularize adaptation. For labeled calibration, we introduce closed-form Bayesian prototype estimation and extend the same principle to unlabeled calibration. With only 3 seconds of calibration data per activity (one shot), supervised adaptation improves macro-F1 by +2.76 to +33.44 percentage points across four datasets, while unsupervised adaptation improves by +0.56 to +32.13 points. Since adaptation requires only closed-form prototype updates, the framework enables efficient and robust on-device personalization of preexisting HAR classifiers.

cs.LG↗

arXiv2026

pcbGPT: Automatic PCB Schematic Synthesis from Natural Language Requirements

Translating natural-language hardware requirements into correct printed circuit board (PCB) schematics remains difficult in embedded, IoT, and wearable development. Designers must choose compatible components, interpret datasheets, add support circuitry, and expose correct interfaces before layout and prototyping can begin, while many such circuits cannot be validated through straightforward simulation. We present pcbGPT, a grounded system for generating editable KiCad schematics from natural-language specifications. pcbGPT represents circuits in a Python DSL and combines tool-augmented synthesis with component-library search, datasheet-grounded design knowledge, execution-based checking, structural and semantic validation, and an interactive web workflow that supports iterative refinement and synchronization with KiCad projects. We evaluate the system on 20 embedded schematic-generation tasks with reference implementations, required components, and interface constraints that enable automatic comparison. The best model reaches overall pass@1 of 0.90 and pass@5 of 1.00; pass@1 is 1.00 on basic and easy tasks, 0.91 on medium tasks, and 0.72 on hard tasks. These results, together with failure analysis, show that pcbGPT can already generate useful, reviewable first-draft schematics for early prototyping, but is not yet reliable enough to replace expert review.

cs.HC↗

arXiv2026

EarResp-ANS : Audio-Based On-Device Respiration Rate Estimation on Earphones with Adaptive Noise Suppression

Respiratory rate (RR) is a key vital sign for clinical assessment and mental well-being, yet it is rarely monitored in everyday life due to the lack of unobtrusive sensing technologies. In-ear audio sensing is promising due to its high social acceptance and the amplification of physiological sounds caused by the occlusion effect; however, existing approaches often fail under real-world noise or rely on computationally expensive models. We present EarResp-ANS, the first system enabling fully on-device, real-time RR estimation on commercial earphones. The system employs LMS-based adaptive noise suppression (ANS) to attenuate ambient noise while preserving respiration-related acoustic components, without requiring neural networks or audio streaming, thereby explicitly addressing the energy and privacy constraints of wearable devices. We evaluate EarResp-ANS in a study with 18 participants under realistic acoustic conditions, including music, cafeteria noise, and white noise up to 80 dB SPL. EarResp-ANS achieves robust performance with a global MAE of 0.84 CPM , reduced to 0.47 CPM via automatic outlier rejection, while operating with less than 2% processor load directly on the earphone.

cs.SD↗

arXiv2026

CHOMP: Multimodal Chewing Side Detection with Earphones

Chewing side preference (CSP) has been identified both as a risk factor for temporomandibular disorders (TMD) and behavioral manifestation. Despite TMDs affecting roughly one third of the global population, assessment mainly relies on clinical examinations and self-reports, offering limited insight into everyday jaw function. Continuous CSP monitoring could provide an objective proxy for functional asymmetries. Prior wearable approaches, however, mostly use specialized form factors and demonstrate limited performance. We therefore present CHOMP, the first system for chewing side detection using earphones. Employing OpenEarable 2.0, we collected data from 20 participants with microphones, a bone-conduction microphone, IMU, PPG, and a pressure sensor across eleven foods, five non-chewing activities, and three noise conditions. We apply the Continuous Wavelet Transform to each sensing modality and use the resulting multi-channel scalograms as inputs to CNN-based classifiers. Microphones achieve the strongest single-sensor unit performance, with median F1 scores of 94.5% in leave-one-food-out (LOFO) and 92.6% in leave-one-subject-out (LOSO) cross-validations. Fusing sensing modalities further improves performance to 97.7% for LOFO and 95.4% for LOSO, with additional evaluations under noise interference indicating robust performance. Our results establish earphones as a practical platform for continuous CSP monitoring, enabling clinicians and patients to assess jaw function in everyday life.

cs.HC↗

arXiv2025

Feasibility of In-Ear Single-Channel ExG for Wearable Sleep Monitoring in Real-World Settings

