Searcharxiv⌕ Search

arXiv subjects

Khaldoon Al-Naimi

Publications and source records attributed to Khaldoon Al-Naimi.

2 recordsLinked to original sources

Listening to the Mind: Earable Acoustic Sensing of Cognitive Load

Earable acoustic sensing offers a powerful and non-invasive modality for capturing fine-grained auditory and physiological signals directly from the ear canal, enabling continuous and context-aware monitoring of cognitive states. As earable devices become increasingly embedded in daily life, they provide a unique opportunity to sense mental effort and perceptual load in real time through auditory interactions. In this study, we present the first investigation of cognitive load inference through auditory perception using acoustic signals captured by off-the-shelf in-ear devices. We designed speech-based listening tasks to induce varying levels of cognitive load, while concurrently embedding acoustic stimuli to evoke Stimulus Frequency Otoacoustic Emission (SFOAEs) as a proxy for cochlear responsiveness. Statistical analysis revealed a significant association (p < 0.01) between increased cognitive load and changes in auditory sensitivity, with 63.2% of participants showing peak sensitivity at 3 kHz. Notably, sensitivity patterns also varied across demographic subgroups, suggesting opportunities for personalized sensing. Our findings demonstrate that earable acoustic sensing can support scalable, real-time cognitive load monitoring in natural settings, laying a foundation for future applications in augmented cognition, where everyday auditory technologies adapt to and support the users mental health.

cs.HC↗

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

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.

cs.ET↗