Automatic sleep staging typically relies on gold-standard EEG setups, which are accurate but obtrusive and impractical for everyday use outside sleep laboratories. This limits applicability in real-world settings, such as home environments, where continuous, long-term monitoring is needed. Detecting sleep onset is particularly relevant, enabling consumer applications (e.g. automatically pausing media playback when the user falls asleep). Recent research has shown correlations between in-ear EEG and full-scalp EEG for various phenomena, suggesting wearable, in-ear devices could allow unobtrusive sleep monitoring. We investigated the feasibility of using single-channel in-ear electrophysiological (ExG) signals for automatic sleep staging in a wearable device by conducting a sleep study with 11 participants (mean age: 24), using a custom earpiece with a dry eartip electrode (D\"atwyler SoftPulse) as a measurement electrode in one ear and a reference in the other. Ground truth sleep stages were obtained from an Apple Watch Ultra, validated for sleep staging. Our system achieved 90.5% accuracy for binary sleep detection (Awake vs. Asleep) and 65.1% accuracy for four-class staging (Awake, REM, Core, Deep) using leave-one-subject-out validation. These findings demonstrate the potential of in-ear electrodes as a low-effort, comfortable approach to sleep monitoring, with applications such as stopping podcasts when users fall asleep.

cs.LG↗

arXiv2025

WHAR Datasets: An Open Source Library for Wearable Human Activity Recognition

The lack of standardization across Wearable Human Activity Recognition (WHAR) datasets limits reproducibility, comparability, and research efficiency. We introduce WHAR datasets, an open-source library designed to simplify WHAR data handling through a standardized data format and a configuration-driven design, enabling reproducible and computationally efficient workflows with minimal manual intervention. The library currently supports 9 widely-used datasets, integrates with PyTorch and TensorFlow, and is easily extensible to new datasets. To demonstrate its utility, we trained two state-of-the-art models, TinyHar and MLP-HAR, on the included datasets, approximately reproducing published results and validating the library's effectiveness for experimentation and benchmarking. Additionally, we evaluated preprocessing performance and observed speedups of up to 3.8x using multiprocessing. We hope this library contributes to more efficient, reproducible, and comparable WHAR research.

cs.HC↗

arXiv2025

EarXplore: An Open Research Database on Earable Interaction

Interaction with sensor-augmented earphones, referred to as earables or hearables, represents a major area of earable research. Proximate to the head and reachable by hand, earables support diverse interactions and can detect multiple inputs simultaneously. Yet this diversity has fragmented research, complicating the tracking of developments. To address this, we introduce EarXplore, a curated, interactive online database on earable interaction research. Designed through a question-centered approach that guided the development of 33 criteria applied to annotate 118 studies and the structure of the platform, EarXplore comprises four integrated views: a Tabular View for structured exploration, a Graphical View for visual overviews, a Similarity View for conceptual links, and a Timeline View for scholarly trends. We demonstrate how the platform supports tailored exploration and filtering, and we leverage its capabilities to discuss gaps and opportunities. With community update mechanisms, EarXplore evolves with the field, serving as a living resource to accelerate future research

cs.HC↗

arXiv2025

Closed-Loop Rhythmic Haptic Biofeedback via Smartwatch for Relaxation and Sleep Onset

We investigate the use of musically structured, closed-loop vibration patterns as a passive biofeedback intervention for relaxation and sleep initiation. By encoding rhythmic meter structures into smartwatch vibrations and adapting their frequency to be slightly slower than the user's real-time heart rate, our system aims to reduce arousal through tactile entrainment, offering a non-invasive alternative to auditory or open-loop approaches previously used in sleep and anxiety contexts. In the first study (N=20), we compared five adaptive vibration rhythms for their effects on heart rate and subjective perceptions of relaxation in a resting context. In the second study (N=28), we evaluated the most promising pattern from Study 1 in a prolonged sleep initiation setting. Results showed increased parasympathetic activity and perceived relaxation during short-term stimulation, but no significant effects on sleep-related measures during the sleep onset phase. This work contributes to the understanding of how wearable haptic feedback can support relaxation and sleep, offering design insights and identifying methodological considerations for effectively integrating haptic interaction into self-directed interventions.

cs.HC↗

arXiv2025

Haptic Biofeedback for Wakeful Rest: Does Stimulation Location Make a Difference?

Wearable haptic interventions offer promising support for relaxation through slow, vibrotactile biofeedback. Despite their potential, current applications focus on stress-inducing procedures and fixed vibration patterns, with limited consideration of body location and dynamic biofeedback during restful states. This study investigates the effects of haptic biofeedback adjusted from real-time heart rate during eyes-closed wakeful rest, comparing four wearable body placements: the wrist, hand, forearm, and shoulder. Heart rate, alpha wave activity on the ear, subjective restfulness, and vibration experience were measured across these conditions. Results show that biofeedback reduced heart rate at the wrist, shoulder, and forearm, while alpha power measured at the ear remained unchanged. Subjective restfulness was rated highest at the shoulder and forearm, which were also the most preferred locations. In addition, participants reported greater comfort, relaxation, and further increased sleepiness at the forearm compared to the wrist, which was more easily recognizable. These findings suggest that the forearm and shoulder are ideal for unobtrusive relaxation feedback for wakeful rest, while the wrist may require design improvements for subjective experience.

cs.HC↗

arXiv2025

earEOG via Periauricular Electrodes to Facilitate Eye Tracking in a Natural Headphone Form Factor

Eye tracking technology is frequently utilized to diagnose eye and neurological disorders, assess sleep and fatigue, study human visual perception, and enable novel gaze-based interaction methods. However, traditional eye tracking methodologies are constrained by bespoke hardware that is often cumbersome to wear, complex to apply, and demands substantial computational resources. To overcome these limitations, we investigated Electrooculography (EOG) eye tracking using 14 electrodes positioned around the ears, integrated into a custom-built headphone form factor device. In a controlled experiment, 16 participants tracked stimuli designed to induce smooth pursuits and saccades. Data analysis identified optimal electrode pairs for vertical and horizontal eye movement tracking, benchmarked against gold-standard EOG and camera-based methods. The electrode montage nearest the eyes yielded the best horizontal results. Horizontal smooth pursuits via earEOG showed high correlation with gold-standard measures ($r_{\mathrm{EOG}} = 0.81, p = 0.01$; $r_{\mathrm{CAM}} = 0.56, p = 0.02$), while vertical pursuits were weakly correlated ($r_{\mathrm{EOG}} = 0.28, p = 0.04$; $r_{\mathrm{CAM}} = 0.35, p = 0.05$). Voltage deflections when performing saccades showed strong correlation in the horizontal direction ($r_{\mathrm{left}} = 0.99, p = 0.0$; $r_{\mathrm{right}} = 0.99, p = 0.0$) but low correlation in the vertical direction ($r_{\mathrm{up}} = 0.6, p = 0.23$; $r_{\mathrm{down}} = 0.19, p = 0.73$). Overall, horizontal earEOG demonstrated strong performance, indicating its potential effectiveness, while vertical earEOG results were poor, suggesting limited feasibility in our current setup.

cs.HC↗

arXiv2025

Heatables: Effects of Infrared-LED-Induced Ear Heating on Thermal Perception, Comfort, and Cognitive Performance

Maintaining thermal comfort in shared indoor environments remains challenging, as centralized HVAC systems are slow to adapt and standardized to group norms. Cold exposure not only reduces subjective comfort but can impair cognitive performance, particularly under moderate to severe cold stress. Personal Comfort Systems (PCS) have shown promise by providing localized heating, yet many designs target distal body parts with low thermosensitivity and often lack portability. In this work, we investigate whether targeted thermal stimulation using in-ear worn devices can manipulate thermal perception and enhance thermal comfort. We present Heatables, a novel in-ear wearable that emits Near-Infrared (NIR) and Infrared (IR) radiation via integrated LEDs to deliver localized optical heating. This approach leverages NIR-IR's ability to penetrate deeper tissues, offering advantages over traditional resistive heating limited to surface warming. In a placebo-controlled study with 24 participants, each exposed for 150 minutes in a cool office environment (approximately 17.5 degrees Celsius) to simulate sustained cold stress during typical sedentary office activities, Heatables significantly increased the perceived ambient temperature by around 1.5 degrees Celsius and delayed cold discomfort. Importantly, thermal benefits extended beyond the ear region, improving both whole-body comfort and thermal acceptability. These findings position in-ear NIR-IR-LED-based stimulation as a promising modality for unobtrusive thermal comfort enhancement in everyday contexts.

cs.HC↗

arXiv2025

UltrasonicSpheres: Localized, Multi-Channel Sound Spheres Using Off-the-Shelf Speakers and Earables

We present a demo of UltrasonicSpheres, a novel system for location-specific audio delivery using wearable earphones that decode ultrasonic signals into audible sound. Unlike conventional beamforming setups, UltrasonicSpheres relies on single ultrasonic speakers to broadcast localized audio with multiple channels, each encoded on a distinct ultrasonic carrier frequency. Users wearing our acoustically transparent earphones can demodulate their selected stream, such as exhibit narrations in a chosen language, while remaining fully aware of ambient environmental sounds. The experience preserves spatial audio perception, giving the impression that the sound originates directly from the physical location of the source. This enables personalized, localized audio without requiring pairing, tracking, or additional infrastructure. Importantly, visitors not equipped with the earphones are unaffected, as the ultrasonic signals are inaudible to the human ear. Our demo invites participants to explore multiple co-located audio zones and experience how UltrasonicSpheres supports unobtrusive delivery of personalized sound in public spaces.

cs.SD↗

arXiv2025

HARNode: A Time-Synchronised, Open-Source, Multi-Device, Wearable System for Ad Hoc Field Studies

Human activity recognition (HAR) research often lacks accessible, comprehensive field data. Commercial systems are rarely open source, hard to expand, and limited by issues like node synchronisation, data throughput, unclear sensor placement, complexity, and high cost. As a result, researchers typically use only a few intuitively placed sensors and conduct limited field trials. HARNode overcomes these challenges with a fully open-source hardware and software platform. Each node includes an ESP32-S3 module (AtomS3), a 9-axis IMU (Bosch BMX160), pressure and temperature sensors (Bosch BMP388), a display, and an I2C port. Data is streamed via Wi-Fi, with NTP-based time synchronisation achieving roughly 1 ms accuracy. The system runs for up to 8 hours and is built using off-the-shelf parts, a simple online PCB service, and a compact 3D-printed housing with Velcro straps, enabling flexible and scalable body placement while requiring little hardware knowledge. In a study with ten subjects wearing eleven HARNodes each, setup took under five minutes per person. A random forest classifier distinguished walking from stair-climbing transitions, showing the benefits of sensor-overprovisioning: Seven nodes achieved approx. 98% accuracy, matching the performance of all eleven. These findings confirm HARNode's value as a fast-deploying, scalable tool for field-based HAR research and optimised sensor placement.

cs.NI↗

arXiv2024

EarCapAuth: Biometric Method for Earables Using Capacitive Sensing Eartips

Earphones can give access to sensitive information via voice assistants which demands security methods that prevent unauthorized use. Therefore, we developed EarCapAuth, an authentication mechanism using 48 capacitive electrodes embedded into the soft silicone eartips of two earables. For evaluation, we gathered capactive ear canal measurements from 20 participants in 20 wearing sessions (12 at rest, 8 while walking). A per user classifier trained for authentication achieves an EER of 7.62% and can be tuned to a FAR (False Acceptance Rate) of 1% at FRR (False Rejection Rate) of 16.14%. For identification, EarCapAuth achieves 89.95%. This outperforms some earable biometric principles from related work. Performance under motion slightly decreased to 9.76% EER for authentication and 86.40% accuracy for identification. Enrollment can be performed rapidly with multiple short earpiece insertions and a biometric decision is made every 0.33s. In the future, EarCapAuth could be integrated into high-resolution brain sensing electrode tips.

cs.CR↗

arXiv2024

OpenEarable ExG: Open-Source Hardware for Ear-Based Biopotential Sensing Applications

While traditional earphones primarily offer private audio spaces, so-called "earables" emerged to offer a variety of sensing capabilities. Pioneering platforms like OpenEarable have introduced novel sensing platforms targeted at the ears, incorporating various sensors. The proximity of the ears to the eyes, brain, and facial muscles has also sparked investigation into sensing biopotentials. However, currently there is no platform available that is targeted at the ears to sense biopotentials. To address this gap, we introduce OpenEarable ExG - an open-source hardware platform designed to measure biopotentials in and around the ears. OpenEarable ExG can be freely configured and has up to 7 sensing channels. We initially validate OpenEarable ExG in a study with a left-right in-ear dual-electrode montage setup with 3 participants. Our results demonstrate the successful detection of smooth pursuit eye movements via Electrooculography (EOG), alpha brain activity via Electroencephalography (EEG), and jaw clenching via Electromyography (EMG). OpenEarable ExG is part of the OpenEarable initiative and is fully open-source under MIT license.

cs.AR↗